modelling a real-time multi-sensor fusion-based navigation
TRANSCRIPT
i
Modelling a Real-Time Multi-Sensor Fusion-Based Navigation
System for Indoor Human-Following Companion Robots
Mark Tee Kit Tsun
A thesis submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy
Performed at
Swinburne University of Technology 2018
ii
ABSTRACT
Assistive robotics today have been involved to a wide range of applications which include
studies that aid children with cognitive disabilities, disabled patients and elderly care.
However, it is difficult to acquire any commercially available robotic solution that
universally caters to Assistive Technology needs of domestic households. People
burdened with the care of their disabled family members have limited access to
Commercial Off-the-Shelf (COTS) or easily developed companion robots that assist in
carrying out their responsibilities while allowing caregivers to perform Activities of Daily
Living (ADL) in the meantime. This research investigated this problem and attempted to
formulate a solution by proposing a companion robot planning template that emphasizes
Object-Oriented mapping of functionalities to standalone solution modules built using
COTS components. However, the findings indicated that there is an absence of readily
available homogenous human-following capability for companion robots. This problem is
further refined into two navigational challenges: the need for an effective autonomous
indoor navigation and a reliable human tracking method. The research proceeded to
explore existing works that help influence the formation of a possible solution to these
challenges. The resultant solution is a robot navigation model based on an adapted
Potential Field Method and multi-sensor fusion (motion capture, raw depth and proximity
array), providing range-free pathfinding decisions that consider the primary target’s
relative position, immediate surroundings and mid-to-long-range depth profiles of the
environment. This model was implemented as a robot control system using Microsoft
Robotics Developer Studio (MRDS) and tested via the Visual Simulation Environment
(VSE). A total of 7 functional tests, local minima recreation, and 3 performance
benchmark scenarios were created to carry out observations on the effectiveness of this
system. The results show that the system satisfactorily passed all functional tests and
exceeded the performance of projected benchmark studies by 28.85%. This indicates that
the solution model presented in this research has clear potential in contributing towards
making indoor companion robots a common item in future households.
iii
PUBLICATIONS PRODUCED THROUGHOUT THE RESEARCH
Tee, MKT, Lau, BT & Siswoyo, HJ 2018b, ‘An Improved Indoor Robot Human-Following
Navigation Model Using Depth Camera, Active IR Marker and Proximity Sensors
Fusion’, Robotics, vol. 7, no. 1, Multidisciplinary Digital Publishing Institute, p. 4,
viewed 24 February, 2018, <http://www.mdpi.com/2218-6581/7/1/4>.
Tee, MKT, Lau, BT & Siswoyo, HJ 2018c, ‘Exploring the Performance of a Sensor-Fusion-
based Navigation System for Human Following Companion Robots’, International
Journal of Mechanical Engineering and Robotics Research (IJMERR).
Tee, MKT, Lau, BT & Siswoyo, HJ 2014, ‘Exploring the Possibility of Companion Robots
for Injury Prevention for People with Disabilities’, The 19th International Conference
on Transformative Science & Engineering, Business & Social Innovation (SDPS
2014), Kuching, Sarawak, Malaysia, pp. 199 – 210.
Tee, MKT, Lau, BT & Siswoyo, HJ 2017, ‘Pathfinding decision-making using proximity
sensors, depth camera and active IR marker tracking data fusion for human following
companion robot’, ACM International Conference Proceeding Series.
Tee, MKT, Lau, BT, Siswoyo, HJ & Lau, SL 2015, ‘A Human Orientation Tracking System
using Template Matching and Active Infrared Marker’, 2015 International Conference
on Smart Sensors and Application (ICSSA 2015), Kuala Lumpur, Malaysia.
Tee, MKT, Lau, BT, Siswoyo, HJ & Lau, SL 2016, ‘Potential of Human Tracking in
Assistive Technologies for Children with Cognitive Disabilities’, Supporting the
Education of Children with Autism Spectrum Disorders, IGI Global, pp. 245–247,
viewed
<https://books.google.com/books?hl=en&lr=&id=hxwRDQAAQBAJ&oi=fnd&pg=PA
245&dq=potential+of+human+tracking+in+assistive+technologies+for+children+wit
iv
h+cognitive+disabilities&ots=Tns3PcOKbt&sig=Ndby64oOnd7mKypAVSskjXiurhs>.
Tee, MKT, Lau, BT, Siswoyo, HJ & Then, PHH 2015, ‘Robotics for Assisting Children with
Physical and Cognitive Disabilities’, in LB Theng (ed.), Assistive Technologies for
Physical and Cognitive Disabilities, IGI Global, pp. 78–120, viewed 20 February,
2015, <http://www.igi-global.com/chapter/robotics-for-assisting-children-with-
physical-and-cognitive-disabilities/122905>.
Tee, MKT, Lau, BT, Siswoyo, HJ & Wong, DML 2016, ‘Integrating Visual Gestures for
Activity Tracking in the Injury Mitigation Strategy using CARMI’, RESKO Technical
Conference 2016: The 2nd Asian Meeting on Rehabilitation Engineering and
Assistive Technology (AMoRE AT), Rehabilitation Engineering & Assistive
Technology Society of Korea (RESKO), Goyang, Korea, pp. 61–62.
Tee, MKT, Lau, BT, Siswoyo Jo, H & Lau, SL 2016, ‘Proposing a Sensor Fusion
Technique Utilizing Depth and Ranging Sensors for Combined Human Following
and Indoor Robot Navigation’, Proceedings of the Fifth International Conference on
Network, Communication and Computing (ICNCC 2016), ACM Press, New York,
USA, pp. 331–335, viewed 20 June, 2017,
<http://dl.acm.org/citation.cfm?doid=3033288.3033345>.
v
ACKNOWLEDGEMENT
The author owes his deepest gratitude to Associate Professors Dr. Lau Bee Theng and
Dr. Lau Sian Lun as well as Dr. Hudyjaya Siswoyo Jo for their vigilant guidance and
support, without whom this research would not have been possible.
Special thanks go to Dr. Riady Siswoyo Jo for his unparalleled advice and suggestions
during inception of the solution model.
The author is indebted to the Research & Consultancy Office, Faculty of Engineering,
Computing & Science, his fellow postgraduate candidate colleagues, lab technicians and
staff at Swinburne University of Technology Sarawak (SUTS) for their countless
assistance and moral support.
Finally, the author wishes to thank his friends and family for their company throughout
this journey.
This work is dedicated to the members of the Robotics & Automation Club and IEEE
Student Chapter at SUTS in hopes that they never forget to reach out towards the sky.
vi
DECLARATION
This thesis contains no material which has been accepted for the award of any other
degree or diploma in any university, and to the best of my knowledge contains no material
previously published or written by another person, except where due reference is made
in the text of the thesis. Work based on joint research or publications in this thesis fully
acknowledges the relative contributions of the respective authors or workers.
Signature : ____________________
Name : Mark Tee Kit Tsun
Date : 31st May 2018
vii
TABLE OF CONTENTS
Abstract ............................................................................................................................ii
Publications Produced Throughout The Research .......................................................... iii
Acknowledgement ........................................................................................................... v
Declaration ......................................................................................................................vi
Table of Contents ........................................................................................................... vii
List of Figures ..................................................................................................................xi
List of Tables ..................................................................................................................xv
Nomenclature ............................................................................................................... xvii
Chapter 1: Introduction .............................................................................................. 1
1.1 Research Background ........................................................................................ 1
1.2 Research Problems ............................................................................................ 5
1.3 Research Aim and Objectives ............................................................................ 6
1.4 Research Scope ................................................................................................. 7
1.5 Thesis Organization ........................................................................................... 9
Chapter 2: Assistive Companion Robotics and their Challenges ............................. 11
2.1 Assistive Robotics in Preventing Injuries .......................................................... 11
2.2 Assistive Robotics in Physical Rehabilitation for Cerebral Palsy ...................... 15
2.3 Assistive Robotics in Social Interaction Rehabilitation ..................................... 17
2.3.1 Affinity to Robotic Interaction Companions ................................................ 18
2.3.2 Robot-Mediated Social Communication Treatment ................................... 20
2.3.3 Robot-Mediated Active Play ...................................................................... 25
2.3.4 Robotics in Preventive Intervention ........................................................... 28
2.4 Life-Long Assistive Robotics for Cognitively Disabled Children ....................... 30
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2.5 Identifying Companion Robots and Their Challenges ...................................... 33
2.6 Proposed Indoor Companion Robot Planning Template .................................. 36
2.7 Case Study: CARMI ......................................................................................... 39
2.8 Chapter Summary ............................................................................................ 43
Chapter 3: Indoor Robot Navigation and Human Following ..................................... 46
3.1 Challenge 1: Indoor Navigation ........................................................................ 46
3.1.1 Location-Specific Issues ............................................................................ 47
3.1.2 Autonomous Wayfinding Dilemmas ........................................................... 48
3.1.3 Sensory and Actuation Hardware Dilemmas ............................................. 51
3.1.4 Overview of Possible Autonomous Navigation Solutions .......................... 54
3.2 Challenge 2: Human Tracking .......................................................................... 58
3.2.2 Self-Localization ........................................................................................ 59
3.2.3 Body Tracking ............................................................................................ 65
3.2.4 Biomonitoring ............................................................................................. 69
3.2.5 Examples of Human Tracking Technologies Fusion in Existing Research 71
3.3 Proposed Combined Human Tracking and Indoor Navigation Solution ........... 73
3.4 Chapter Summary ............................................................................................ 75
Chapter 4: Design and Prototyping of the Multi-Sensor Fusion-Based Navigation
Model 77
Introduction ............................................................................................................ 77
4.1 .............................................................................................................................. 77
4.2 Human Orientation Tracking using an Active InfraRed (IR) Marker ................. 81
4.3 Sensor-Fusion Based Robot Navigation Model................................................ 86
4.3.1 Identification and Locking of Primary Subject ............................................ 87
4.3.2 Pathfinding and Obstacle Avoidance ......................................................... 94
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4.4 Example Model Application ............................................................................ 113
4.5 Robot Control Prototype Implementation ....................................................... 117
4.5.1 Use Case Model ...................................................................................... 118
4.5.2 Activity Model .......................................................................................... 123
4.5.3 Software Structure Model ........................................................................ 126
4.5.4 Development of the RobotBase Prototype Platform ................................ 128
4.5.5 Development of the Human Activity Tracking System ............................. 131
4.5.6 Navigation System Implementation using MRDS .................................... 134
4.5.7 CARMI Navigation System State Machine .............................................. 136
4.6 Chapter Summary .......................................................................................... 140
Chapter 5: Testing and Benchmarking Results ...................................................... 142
5.1 Introduction .................................................................................................... 142
5.2 Functional Testing Plan .................................................................................. 143
5.3 Functional Testing Simulation Results ........................................................... 147
5.3.1 Scenario: Single Uniform Obstruction ...................................................... 148
5.3.2 Scenario: Uniform Obstruction with Scattered Obstacles on the Left ...... 151
5.3.3 Scenario: Uniform Obstruction with Scattered Obstacles on the Right .... 153
5.3.4 Scenario: Single Non-Uniform Obstruction .............................................. 156
5.3.5 Scenario: Non-Uniform Obstruction with Scattered Obstacles on the Left
159
5.3.6 Scenario: Non-Uniform Obstruction with Scattered Obstacles on the Right
162
5.3.7 Functional Testing Simulation Findings and Discussion .......................... 167
5.4 Existing Indoor Robot Navigation Studies and Benchmark Scenarios Selection
171
5.4.1 Benchmark Study 1 ................................................................................. 171
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5.4.2 Benchmark Study 2 ................................................................................. 173
5.4.3 Benchmark Study 3 ................................................................................. 174
5.5 Performance Benchmark Scenario Design and Results ................................ 175
5.5.1 Benchmark 1 Simulation Results ............................................................. 177
5.5.2 Benchmark 2 Simulation Results ............................................................. 179
5.5.3 Benchmark 3 Simulation Results ............................................................. 182
5.6 Chapter Summary .......................................................................................... 186
Chapter 6: Conclusion ........................................................................................... 187
6.1 Introduction .................................................................................................... 187
6.2 Contributions .................................................................................................. 187
6.2.1 Identification of Navigational Challenges for Indoor Companion Robots . 187
6.2.2 Design of a Novel Indoor Robot Navigation Model to Perform Real-time
Human-following and Obstacle Avoidance ........................................................... 190
6.2.3 Evaluation of the Effectiveness of the Proposed Navigation Model in Indoor
Human-following and Obstacle Avoidance ........................................................... 196
6.3 Limitations and Future Work .......................................................................... 198
6.4 Research Summary ....................................................................................... 201
References .................................................................................................................. 204
Appendices ................................................................................................................. 219
Appendix A – Use Case Modelling .......................................................................... 219
Appendix B – Activity Model Flow Charts ................................................................ 224
Appendix C - Custom Structural Schematics ........................................................... 228
Appendix D – Wiring Diagram ................................................................................. 236
Appendix E – Functional Testing Scenarios Simulation Results .............................. 237
Appendix F – Benchmark Scenarios Simulation Results ......................................... 311
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LIST OF FIGURES
Figure 2-1: G-EO System by Reha Technology AG (Reha Technology AG 2012). ...... 17
Figure 2-2: The HapticMaster end effector device (Delft Haptics Lab 2018). ................ 17
Figure 2-3: Triadic Interactions model (Colton, Ricks & Goodrich 2009). ...................... 19
Figure 2-4: KASPAR (Wood et al. 2013). ...................................................................... 19
Figure 2-5: Tito (Michaud et al. 2006). .......................................................................... 23
Figure 2-6: NAO H25 features diagram (Aldebaran Robotics 2014). ............................ 23
Figure 2-7: Scenario 3 transpiring between both patients and the facilitating robot (Costa
et al. 2010). ................................................................................................................... 25
Figure 2-8: Neuronics Katana 6M180 (Trevor, Howard & Kemp 2009). ........................ 26
Figure 2-9: The IROMEC (Ferrari, Robins & Dautenhahn 2009). .................................. 28
Figure 2-10: Roball (Trevor, Howard & Kemp 2009). .................................................... 28
Figure 2-11: The MATS robot (Balaguer & Gimenez 2006). ......................................... 35
Figure 2-12: Companion robot Planning Template intended for use with cognitively
disabled children. .......................................................................................................... 38
Figure 2-13: An early prototype of the CARMI robot as the planning template's proof of
concept. ......................................................................................................................... 40
Figure 2-14: The Robot-Based Injury Prevention Strategy. ........................................... 41
Figure 4-1: Microsoft Kinect documentation of hardware limitation. (Microsoft Corporation
2013) ............................................................................................................................. 81
Figure 4-2: First prototype of the active IR marker. The vest is equipped with hook & loop
strips that allow the IR modules to be mounted in a variety of patterns. An example of a
pattern as perceived by the camera is shown. (Tee, Lau, Siswoyo Jo & Lau 2015) ..... 82
Figure 4-3: IR Active Marker preconditioning process. (Tee, Lau, Siswoyo Jo & Lau 2015)
...................................................................................................................................... 83
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Figure 4-4: Example of an orientation pattern data set. (Tee, Lau, Siswoyo Jo & Lau 2015)
...................................................................................................................................... 83
Figure 4-5: Calibration rig for the active IR marker and camera. (Tee, Lau, Siswoyo Jo &
Lau 2015) ...................................................................................................................... 83
Figure 4-6: Illustration of the hardware detection zone performance, relative to orientation
tracking. (Tee, Lau, Siswoyo Jo & Lau 2015) ................................................................ 86
Figure 4-7: The depth sensor's camera space illustration. ............................................ 88
Figure 4-8: Illustration of the Active Marker IR Camera's view space. .......................... 89
Figure 4-9: Wandering Standpoint Algorithm. ............................................................... 95
Figure 4-10: Detection zones for an array of ultrasonic sensors on a robot. ................. 96
Figure 4-11: Simple visualization of the ranging sensor array, S. ................................. 97
Figure 4-12: Template of a depth map. ......................................................................... 99
Figure 4-13:Example of a depth image frame. .............................................................. 99
Figure 4-14: Illustration of the transformation problem from the Vertical to Horizontal
Plane. .......................................................................................................................... 101
Figure 4-15: Illustration of the Potential Field Method. (a) An overhead depiction of a
potential field. (b) The same field reimagined as a contoured slope. (Bräunl 2006) .... 102
Figure 4-16: Only the obstructions and target within field of view is considered when
deciding which direction to take. (Tee, Lau, Siswoyo Jo & Lau 2016) ......................... 104
Figure 4-17: Illustration of the transformed depth map into horizontal form. ............... 105
Figure 4-18: Revisited depth sensor camera view with top and bottom trims. ............. 107
Figure 4-19: Example of populating the Target Location array. ................................... 108
Figure 4-20: Example of the Bias and Sensor arrays alignment process. ................... 111
Figure 4-21: Example Model Application 1 – No Obstruction. ..................................... 114
Figure 4-22: Example Model Application 2 – Single Primary Obstruction. .................. 115
Figure 4-23: Example Model Application 3 – Obstruction and Clutter Encounter. ....... 116
Figure 4-24: Use Case model illustrating the general functions of the required robot avatar.
.................................................................................................................................... 118
Figure 4-25: The interaction model for the Injury Prevention Telepresence System. .. 119
Figure 4-26: Main execution loop of the Injury Prevention Telepresence System. ...... 123
Figure 4-27: Expression of the "Monitor Activity" subsystem. ..................................... 124
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Figure 4-28: ANIMA System Structure. ....................................................................... 127
Figure 4-29: RobotBase development montage. (a) Drive and Controller assembly. (b)
Reworked mounting and custom power distribution. (c) Completed ultrasonic sensor array.
(d) 3D printed housings for the robot head. (e) Completed actuated turn-table mechanism.
.................................................................................................................................... 128
Figure 4-30: Electronics component block diagram. ................................................... 129
Figure 4-31: The completed version 1 of the RobotBase. ........................................... 130
Figure 4-32: Example of Visual Gestures sampling for identifying injurious actions. (Tee,
Lau, Siswoyo Jo & Wong 2016) .................................................................................. 132
Figure 4-33: Functionality exhibition during PECIPTA 2015.(Borneo Post Online 2016)
.................................................................................................................................... 133
Figure 4-34: Illustration of a standard telepresence robot services structure in MRDS.
(Microsoft 2012b) ........................................................................................................ 135
Figure 4-35: The CARMI navigation system state machine. ....................................... 138
Figure 4-36: Overview of the custom-build services for CARMI to be simulated using
MRDS and VSE. ......................................................................................................... 139
Figure 4-37: Example of the simulated CARMI and Child entities in VSE. .................. 140
Figure 5-1: Typical indoor environment simulated with VSE (Microsoft Corporation 2012).
.................................................................................................................................... 143
Figure 5-2: Classification of indoor obstacles. (Tee, Lau, Siswoyo Jo & Lau 2016) .... 144
Figure 5-3: A baseline and six obstacle scenarios. (Tee, Lau & Siswoyo Jo 2018a) .. 145
Figure 5-4: Baseline Scenario Sample. Unit for X-Z world coordinates in meters (m). 146
Figure 5-5: Combined CARMI paths during single uniform obstruction test. ............... 148
Figure 5-6: Combined motion graph and sample dataset for Uniform Obstruction with
Leftward Scatter. Unit for X-Z world coordinates in meters (m). .................................. 152
Figure 5-7: Combined motion graph and sample dataset for Uniform Obstruction with
Rightward Scatter. Unit for X-Z world coordinates in meters (m). ............................... 155
Figure 5-8: Combined CARMI paths during the single non-uniform obstruction scenario,
along with plot digitization of the alternate route and their approximated travel distance
comparisons. ............................................................................................................... 156
xiv
Figure 5-9: Combined CARMI paths during the non-uniform obstruction scenario with left
scatter field, along with plot digitization of the alternate route and their approximated travel
distance comparisons. ................................................................................................. 160
Figure 5-10: Combined CARMI paths during the non-uniform obstruction scenario with
right scatter field, along with plot digitization of the alternate route and their approximated
travel distance comparisons. ....................................................................................... 163
Figure 5-11: CARMI motion path logged during the Local Minima Problem recreation
scenario. ...................................................................................................................... 170
Figure 5-12: Graphical test results of the Multimodal Person-Following System. (Pang,
Seet & Yao 2013) ........................................................................................................ 172
Figure 5-13: Experimental pathfinding test results of the Meemo robot for tracking a
person in a gathering. (Harada et al. 2017) ................................................................. 173
Figure 5-14: Experimental pathfinding results of the fuzzy logic controller's performance
simulation. (Montaner & Ramirez-Serrano 1998) ........................................................ 175
Figure 5-15: Attempted replication of benchmark testing environments for performance
measurement. ............................................................................................................. 176
Figure 5-16: Plot digitization of the Human motion path from the Multimodal Telepresence
Robot project. (Pang, Seet & Yao 2013) .................................................................... 177
Figure 5-17: A graphical path result from a selected runtime sample of Benchmark 1
performance tests........................................................................................................ 178
Figure 5-18: Plot digitization of the Robot motion path from the Human-Following in
Crowded Environment project. (Harada et al. 2017) ................................................... 180
Figure 5-19: A graphical path result from a selected runtime sample of Benchmark 2
performance tests........................................................................................................ 181
Figure 5-20 Plot digitization of the Robot motion path from the Fuzzy Knowledge-based
Controller project. (Montaner & Ramirez-Serrano 1998) ............................................. 183
Figure 5-21 A graphical path result from a selected runtime sample of Benchmark 3
performance tests........................................................................................................ 184
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LIST OF TABLES
Table 4-1: Initial hardware plan for CARMI (Tee, Lau, Siswoyo Jo & Then 2015) ........ 78
Table 4-2: Initial software plan for CARMI (Tee, Lau, Siswoyo Jo & Then 2015) .......... 79
Table 4-3: Injurious gesture detection performance (Tee, Lau, Siswoyo Jo & Wong 2016).
...................................................................................................................................... 80
Table 4-4: Performance results of the human-orientation tracking system. (Tee, Lau,
Siswoyo Jo & Lau 2015) ................................................................................................ 84
Table 4-5: Example of raw distance input from ultrasonic sensors array. ..................... 96
Table 4-6: Adjusted example of ultrasonic sensors feedback. ...................................... 96
Table 4-7: Summary of generated Use Case stories................................................... 120
Table 4-8: Use Case story encompassing the autonomous monitoring function. ........ 120
Table 5-1: Excerpt from the combined logs of Uniform-Obstruction-Clear Sample 1. Unit
for X-Z world coordinates in meters (m). ..................................................................... 149
Table 5-2: Elapsed time for each sample in Uniform-Obstruction-Clear. .................... 150
Table 5-3: Elapsed time for each sample in Uniform-Obstruction-with-Left-Scatter. ... 153
Table 5-4: Elapsed time for each sample in Uniform-Obstruction-with-Right-Scatter. . 154
Table 5-5: A runtime sample excerpt from the combined logs of the Single Non-Uniform-
Obstruction scenario. Unit for X-Z world coordinates in meters (m). ........................... 157
Table 5-6: Elapsed time for each sample in Single Non-Uniform-Obstruction scenario.
.................................................................................................................................... 158
Table 5-7: A runtime sample excerpt from the combined logs of the Non-Uniform-
Obstruction-with-Left-Scatter-Field scenario. Unit for X-Z world coordinates in meters (m).
.................................................................................................................................... 161
Table 5-8: Elapsed time for each sample in the Non-Uniform-Obstruction-with-Left-
Scatter-Field scenario. ................................................................................................ 162
Table 5-9: A runtime sample excerpt from the combined logs of the Non-Uniform-
Obstruction-with-Right-Scatter-Field scenario. Unit for X-Z world coordinates in meters
(m). .............................................................................................................................. 164
xvi
Table 5-10: Elapsed time for each sample in the Non-Uniform-Obstruction-with-Right-
Scatter-Field scenario. ................................................................................................ 167
Table 5-11: Plot digitized travel distances for both Robot and Child entities for all samples
of Benchmark 1 performance tests. Travel distances are measured in meters (m). ... 179
Table 5-12: Tabulated calculations of Robot-To-Human travel distances for both
Benchmark 1 study and simulation results. ................................................................. 179
Table 5-13: Plot digitized travel distances for both Robot and Child entities for all samples
of Benchmark 2 performance tests.............................................................................. 182
Table 5-14: Tabulated calculations of Robot-To-Human travel distances for both
Benchmark 2 study and simulation results. ................................................................. 182
Table 5-15: Plot digitized travel distances for both Robot and Child entities for all samples
of Benchmark 3 performance tests.............................................................................. 185
Table 5-16: Tabulated calculations of Robot-To-Human travel distances for both
Benchmark 3 study and simulation results. ................................................................. 185
xvii
NOMENCLATURE
° Degrees
AAL Ambient Assisted Living
ADL Activities of Daily Living
AIRMT Active IR Marker Tracking
ANIMA Autonomous Injury Mitigation Avatar
API Application Programming Interfaces
ASD Autism Spectrum Disorder
CARMI Companion Avatar Robot for Mitigation of Injuries
CCR Concurrency and Coordination Runtime
COTS Commercial Off-The-Shelf
CP Cerebral Palsy
CWA Collaborative Wheelchair Assistant
DOF Degrees of Freedom
DSS Decentralized Software Services
EEG Electroencephalography
EMG Electromyography
EOG Electrooculography
ESM Experience Sampling Method
FOV Field of View
GPS Global Positioning System
HAL Hybrid Assistive Limb
IMU Inertial Measurement Unit
IoT Internet of Things
IQ Intelligence Quotient
IR InfraRed
IROMEC Interactive Robotic Social Mediators as Companions
LED Light Emitting Diode
LiDAR Light Detection and Ranging
LOS Line of Sight
xviii
LRF Laser Range Finder
m Meters
MATS Mechatronic Assistive Technology System
MEMS Micro-Electric-Mechanical Systems
MMDB Multimodal Dyadic Behaviour
MRDS Microsoft Robotics Developer Studio
PFM Potential Field Method
PIR Pyroelectric InfraRed
QOL Quality of Life
QR Quick Response
RF Radio Frequency
RGB-D Red Green Blue Depth
ROI Region of Interest
RSS Received Signal Strength
SDK Software Development Kit
SLAM Simultaneous Localization and Mapping
TB Turning Bias
TEBRA Teeth Brushing Assistance
VFH Virtual Force Histogram
VPL Visual Programming Language
VR Virtual Reality
VSE Visual Simulation Environment
WSA Wandering Standpoint Algorithm
1
Chapter 1: INTRODUCTION
This chapter introduces the area of application that the research intends to contribute in.
It encompasses initial literature survey over Assistive Robotics used in long-term
accompanying of people and seeks to identify the problems that prevent this technology
from being widely available for consumer-level use. The identification of these problems
leads to the establishing of research questions and objectives, forming the aim of this
study.
1.1 RESEARCH BACKGROUND
Assistive Technologies is a field that has long been in service of the elderly, children and
people with disabilities (Tee, Lau & Siswoyo 2014). One of its branches of study is
Assistive Robotics, which has made significant strides in aiding children with cognitive
disabilities. Originally predominant in augmenting therapies for children with Cerebral
Palsy (CP) and Autism Spectrum Disorder (ASD), there is a rising prevalence of
companion robots that are designed to accompany and interact with them (Jones, Trapp
& Jones 2011; Shamsuddin et al. 2012, 2013). Their function varies from reinforced
therapeutic exercises and induced play to aiding parental monitoring and injury prevention
(Cabibihan et al. 2013; Tee, Lau, Siswoyo & Then 2015). A large portion of the children's
time is spent indoors, which presents navigational constraints to an autonomous robot
that are commonly compensated using costly and resource-consuming technologies such
as Simultaneous Localization and Mapping (SLAM), Light Detection and Ranging (LiDAR),
and more. This usually results in companion robots being out of reach for most middle to
low income families.
There is yet to be a consumer-accessible companion robot platform that has widespread
acceptance. Over the last 40 years, robots have been widely circulated in consumer
markets from toys and industrial assemblers to intelligent household appliances. The
exponential growth of the Internet since the 1990’s has also instigated the introduction of
2
robots as interactive telecommunications platforms, telepresence avatars in medical
practice and elaborate Internet-of-Things (IoT) applications (Pang, Seet & Yao 2013).
There has also been a multitude of experimental applications for companion robots in
elderly care centers and various augmented therapies for cognitively impaired children.
Studies have shown that children with impaired social interaction skills have higher affinity
towards robots, preferring a non-human partner for therapy sessions. Because of this,
therapists have used robots as puppet surrogates to better elicit responses from their
children patients. There are also a variety of experimental companion robots that are
designed to interact and play with the children as a means of disguising reinforced
exercises between therapy sessions. These studies are explored as part of this
research’s literature review.
Despite the large number of assistive robot applications for people with disabilities, there
has yet been any widely accepted robot platform that is available for the consumer market
in terms of accessibility. Widespread accessibility can only be achieved if the platform is
easily acquired or scratch-built from common components, said components can be
locally procured in most parts of the world, and development resources can be freely
made available.
There are a few possible reasons for this, including the fact that most assistive robot
systems are developed for specific applications. Many of these systems serve to aid in a
single therapeutic function (e.g. reinforcing social interaction exercises, voice-controlled
avatar or etc.) and do not share common platform features (e.g. mobility between rooms
or floors, interfaces with external networks, unsupervised operation, etc.).
Also, most of these robots are not developed beyond their prototyping stages because
their purpose is to fulfil the requirements of their specific studies. The high hardware and
computational costs of a full-featured human-following companion robot is not feasible for
most research projects. Thus, the lack of common functionality, no incentive for
development beyond prototyping and high costs of companion robots result in little
3
demand for the creation of a platform that could potentially be refined for consumer
affordability use.
This research aims to contribute towards improving the current state of companion robot
accessibility. To do this, an initial study to identify the nuances of developing and
implementing companion robots was carried out. The findings show that one of the major
challenges in this endeavor is autonomous navigation.
As a human companion, a robot has several criteria to fulfil. Its principle requirement is to
perform human-following, an action that is comprised of tracking a human target and then
repositioning itself in response. The simplest example of this action can be observed by
physically tethering the robot to a human target using a rope. The tension on the rope
acts as a leash, indicating which direction the robot must redirect to so that it continues
to face its human target. Although this example is crude, it does help visualize the
navigational challenges that the robot must face.
The first challenge is to know which direction to turn to so that the target remains in front
of the robot. A physical leash can provide this information in the form of vectored tension,
while a virtual tether will need to employ some form of vision or wireless solution to
achieve this.
The second challenge is to maintain a specific distance between the robot and the human
target. A physical tether solves this by introducing rope tension to indicate distance, but
the challenge becomes more difficult for a virtual one. If the robot moves too fast or near
its target, a collision and injury could occur. Likewise, a robot that is too far away will lose
sight of the target and fail its escort function.
The third challenge is the act of reorientation and repositioning of the robot itself. Its
motion must be controlled, either via closed-loop control, visual servoing or equivalent
methods. The robot’s build and form must be considered during actuation to compensate
4
for motion deviations such as overshooting and drifts. These deviations impede the
human-following performance.
The final challenge is the ability to steer around obstacles in the operating environment.
The example of using a physical leash will most likely fail in this challenge, as the direct
tethering will cause the robot to turn and run into collision with objects between itself and
the human target. A virtual leash allows the robot the freedom to employ a variety of
maneuvering algorithms and methods, but the selection depends on what information and
provisions that is available to it.
For a companion robot to be effective, it must be able to overcome these challenges while
simultaneously perform its specific function autonomously. While there may be suites of
technologies to achieve this, their implementation cost may be unfeasible, as explored
further.
While a physically leashed robot offers a basic visualization of how human-following is
performed, almost all companion robots operate wirelessly. The foundation of robot
navigation is in localization, which is the ability to identify and track the location of itself
and the target within an operating environment. This is accomplished through a
combination of embedded environments, smart wearables and/or vision-based
technologies. However, due to the severity of the compound challenges of indoor human-
following, these systems often result in high cost of hardware and computational
resources.
Embedded environments involve installation and establishment of sensor networks within
the operating environment. These sensors help supply location information to the robot,
in addition to other miscellaneous environmental data. Unfortunately, embedded
environments are costly to set up and are not portable.
Smart wearables are sensors and devices mounted on the human target that serve as
beacons or part of virtual tethers that a robot can be “leashed” to. Variations of this method
5
are portable and cost less than embedded environments but are mostly effective at
reporting the proximity of the target, rather than actual position. Wearable technologies
are best employed in combination with other sensory techniques.
Vision-based sensors emulate human perception, by taking photographic snapshots of
the environment and using image processing to identify entities and obstructions. This
branch of technology is currently the most popular method for target identification and
tracking as well as robot-navigation. Sensors such as Light Detection and Ranging
(LiDAR) return 360° depth maps of the robot’s surroundings, enabling easy localization.
The drawback of vision-based technologies is their susceptibility to environmental lighting
conditions and image quality of their sensor output. Better performance can only be
acquired from high-cost options.
Effective human-following benefits from the ability of self-localization to enable navigation
in a known environment. This is commonly achieved via established mapping techniques
such as Simultaneous Localization and Mapping (SLAM), which require the use of LiDAR,
embedded environments or their equivalent. Mapping of the operating environment is also
the prerequisite for most of the existing navigation methods including A* algorithm,
Potential Field Method (PFM) and Wandering Standpoint Algorithm (WSA). The drawback
is that these methods require significant computational resources to accommodate path
searching and learning.
To summarize, one of the main contributors to the hampering of widespread companion
assistive robot accessibility is the complexity of overcoming autonomous navigation
challenges and the high component costs of its current solutions.
1.2 RESEARCH PROBLEMS
The initial literature survey and review have resulted in formalizing the navigation issues
into two main Research Problems (RP):
6
RP1: Complex Autonomous Navigation Challenge The ability to independently locate and move around a dynamic environment has been
the subject of intense research which resulted in surprisingly few universal solutions.
Machines lack the organic adaptability of animals and humans to easily maneuver
multiple kinds of terrain, thus making their development process very difficult and
complicated. This research narrows down on indoor navigation because most companion
robots operate under the roof while accompanying their human users. There is a need for
a simpler but homogenous solution to general indoor pathfinding and simultaneous
human-following so that the burden of custom robot navigation can be lifted from the
implementation effort.
RP2: Human-Following Robot Techniques Require Costly Sensor Technologies
Current indoor localization technologies such as SLAM and LiDAR require the use of
rotary rangefinder lasers, advanced environmental imaging devices and embedded
sensor networks that are either expensive, unfeasible or unwieldy for a standalone mobile
robot. In addition, pathfinding algorithms and machine learning that can help in optimized
human-following and obstacle avoidance require substantial computational resources to
cope with the volume of real-time calculations. A fusion of low-cost or simple sensor
solutions with acceptable performance limitations may be viable for lowering the difficulty
bar for indie developers of companion assistive robots.
1.3 RESEARCH AIM AND OBJECTIVES
The investigation of the presented research problems is carried out through exploring the
following Research Questions (RQ):
RQ1: What are the navigational challenges for indoor companion robots? RQ2: What available technologies are viable for solving the identified navigational challenges? RQ3: How to model a solution to the navigational challenge in indoor human following and obstacle avoidance?
7
The aim of this research is to model a robot navigation solution that relies on multi-sensor
fusion for real-time human-following and obstacle avoidance and gauge the effectiveness
of the model. Therefore, this aim is achieved through the following objectives:
RO1: To identify the challenges in navigation systems for indoor companion robots. The obstacles and difficulties faced in autonomous indoor navigation need to be studied
and encapsulated into an environment model with which navigation systems can be
tested in. This research aims to use this study to identify any possibility of abstracting the
perception-circumvention process to reduce the complexity of RP1.
RO2: To design a novel indoor robot navigation model to perform real-time human-following and obstacle avoidance. By abstracting the operating environment, a navigation strategy needs to be formulated
that capitalize on the perceived environmental attributes. The resultant model will then
provide a blueprint for implementing a robot control solution that emphasizes on
alternative sensory solutions that can overcome the high cost of RP2.
RO3: To evaluate the effectiveness of the proposed navigation model in indoor human following and obstacle avoidance. The robot control solution can then be gauged for validity by means of simulations against
both a set of isolated challenges identified in RO1. System performance can be measured
by comparing it to existing robot navigation studies with reproducible test data.
1.4 RESEARCH SCOPE
This research focuses on creating a novel navigation solution for companion robots that
operate in indoor environments. The reason for this is because most existing assistive
robot systems interact with their users in healthcare facilities and within the confines of
their homes. Also, autonomous robots in outdoor environments benefit from increasingly
accurate heading and localization systems such as GPS technology, which are
unavailable for their indoor counterparts. Hence, there is a significantly larger need for
indoor autonomous navigation solutions. This research will scope itself around improving
8
the operation of companion robots that are intended for typical indoor living environments
amongst human users.
Part of the research problem pertains to the high computational and hardware costs of
current companion robot implementations. This research aims to design and develop a
viable solution that does not involve complete environmental mapping, extensive external
sensor networks and extreme changes to the environment. This leaves computational
and hardware headroom for the specific functions an implemented robot may be tailored
to have, in addition to operating as an all-in-one portable package. The resultant robot
control will be implemented to run in standalone robot operating systems like typical
consumer variants.
An object-oriented approach is adopted for the design of solution, highlighting its ability
to utilize Commercial Off-The-Shelf (COTS) sensor hardware and combination with
existing navigation algorithms to complete the navigation system. This helps minimize the
difficulty in implementing companion robots tailored for aiding disabled people in low-
income families. The implementation process will emphasize on the use of consumer
procurable components and products to make the template available for use throughout
the world.
This research aims to present a novel robot navigation model that optimizes the
application of existing real-time pathfinding algorithms using perception data from multi-
sensors fusion. Thus, the design of the model emphasizes on generic algorithm interfaces
that can suit any vision-based non-mapped pathfinding methods. This research does not
include developing another pathfinding method because there is already a myriad of
existing indoor pathfinding algorithms which can be selected based on varying operating
environments and specialized robot purposes. The navigation model will serve as an
augmentation to any choice of existing pathfinding algorithms.
This research entails the design and development of a robot navigation model which does
not include the development of a companion robot. This is because the model must be
9
devised as a generic autonomous robot system that can be applied on any companion
robot that satisfies the required design template. Testing and performance benchmarking
will be done via software simulation. However, a baseline companion robot project is
selected to serve as a platform for implementing the model for the simulation purposes.
1.5 THESIS ORGANIZATION
This thesis documents the findings, proposed model, developed system, simulation
results and findings of this research within the following chapters:
Chapter 1: Introduction Provides an overview of the research problems, and how it is composed of research
questions that can be answered through the study’s aim and objectives. The overall
methodology used to organize the research is also explained here, in addition to a
summary of each thesis chapter’s content.
Chapter 2: Survey of assistive robotics The scope of this research covers companion robots for children with cognitive disabilities
such as Cerebral Palsy (CP) and Autism Spectrum Disorder (ASD). This chapter
documents the state-of-the-art of assistive robotics in service of these disabled children
and discusses the common traits or functions that can be found amongst the individual
robot prototypes. This chapter concludes by composing a generic robot template that can
be used to guide the design of a low-cost companion robot. This template was utilized in
creating an injury mitigation robot (CARMI) as an example platform for implementing this
study’s navigation system. In addition, this chapter also helped uncover the key
challenges that cannot be simply mitigated using that template’s COTS-centric approach.
Chapter 3: Survey of human tracking technologies This chapter attempts to delve into the nuances of the identified challenges in
implementing indoor companion robots: the need for an autonomous navigation system
that can traverse common human-populated rooms and a reliable method to tracking the
10
human subject’s position so that the companion robot can perform effective human
following. The spectrum of each problem is explored by reviewing related existing
research works, along with their proposed solutions. These publications help provide
clues to how a suitable fusion solution can be formulated to simultaneously tackle both
challenges. This chapter ends by proposing a joint human-following and indoor navigation
solution.
Chapter 4: Design and Development of the Multi-Sensor Fusion-Based Navigation Model To minimize the drawbacks of depth camera body detection, an Active InfraRed Marker
tracking system was created and documented. For the model’s Phase 1, this system is
used to reduce false detections and lock onto the body profile of the target human for
consistent activity tracking. Phase 2 involves transformation of the raw depth frame,
perimeter proximity sensors feedback and Phase 1 position data to help the robot decide
which direction around an obstruction to begin avoidance maneuvering. Lastly, this
chapter covers the design and implementation process of this model into a Microsoft
Robotics Developer Studio (MRDS) system that can be ported for both simulation and
robot hardware.
Chapter 5: Testing and Evaluation
This chapter documents the design and development of simulation scenarios that act as
the navigation system’s functional testing and performance benchmarks. The results are
presented here and discussed regarding its performance and viability.
Chapter 6: Conclusion This chapter presents the overall progress of the research effort by mapping the research
findings to the objectives and questions, before concluding with a discussion of possible
improvements and future work direction.
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Chapter 2: ASSISTIVE COMPANION ROBOTICS AND THEIR CHALLENGES
Robotics has had a long history of application in a variety of industries since their infancy
in early Greek, Chinese and Egyptian civilizations. First introduced as automata solely for
entertainment, robotics has started to be used for relieving humankind of menial and
repetitive tasks since the mid-1900s. The technology was limited to purely industrial work
with limited to no direct human contact until the beginnings of rehabilitation robotics.
Unlike the industrial predecessors that center upon swift and precise execution of actions
using heavy equipment, rehabilitation robotics focus on lightening the workload of
therapists and caregivers for disabled children and the elderly. In these cases, human
factors, safety, intelligent adaptation of treatments according to previous progress, and
providence of Quality of Life (QOL) services are more emphasized for user wellbeing and
assisting therapeutic processes.
This chapter introduces the state of the art for Assistive Robotics and how companion
robots are part of this collection of technologies. Companion robots that are aimed at
long-term close-contact with their patients face a host of unique challenges in both
development and operation, which are also discussed.
Since the spectrum of assistive robot applications is incredibly wide, this research focuses
on the specific area of Assistive Robotics employed for aiding children with cognitive
disabilities such as Autism Spectrum Disorder (ASD) and Cerebral Palsy (CP).
2.1 ASSISTIVE ROBOTICS IN PREVENTING INJURIES
There have been a multitude of Assistive Robotics research effort dedicated in aiding
children afflicted with ASD and CP, which involves the active participation of Information
Communications Technology and Robotics as Assistive Technologies. Due to stereotypy
or motor function trouble, children with cognitive disabilities are subjected to the primary
12
cause of most injuries – falls. Falls had been found to be the biggest threat to elderly and
disabled patients, indicating that the detection and handling of falls should be of utmost
importance. Falls had been categorized into 3 forms, based on whether the victim fell
from sleeping, standing or sitting positions (Yu 2008). Thus in order to detect falls, current
assistive technologies come in the form of wearable devices, embedded sensors and
vision-based solutions (Mubashir, Shao & Seed 2013).
Wearable devices are usually sensor packages that are attached to the human body for
gauging and detecting the conditions which describe an injury occurrence. Some devices
are also equipped with health monitoring sensors to track real-time health signatures of
the patient to healthcare personnel remotely. It is by far the most common approach to
injury detection and prevention, without requiring extensive modifications of the living
environment. However portable, wearable devices require strapping equipment and
electronics to the human body, thus imposing significant intrusion to the subject’s
performance of daily routine. The user needs to be disciplined and trained to consistently
wear the sensors in the same correct manner, as well as attempt to get by with daily
routines as normal as possible. In most cases, live tests could not be carried out
consistently due to the test subjects’ lacking will and commitment to the devices (Tee,
Lau & Siswoyo Jo 2014). One good example of a wearable device for fall detection was
presented in Singapore in 2008, which uses sensors to examine the posture and motion
of the human body in real-time. If a combination of readings match those found in fall
templates, then a healthcare crew will be notified of a possible fall event (Yu 2008).
Likewise, another study attempted to integrate a piezoresistive sole into running shoes
for monitoring the pressure spots on the bottom of the feet during ambulation. The shoes
were developed to assist gait training by providing timely feedback on how the patient
walks. The system can be used to detect abnormal pressure signatures such as loss of
footing which indicates an impending fall (Canavese et al. 2014). The wide availability of
smartphones integrated with accelerometers had been capitalized by another study,
which utilized the said sensors in tandem with shoes embedded with sensors to anticipate
abnormal walk gait. In the event of falls, the system would automatically notify caregivers
for immediate assistance (Majumder et al. 2014).
13
Embedded sensors or the ambient approach aims to allay the intrusive nature of wearable
devices by embedded sensors within the confines of the living environment. Therefore,
instead of placing bulky appendages onto clothing or the body, sensors are integrated
into flooring, walls, ceilings, furniture and appliances to gauge and monitor the health
signatures of the patient. Embedded environments also partially serve as an enabler for
smart homes and automated living space. Even though the ambient approach completely
removes the need for intrusive wearable devices, they do require immense amount of
cost and work to acquire, embed and network the living environment. Also, the system
would only work when the patient is within the confines of the living environment, unlike
wearable devices which follow the patient around wherever he or she goes (Tee, Lau &
Siswoyo Jo 2014). A typical example of an ambient approach can take the form of a home
with flooring that was fitted with arrays of pressure and vibration sensors. While this
implementation is electronically simple, it provides a reliable method of indoor tracking as
well as fall detection. Pressure concentrations caused by feet would help locate anyone
within the premises, while any adverse trauma would signal a possible fall event.
Installation of such a system would unfortunately require a major overhaul of the flooring
(Yu 2008). Yet another simple ambient implementation is a bed that is fitted with bladders
that are filled with measurable liquid. By tracking the changing distribution of fluid within
the bladders, the system can identify and record how the patient shifts sleeping postures,
or whether a fall from the sleeping position may possibly occur (Yu 2008). Ambient
Assisted Living (AAL) describes the dimensions for intelligent living spaces that combines
both ambient and wearable devices for implementing telemetric health monitoring, indoor
personal localization, automated emergency alert and more. In addition to falls, AAL is
also geared to detect other danger situations that can cause injuries, such as fires, and
other critical health conditions (Spasova & Iliev 2014). Another similar system to AAL
integrates autonomous suites of triggers, sensors and alarms in order to monitor the
health state of the patient, and adjust the environmental condition to stabilize him or her
until auto-notified health personnel arrives (Taraporewalla 2014).
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The third approach to injury detection comes in the form of vision-based systems that
utilize a myriad of camera hardware and computer vision instead of micro-electro-
mechanical-systems (MEMS) that use discrete sensors as discussed previously. Vision-
based systems utilize various technologies such as Infrared imaging, stereoscopic
cameras and combinations of depth sensing sensors to detect the location and orientation
of people who are within their view. The computer vision software will then process the
data feed and discern the orientation, distance, height, posture, and limb positions of the
subject (Mubashir, Shao & Seed 2013). When aimed at detecting falls and injury-causing
activities, vision-based systems provide a middle-ground between wearable devices and
the ambient approach, both in terms of intrusiveness and complexity in environmental
setup (Tee, Lau & Siswoyo Jo 2014). Vision-based systems had traditionally been
accomplished using high frame-rate cameras and motion-capture harnesses, but since
2009, motion tracking sensors such as Microsoft’s Kinect has been able to track human
position and skeletal orientations without the need for any wearable markers (Pham 2009).
Other alternatives to the Infrared-based depth sensor are available such as the Asus
Xtion Pro (ASUSTeK Computer Inc. 2014). Alternative technologies in the form of fixed
stereoscopic camera arrays can also be found in products such as Sony’s PlayStation
Camera (Sony Computer Entertainment Inc 2013) and Intel’s RealSense Camera
(Creative Technology Ltd. 2014). Both technologies are viable for 3D human body
tracking, albeit with differences with respect to reaction to environmental lighting
conditions and tracking accuracy. The Microsoft Kinect sensor has been heavily utilized
in human tracking efforts, such as the implementation of a human escorting telepresence
robot that moves in stride with a human conversation partner (Cosgun, Florencio &
Christensen 2013). Another Kinect-based project utilizes multiples of the sensor in order
to actualize an assembly station robot that examines the posture and motion of its human
partner and then calculates the best motion paths that prevent collision with him or her
(Morato et al. 2014).
A pivotal research was carried out in 2013 using a Microsoft Kinect sensor for human
activity tracking and detection of possible injuries. This system tracks the human subject
and attempts to match the input with a database of template activities for discerning what
15
the subject is currently doing. The system can detect 14 different activities at varying
degrees of success, ranging from standing and walking to drinking and eating (Ann & Lau
2013). This approach would seem to be the best lead for non-contact activity monitoring
and injury prevention for children with cognitive disabilities. Although vision-based
systems may seem to be the bulletproof solution to the difficulties faced by wearables
and the ambient approach, they do still suffer from hardware-limited Field of View (FOV),
false readings, interference from environmental lighting conditions as well as complexity
of implementing machine vision solutions.
2.2 ASSISTIVE ROBOTICS IN PHYSICAL REHABILITATION FOR CEREBRAL PALSY
Robotics for physical rehabilitation largely serve people who are diagnosed with Cerebral
Palsy (CP). There are 3 classes of CP cases, namely spastic, non-spastic (being dystonic
or ataxic) or a combination of both types (McMurrough et al. 2012). These types
differentiate the level of damage to the Pyramidal Tract that the patient is experiencing,
caused by irregular or fluctuating muscle development which leads to posture and
balance difficulties. This condition complicates the patient’s mobility, ability to interact with
the environment and communicating body language, inevitably interfering with personal
growth and capability of independent performance of daily routines.
Children with Cerebral Palsy suffer from muscle decay, motor control difficulties and
spasms which could be minimized via repetitive stretching and physiotherapy. By 2012,
Assistive Technologies for rehabilitation of children with CP come in 3 forms: Assistive
Devices, Interactive Games and Robotics. “Assistive Devices” is a category of tools
fashioned from household and care items that have been augmented for ease of use by
people with physical impairments. Examples of these tools include motorized wheelchairs,
intelligent spoons and the Ankle Foot Orthosis (McMurrough et al. 2012). As Assistive
Devices become increasingly intelligent and connected to the web, its distinction from
Assistive Robotics begin to diminish. Interactive Games have been steadily progressing,
partially due to the resurgent interest in consumer Virtual Reality (VR) facilities. These
16
games present interesting virtual environments that the patients can interact with, via a
series of active and passive exercises.
Assistive Robotics for rehabilitation consists of intelligent devices that act as components
in Robot Mediated Therapy. These devices offer mechatronic assistance in both sensing
the performance of the patient as well as active actuation of stretching exercises. While
a majority of these technologies are originally developed for aiding stroke and physically
disabled patients, their application can be adjusted to suit the needs of children with CP.
Assistive Robotics help in physical treatments for impaired lower and upper extremities,
as well as walking gait (Tee, Lau, Siswoyo Jo & Then 2015).
One prototype mechatronic apparatus combines both passive stretching and active
movement rehabilitation in a compact form factor. While concentrating on only the lower
extremity, the machine was targeted towards children with spastic CP which often cause
involuntary muscle spasms. These spasms often cause imbalance between both legs,
thus impeding normal ambulation. The apparatus can be strapped to the child and
automatically commence with passive stretching routines according to preset treatments
(Wu et al. 2011). A commercial scale robotic treadmill was developed by Reha
Technology AG for the assistance in rehabilitating ambulation capability.
The G-EO System, shown in Figure 2-1, is an end effector device which suspends the
patient’s body and simulates ambulation via actuated footpads. An end effector device is
basically a robotically actuated appendage that is grasped or connected to a patient’s
limb, simulating exerted forces typically experienced in a specific exercise routine. The
machine can simulate up to 3 Degrees of Freedom (DOF) for each foot and is extensively
used for extended sessions of repetitive walk exercises. The G-EO System is also
reprogrammable to support various custom routines for strengthening the patient’s
walking gait and control (Reha Technology AG 2012).
17
Figure 2-1: G-EO System by Reha
Technology AG (Reha Technology AG 2012).
Figure 2-2: The HapticMaster end effector
device (Delft Haptics Lab 2018).
Yet another example of an end effector device used for physical therapy would be the
HapticMaster robot arm (Figure 2-2), used in tandem with the NJIT-RAVR system in a
study that combines virtual reality and an adaptive robot for treating negative muscle
responses. A child with cognitive disabilities who suffers from negative muscle responses
can use this system to enter a simulated environment, with the HapticMaster acting as
the object to be grasped, manipulated or maneuvered according to the running program
(Fluet et al. 2009).
2.3 ASSISTIVE ROBOTICS IN SOCIAL INTERACTION REHABILITATION
Children with social and communication impairments require treatment of a mental and
interactive nature. While conventional rehabilitation seeks to establish physical aptitude
to patients in order to accomplish daily tasks, socio-communicative function rehabilitation
is not as straight forward (Tee, Lau, Siswoyo Jo & Then 2015). In this respect, children
with ASD face 3 major difficulties: beginning and maintaining social interactions,
participating in active play and stereotypy (Ricks & Colton 2010).
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One lead that has spearheaded robotics as one of the prime assistive technologies
available for the support of rehabilitation and supervision of Autistic children indicated that
observations on these children show that they are far less intimidated by interactions with
robots as compared to fellow human beings (Michaud et al. 2006). A study hypothesized
that children with social and communication impairments find robots more appealing as
they lack the confusing and complex mixture of gestures, expressions and words from
human beings that cause the sensation akin to sensory overload (Ricks & Colton 2010).
Robots are designed with clear and consistent representations of interaction cues,
simulating correct and repeatable expressions to suit individual tailored exercises
(Michaud et al. 2006).
2.3.1 Affinity to Robotic Interaction Companions
Children suffering from Autism Spectrum Disorder (ASD) have often been described
feeling confused, frightened and frustrated by trying to understand social communication
concepts such as facial expressions, body language and social cues. Being detached
from the normal learning curve for social interaction, they experience sensory overload
when facing simultaneous tasks of maintaining eye contact, scanning facial expressions
and managing other social signals (Ricks & Colton 2010). Repeated bouts of sensory
overload from early childhood lead to growing fear and negative attachment to the
presence of other humans, making Autistic children seem lonely, aloof and displaced at
social environments. Eventually, this fear of people will develop into significant
communication impairments that would disrupt the child’s ability to live independently in
society. This condition also makes rehabilitation increasingly difficult for therapists as the
child gets older.
An interesting discovery was made when children with ASD were observed interacting
with animated robot constructs. It appears that most of them demonstrate more visible
response and less intimidation when interacting with anthropomorphic robots than they
do with human peers. One possible reason for this is that robots can be constructed to
show specific facial expressions clearly, as opposed to humans who are capable of
19
displaying complex combinations of expressions (Michaud et al. 2006). Also, the
expressions could be replicated clearly and consistently, easing the learning curve of the
child at recognizing which facial expression corresponds to a specific emotion before
transitioning to a live human partner at a later stage.
Another pilot study was conducted to investigate the responses of children with lower
Intelligence Quotient (IQ) classifications towards a humanoid robot. Lower IQ is a
common characteristic of cognitively impaired children who have negative reactions
towards human interaction. The study found that 5 of 6 children showed largely positive
reactions, indicating that there is a possible advantage at using child-robot interaction as
a means of encouraging active social responses from children with communication
impairments (Shamsuddin et al. 2013).
A robot companion can also act as a non-human social partner, gradually acclimatizing
the child to initiating active play, participate in joint imitation exercises and practicing basic
social cues. The robot may also function as an introducer to a human peer as part of the
social interaction exercise. Figure 2-3 shows an example model of robots used as
mediators in social rehabilitation of disabled children.
Figure 2-3: Triadic Interactions model (Colton, Ricks &
Goodrich 2009).
Figure 2-4: KASPAR (Wood et
al. 2013). Called the Triadic Interactions, this model ties the Autistic child with social impairments
to a human companion with the robot acting as a mediator, and a Wizard of Oz entity who
20
acts as a teleoperator of the robot and indirect accomplice to the companion (Colton,
Ricks & Goodrich 2009). In a typical session, a child is left alone with the robot, with the
Wizard of Oz controlling it from behind the scene. Exercises ranging from basic
conversation to joint imitation games can be performed using the robot. Once the child
has become accustomed to the robot, it can then be used to introduce a human
companion into the room. The robot can be used to lead activities and conversations
between all three entities until the child can acceptably interact with the human
companion entirely without the robot’s presence.
One particularly successful application of mediatory robotics according to the Triadic
Interactions is the use of the humanoid robot called KASPAR, shown in Figure 2-4.
KASPAR was developed by the Adaptive Systems Research Group at the University of
Hertfordshire to facilitate robot mediated social sessions between an Autistic child and a
human companion. The child-like robot has actuated facial features and upper extremity
limbs for expressing emotions during conversations. In one application, KASPAR was
successfully utilized to extract information favorably from children during high-stress
interviews. This will prove invaluable for future law enforcement, healthcare and social
service uses (Wood et al. 2013).
Though the impaired children’s affinity towards robots is a positive sign towards the
progression of Assistive Robotics, some studies suggest that this affinity may only be
applicable to children with ASD or specific socio-communication conditions. One such
study showed that normal-functioning people empathize more towards robots that closely
resemble human features (Riek et al. 2009). These findings hint that children with ASD
are more receptive of anthropomorphic robots because they do not perceive the need for
the magnitude of empathy that human companions seem to demand.
2.3.2 Robot-Mediated Social Communication Treatment
A child with ASD or other conditions that share similar characteristics is treated by
establishing basic components of communication skills, which include joint attention,
21
imitation, active play, taking turns and recognition of emotions (Ricks & Colton 2010).
These therapy sessions are rigorously repeated to ensure iterative reinforcement of
lessons, like developing muscle memory. Initial sessions are conducted between the child
and a therapist. After extensive lessons, the child can be introduced into sessions that
include other facilitators or peers. Thus, the therapy process for a single child is grueling
and time consuming for both parents and therapist. The amount of time and effort required
depend on how quickly the child can acclimatize to the presence of another human being.
This means that group session successes depend highly on the level of its members’
rehabilitation progresses.
The child will have to be formally diagnosed with ASD. Autistic symptoms in children can
be visibly identified by trained therapists from the age of 3 years and up (Ricks & Colton
2010). The most likely advice upon positive diagnosis would be to begin rehabilitative
therapy sessions as soon as possible. For higher chance of rehabilitation success, the
child will have to undergo continuous and consistent sessions to reinforce their learning.
This can be incredibly difficult for both child and parents due to the sessions being foreign
and intimidating. An Autistic child or one who suffers from language impairments often
feel inadequate in front of other people, especially towards therapists and caregivers. It
is in these particular moments that robots can lend a hand; their toy-like design and
motions can help coax little children to initiate active interaction and play (Colton, Ricks
& Goodrich 2009). Once a child is familiar with the presence and interaction with the robot,
it can then begin to act as a mediator between her and the therapist. All the while, an
operator (referred to as the Wizard of Oz in the Triadic Interactions model) continue to
control the robot’s actions and conversations hidden from view. It is imperative that the
robot does not establish itself as the focal point of interaction. Rather, it should be used
to introduce interactions with the human companion (the therapist) as soon as the child
is able. Doing this should prevent the robot from becoming an emotional crutch for the
child.
The first therapeutic exercise for building social interaction skills is the ability to imitate.
This attracts the child’s attention to detail and acts as a precursor to active play. When
22
done by a therapist, it is usually difficult to convince a child to begin participation. By
introducing the activity using a robot medium, the stigma of human presence is negated.
The presence of a human companion in the activity is gradually introduced by the robot.
Honda Research Institute had developed an advanced humanoid robot platform called
ASIMO which had been used to experiment with robot-mediated imitation games for
disabled children (Colton, Ricks & Goodrich 2009). Though the experiment was largely a
success, the immense cost of the robot would mean that the technology is still out of
reach for most households.
A research was undertaken to develop similar but simpler mediator robots for facilitating
therapy sessions which include imitation games, as shown in Figure 2-5. TITO was
designed to have a friendlier cartoonish look, with two actuated arms in addition to a
rotating and nodding head complete with an LED matrix for a mouth. TITO was designed
to appeal to young patients and is adequate for aiding in simple recognition of facial
expressions, body language and basic imitation. However, its physical limitations
constraint its accuracy for imitation games, although this would be the focus for its future
improvements (Michaud et al. 2006).
Another robot called CHARLIE was developed with similar build and purpose as TITO,
being equipped with hand and face tracking using an integrated camera in addition to its
actuated arms and head. CHARLIE uses the vision-based tracking system to implement
more accurate and involving imitation games. It is also capable of operating in two-player
configuration, for turn-taking imitation plays (Boccanfuso & O’Kane 2011).
23
Figure 2-5: Tito (Michaud et al.
2006).
Figure 2-6: NAO H25 features diagram (Aldebaran
Robotics 2014).
Subsequent communication skills such as active play, joint attention and recognition of
emotions require repetitive exercises to reinforce a child’s understanding and ability to
overcome her condition’s impediments. In most parts of the world, there are shortages of
trained therapists. It is often difficult for parents to schedule consistent sessions with
therapists. The Interactive Robotic Social Mediators as Companions project (IROMEC)
set out to develop a personalized companion robot that would hopefully assist in
facilitating reinforced lessons for Autistic children. The IROMEC was designed to be
programmable with a host of interactive games. Using its graphical user interface and
mobile robot build, it aims to coax children to initiate active play (Ferrari, Robins &
Dautenhahn 2009). While the robot is not designed to be humanoid, it is constructed to
withstand the wear and tear of domestic use, encouraging future development of
consumer obtainable access to Assistive Robotics.
Aldebaran Robotics developed a stand-alone humanoid robot called the NAO in 2004,
which is of a smaller scale when compared to Honda’s ASIMO. The NAO is fully
articulated with functioning upper and lower limb manipulation, as depicted in Figure 2-6.
24
The robot has been put to task with a multitude of robotics challenges including robot
soccer matches, humanoid robot locomotion and children rehabilitation. The NAO can be
reprogrammed to suite a multitude of interactive storytelling, game playing and mediatory
functions, utilizing its suite of sensors, cameras and voice recognition facilities. One study
made use of the NAO for imitation exercises in the attempt to document the behavioral
profiles of children with ASD (Tapus et al. 2012). Another study used the NAO’s HRI
modules that harnessed the robot’s LED eyes, audio speech, music and full-range
motions to successfully engage and hold the attention of 4 out of 5 children in its control
group (Shamsuddin et al. 2012).
Once the child has achieved a level of social interaction acuity with a single person without
the need of continuous robot company, she is ready to begin therapy sessions with
multiple peers. Again, the challenges of introducing the presence of other human beings
into the child’s social environment are great. While the interaction between the child and
a therapist may be stable, leaving two or more similarly afflicted children alone is
unpredictable. In this scenario, a robot can help introduce group interaction gradually, as
each child would have been accustomed to robot-based incremental social introductions
by that point. A child can first be coaxed into participating in a social activity using a robot,
before additional partners are individually added. One study performed this using a robot
constructed out of Lego, as shown in Figure 2-7. This robot mediated between two
cognitively impaired adolescents. The robot begins by passing a ball between itself and
a patient. Then, the game is altered by adding a pair of red and green cards. As a cognitive
task, the patient will figure out that displaying the green card signals the robot to kick the
ball back to him. The third scenario involves both patients taking turns to display the card
and pass the ball back to the robot. Finally, the robot is removed from the environment,
leaving both patients playing the game with each other. There were initial challenges in
acclimatizing the patients, but once the session is under way, both had to be stopped by
the therapists. The experiment was a positive indication of the potential of robots as a
non-human medium for easing group social therapy sessions. (Costa et al. 2010).
25
Figure 2-7: Scenario 3 transpiring between both patients and the facilitating robot (Costa et al.
2010).
Children with less severe social impairments can also benefit from mediatory robots in
group interactions. Group interactions can present these children with increased risk of
sensory overload and anxiety attacks. Instead of having a companion robot, Assistive
Robotics can also come in the form of a shared activity, promoting group work as a way
of allaying fear of others. A study of this was conducted in 2009 where a Lego robot-
building class was organized to examine its impact on fostering teamwork between
groups children with ASD. The children were positioned in groups of 3 and were tasked
to gradually build a simple robot. Their creations would compete within an arena to collide
and achieve simple challenges. The groups were observed for individual proximities,
communication, joint attention and emotional responses. The study reported positive
findings, stating progressive group collaboration within a shorter period of time compared
to normal therapy sessions (Wainer et al. 2010).
2.3.3 Robot-Mediated Active Play
Throughout early childhood, active play is responsible as the primary means for a child
to develop her senses and interactions with the living environment. As an activity, playing
helps to develop their motor skills in addition to exercising creative expressions and
enthusiasm with group dynamics. The International Classification of Functioning and
Disability states that the act of playing is an important factor in assessing a child’s Quality
26
of Life (Marti, Pollini & Rullo 2009). By default, normally-functioning children are expected
to automatically begin playing when presented with a toy. However through observation,
this has not always been the case (Trevor, Howard & Kemp 2009). It can be expected
that children with CP or ASD have even lower inclination of initiating play, possibly due to
lacking interest or physical strain.
The previously-discussed mediatory robot called KASPAR was used for an experiment
to determine if it can help spur the interest of a teenager with ASD. The robot featured a
head with 8 Degrees of Freedom (DOF) and arms with 3 DOF to minimally express human
features but is anthropomorphic to avoid rejection (due to it being too human-like). The
16-year-old teenager was reported to have little tolerance of other peers during play.
Once allowed supervised operation of KASPAR, the teenager expressed unexpected
fascination over the controls. This outcome was not previously possible with other therapy
attempts. Eventually, the teenager has gradually learned to participate in group imitation
games with other peers, using KASPAR as his avatar (Robins, Dautenhahn & Dickerson
2009).
Figure 2-8: Neuronics Katana 6M180 (Trevor, Howard & Kemp 2009).
27
The ROBOSKIN project was undertaken to add a synthetic skin that could facilitates
tactile feedback through KASPAR (Amirabdollahian et al. 2011). This additional feature
could offer deeper insights into the child’s degree of motor control over body contact
during robot-mediated play sessions (e.g. gauging grip strength during shaking hands,
and hi-fives). Continued development of mediatory robots such as KASPAR could lead
to the development of Assistive Robots as robotic playmates instead of robotic toys.
While direct control of KASPAR would technically render it as an intelligent toy, the
addition of autonomous operation (which turns it into a robotic playmate) may eventually
enable it to independently interact and accompany children, relieving parents and
caregivers for short moments. In the pursuit of demonstrating the application of a robotic
playmate, a system was developed using a Neuronics Katana 6M180 Robot Arm, shown
in Figure 2-8. The robot’s end effector is fitted with a camera for identifying colored blocks.
The robot system was used for imitation exercises using the blocks (Trevor, Howard &
Kemp 2009).
A robotic playmate was developed in the form of a piano-playing robot. The system was
programmed using the Boardmaker Plus educational software. The software was
designed to be a simplified graphical programming environment that disabled children
could be taught to use. With this, the children were able to manipulate the robot to hit the
keys of the toy piano (Jones, Trapp & Jones 2011). This form of assisted-play is
developed with the similar aim as the Katana robot project discussed previously.
The previously discussed IROMEC (Figure 2-9) is not merely limited to specific
facilitations of reinforced interactive exercises for children with ASD. It was essentially
intended to be a robotic companion for disabled children, which includes assisted play.
As the IROMEC is reprogrammable, it is possible to equip it with limited autonomous play-
assisting routines. To an extent, the IROMEC can also adapt to the child’s performance
in terms of motor control and cognitive skills, adjusting games and interactions for a
personalized learning curve (Ferrari, Robins & Dautenhahn 2009).
28
Figure 2-9: The IROMEC (Ferrari, Robins &
Dautenhahn 2009).
Figure 2-10: Roball (Trevor, Howard
& Kemp 2009).
Non-autonomous robots may yet contribute to assisted play as intelligent toys. The Yale
Child Study Centre attempted to demonstrate the applicability of intelligent toys in helping
to diagnose early signs of Autism (Trevor, Howard & Kemp 2009). The series of toys
contain passive sensors that tracks the motion and forces acted upon them as they are
played by the children. One such toy called the Roball (shown in Figure 2-10) comes
equipped with voice feedback to interact with the child. The ball can identify whenever it
was left unattended, carried or engaged in active play. The Roball reacts to its various
states using a bank of audio responses, eliciting more responses to initiated play by
cognitively disabled children (Michaud et al. 2006). A modified version of the Roball was
fitted with more obvious audio cues aimed at children with impaired vision acuity. Called
the I-Ball, the intelligent ball is intended for facilitating group soccer games for visually-
impaired children (Stephanidis & Antona 2013).
2.3.4 Robotics in Preventive Intervention
As previously discussed, the characteristics of cognitive disabilities can be identified from
the age of 3 years and above. This is especially true for Autism Spectrum Disorder (ASD)
as there are standard tests available that can be administered by trained therapists (Ricks
& Colton 2010). Early detection is vital as preventive measures become increasingly
29
difficult to apply as the child matures. In addition, early participation in rehabilitation
sessions would minimize the condition’s impact on learning and social development.
Treatment for cognitive disabilities are costly in terms of time and effort for both the
parents and the therapists. Due to global shortage of trained therapists, access to early
diagnosis of ASD and CP may not be possible for every family. The diagnosis process is
not easily replicable through artificial means because each child is assessed individually,
with differing tolerances for both interactive and cognitive processes. Therapists depend
on their experience to adjust the parameters of the tests according to each child. One
study attempted to implement a physiology-based robotic solution that gauges the child’s
level of enthusiasm. Through an affect-inference mechanism, the system matches the
enthusiasm level with an appropriate level of play for assessing the child for cognitive
impairment. The mechanism is a stepping stone towards developing Assistive Robotics
with the ability to perceive and understand the morphing states of its various users (Liu
et al. 2008). Future development of this system may eventually lead to the creation of
intelligent systems that relieves therapists of the sensitive process of diagnosing for early
signs of cognitive disabilities.
Mediatory robots could also be used to facilitate diagnostics of early social interaction
impairments. Since most children taking the tests are considerably younger than the
target age bracket for conventional therapy sessions, the robot will most likely be
designed to be much friendlier and simpler than constructs such as KASPAR and the
NAO. One mediatory robot was designed specifically for early intervention, consisting of
a durable robot with actuated eyes, eyelids, head and wings. The robot can be operated
semi-autonomously, following the orientation of the child’s face for joint attention
exercises. Otherwise, it is teleoperated by the therapist for imitation games and
recognition of facial expressions (Dickstein-Fischer et al. 2011). The robot is tethered
wirelessly to the operator’s controls, opening the doors to possible teleoperation via the
Internet and uncoupling therapists from geographical constraints.
30
LEGO has been featured twice in this chapter for providing the building blocks in the
construction of a mediatory robot as well as the vehicle for facilitating group activity. In a
similar fashion, the LEGO line of products presents a safe and flexible means for children
to exercise creative development and active play. Routine play of LEGO system products
in a group setting may help disabled children deviate from stereotypical reclusive behavior
– one of the first challenges to defeat via early intervention (Costa et al. 2011).
2.4 LIFE-LONG ASSISTIVE ROBOTICS FOR COGNITIVELY DISABLED CHILDREN
So far, it appears that the most successful social-aid implementation of Assistive Robotics
had been as components in robot-mediated therapy sessions. Physical rehabilitation with
robotics were largely augmentation solutions for existing physiotherapy functions. Both
facets share the common mode of operation being directly controlled by therapists. The
systems had to be tethered to a remote-control solution that still depends on trained
operators, making current Assistive Robotics unsuitable for prolonged independent use.
The goal of life-long assistance refers to long-term independent augmentation of a patient.
Achieving this goal would help unshackle the time and effort commitment of therapists to
care for more impaired children. This can be accomplished through the development of a
host of assistive technologies that range from a general non-human play companion and
intelligent toys that continue reinforcing lessons through games, to physical
augmentations that help with mobility and performing daily routines. As these
technologies are meant to operate without prolonged supervision, there are a host of
ethical and safety issues that need to be addressed so that these implements do not
inadvertently impede or endanger their users.
As it was with earlier applications of Assistive Robotics in therapy sessions, life-long
assistance is applicable for both social and physical support. The most iconic
representation of life-long robotics assistance comes in the form of companions.
Companion robots can help entertain, practice communication and initiate active play with
the children, reinforcing social rehabilitation lessons between formal sessions with
31
therapists. Granted, there is no artificial counterpart that can supersede the company of
a human therapist or parent, but Assistive Robotics aim to augment the interactive
experience of the disabled child, picking up where therapy and parenting leaves off to
ensure continuous reinforced learning. A good example of a companion robot prototype
is the IROMEC, which is designed to adapt to the user’s changing play characteristics
and dynamically adjusts the tolerances of its interactive games while consistently holding
the child’s attention (Ferrari, Robins & Dautenhahn 2009). The assistive robot system
utilizing the Neuronics Katana also functions in a similar function, presenting disabled
children a companion that does not tire from repetitive and prolonged sessions of
previously restrictive cognitive play (Trevor, Howard & Kemp 2009). The same format of
Assistive Robotics can be applied for aiding the performance of daily routines which
demand better reach, strength and flexibility that the user is not able to provide by himself.
Artificial assistance for performing daily routines constitute yet another avenue for life-
long Assistive Robotics. The said routines are assumed to be activities that are vital
components for day to day living. For instance, there has been extensive research in the
field of augmented mobility for disabled users. While mostly catering to the elderly and
patients suffering from grievous injuries, these Assistive Technologies can also be utilized
to aid children with Cerebral Palsy (CP). However, not all technologies can be directly
applicable without some form of modification. A child with CP would struggle with
conventional user interfaces because of inadequate motor control, making tools such as
motorized wheelchairs a possible health hazard. With the addition of a control system
consisting of a proximity sensor suite and a touch-capable graphical user interface that is
easily operated, a powered wheelchair can be made intelligent enough for a disabled
child to operate (Montesano et al. 2010). The wheelchair would operate according to its
user’s control but have safety routines that intervene whenever it is about to perform a
dangerous maneuver. Another attempt to augment the powered wheelchair comes in the
form of the Collaborative Wheelchair Assistant (CWA). The control system has a built-in
guidance program that recognizes, then charts preprogrammed waypoints in addition to
sensor-based avoidance routines. The wheelchair can adhere to preprogrammed paths
whenever a control mistake is perceived. The course correction can also be made to
32
dampen instead of completely overwriting the user’s command, enabling the child to
slowly learn to grasp and perfect the control of the wheelchair (Zeng, Burdet & Teo 2009).
Augmentation of both motor control and mobility can be provided via development of
various powered exoskeletons. These machines are essentially Assistive Robots that are
attached to the human body to amplify the user’s actions. Most current exoskeleton
projects are developed for military applications, but some companies such as Cyberdyne
in Japan has been developing prototypes of lower extremity exoskeletons that help
restore mobility to aging citizens. Called the Hybrid Assistive Limb (HAL), the prototype
was available for rental in 2014. Newer exoskeletons also account for upper extremity
augmentation, helping with the lifting of boxes and reaching items. It would be a matter
of time before future exoskeletons are developed to accommodate children suffering from
cognitively impaired motor control.
Control schema is one area of big concern over Assistive Robotics that augment the
physical capabilities of cognitively impaired children. The balance between manual
control and programmed intervention needs to be struck well so that the implementation
does not impede the user’s actions, while ensuring that control mistakes due to poor
motor control are mitigated. The possibility and repercussions of programmed
interventions hindering intended actions have been studied by Demiris & Carlson (Demiris
2009). The study aimed to assess the capability of an Assistive Technology system that
dynamically balances between manual control and programmed intervention, presenting
a foundation for designing adaptive control that can one day strike that balance for safe
and comfortable use by children with CP and ASD.
The issue of children with cognitive disabilities preferring the presence of robots over
humans must also be considered. One study observed the empathy between normal-
functioning human beings and an anthropomorphic robot having differed designs. The
study found that normal humans tend to react more favorably towards robots that have
human-like features (Riek et al. 2009). A likely explanation for this finding is that normal-
functioning humans tend to adhere to societal bias, preferring beings with human-likeness
33
over foreign creatures. If true, then this may present some doubt over the affinity that
cognitively disabled children have towards non-humanoid robot companions. It was
assumed that the reason for their affinity was that non-humanoid robots do not present
the complex combinations of expressions that spark sensory overload. In this case, robots
can be used to train them to become comfortable with engaging the components for social
interaction, before slowly transitioning to human peers. However, their affinity could also
be a fixation to a non-human companion as a way of escaping contact with human peers.
This regressive outcome can be avoided if the robot’s mediatory role is enforced from the
onset of exposure – only utilizing it as an introductory tool for ushering interactions with
human companions.
The development of Assistive Technologies and Assistive Robotics for rehabilitating
cognitively disabled children is heavily influenced by ethical issues over safety and
psychological impacts. The effects of using these technologies need to be considered in
terms of the child, parent, caregivers and therapists, especially if they are intended for
long-term use. Adherence to ethical safeguards can be designed based on medical core
principles, which states that a system must uphold the autonomy, beneficence, justice
and non-maleficence of its intended aid (David Feil-seifer 2011). When applied to
Assistive Robotics, a robot should be created primarily to be a fairly-distributed aid, serve
the benefit of the child’s health, not breaching the user’s privacy and never becoming the
cause of harm. While there have been numerous attempts at drawing ethical design
guidelines, a definitive standard has yet to be globally accepted and adhered to.
2.5 IDENTIFYING COMPANION ROBOTS AND THEIR CHALLENGES
Most of the surveyed projects only employ periodic mechatronic aid from the robots
involved. The more iconic systems such as KASPAR and NAO are specifically developed
for sessional use – always with some form of oversight from a therapist or operator.
However, examples such as the IROMEC were intended to be interacted with and used
by the subject for long periods of time, without full-time observation by caregivers. These
robots are more prevalent in elderly care and augmentations for disabled patients.
34
One example of these standalone robots is an anthropomorphic mechatronic toy named
Paro. Built to resemble a plush baby seal, Paro was used in social interaction sessions
at eldercare homes. Its animated fins and audio output responds to voice and touch,
eliciting active play and brings about emotional attachment amongst its users (Kidd,
Taggart & Turkle 2006). While not able to directly assist its elderly users in daily routines,
Paro was instrumental in uplifting morale and inducing social interactions between
patients – yet another important factor in QOL. Paro stands out from the rest of the
previously discussed projects because its operational use extends far beyond individual
therapy sessions. It can accompany its users well throughout the day so long as its
internal batteries are able to sustain its actuators and automated audio feedback. It was
also designed to be plush, so its users are unlikely to be hurt by it even without a caregiver
around.
Other examples such as the ASTROMOBILE platform (Cavallo et al. 2013) and Matilda
(Khosla, Chu & Nguyen 2013) provide more direct assistance via voice recognition. These
server robots can initiate audio-visual calls, handle reminders and call for caregiver
attention in addition of being a constant physical presence to accompany elderly patients.
With suitable programming, robots like Matilda can facilitate conversations by suggesting
topics to groups of patients. These machines can be considered as close to commercial
companion robots as currently possible. However, because they are intended to be
purpose-built automated tools for nursing homes, their construction is boldly industrial,
lacking the anthropomorphic and softer qualities of safer robots such as the IROMEC and
Paro. Also, these machines are not intended for operation by non-adult patients and
caregivers close at hand. Otherwise, these heavy-duty machines can cause bodily harm
if misused.
On the opposite end of the companion robot usability spectrum are systems that are
entirely purpose-built for long-term dedicated aid in performing daily routines on behalf of
disabled users (Tee, Lau & Siswoyo Jo 2014). A chief example is the Mechatronic
Assistive Technology System (MATS) which looks like an industrial assembly robot, but
35
with actuated attachment points on both ends (Figure 2-11). MATS is a multipurpose
manipulator robot specially designed for attaching itself on wheelchairs and several rail-
type attachment points installed throughout the living environment. This helps partially
immobile patients to climb stairs, reach higher areas and grasping objects typically out of
reach (Balaguer et al. 2006). Other similar purpose-built machines that accompany
disabled people include intelligent escorting wheelchairs, smart walking sticks and a host
of augmented implements (Tee, Lau & Siswoyo 2014).
Figure 2-11: The MATS robot (Balaguer & Gimenez 2006).
Overviewing the myriad of assistive robot projects, a conclusion can be drawn for
describing an assistive companion robot’s general attributes:
a) The ability to operate autonomously for the most part, without the need of a full-
time observer.
b) Safe for unattended interaction by its intended user.
c) Able to operate for periods longer than most therapeutic sessions.
d) Sufficiently maneuverable for circumventing the environment.
e) Maintain consistent proximity between itself and the primary user.
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However, considering the polarized themes and variation in form factor between all the
discussed projects, it is not difficult to see the issues that prevent widespread
commercialization or mass-production or companion robots presently. The most glaring
issue is the specific purpose-built nature of each robot. The prototypes encountered thus
far are each developed for individual assistive goals varying from mediating social
interactions between human attendees, playing therapy-reinforcing games, participating
in active play, facilitating communication services, directly extending physical reach and
more. Some implementations require a large mobile platform that is unwieldy in confined
indoor spaces and presents physical hazard when used unattended.
Combined with the fact that robots are inherently complex mechatronic systems,
developing a multifunction companion robot platform becomes a monumental task.
Environmental factors such as terrain, use in indoor versus outdoor settings, obstacle
classifications and level of mobility must be considered. Again, accommodating these
issues in addition to the formfactor requirements impede upon the design effort for user
safety and human factors.
Finally, there is the issue of aesthetic design that impacts the user’s acceptance over
prolonged use. Extreme industrial builds such as the initial prototype MATS manipulator
and mechanized wheelchairs appear both imposing and unapproachable by less-
informed patients and younger children. However, synthetic reproduction of natural
entities such as lifelike androids and models of little children spark a sense of
apprehension in people of all ages due the effect of the Uncanny Valley. Thus, a balance
between both ends of the spectrum must be achieved before a machine may qualify as a
companion robot.
2.6 PROPOSED INDOOR COMPANION ROBOT PLANNING TEMPLATE
The outcome of the literature survey was the realization that a general-purpose
companion robot is difficult to implement. The reasons for this finding are that the platform
must have provisions for a myriad of specific use, sufficiently maneuverable in the
37
intended operating environment, aesthetically designed for optimal user acceptance while
most importantly, not become a source of injury by itself. This research recognizes that it
not feasible to strive towards an all-purpose build, so the scope of possible improvements
to companion robots are narrowed down to the following:
a) Intended for operation in entirely indoor environments.
b) Limited to operation within a living room setting and its common contents.
c) Primarily applied for injury prevention in cognitively disabled children with provision
for other functionalities.
d) Sized for use with children and other users of similar stature.
With these focused scope attributes, a build template can be formalized as the baseline
to help guide how a companion robot is designed and constructed. Each of the explored
challenges can be dealt with using the principles surrounding this template (shown in
Figure 2-12). First proposed in 2014, this template was originally intended for initial
inception of injury prevention companion robots targeting cognitively disabled children
(Tee, Lau, Siswoyo & Then 2015). The prototype was to be created under as minimal
budget and development time as possible while still providing ample space for fitting
interchangeable equipment for facilitating different study requirements.
38
Figure 2-12: Companion robot Planning Template intended for use with cognitively disabled
children.
39
Foremost, the template chiefly employs an Object-Oriented Approach to organize how
companion robot implementations are managed, arraying them as encapsulated
hardware and software modules. The key to enabling rapid prototyping is by exploiting
available technologies in the form of Commercial Off-the-Shelf (COTS) products.
Hardware modules range from enveloped sensors, actuators, structural build and Human
Interface Devices, each enveloped in a self-sustaining package that can be portable
between robot projects according to requirements. Meanwhile, software modules
champion the encapsulation of programmable functionalities, easing the process of
swapping routines according the dynamic therapeutic or monitoring needs. The Object-
Oriented Approach is fully involved throughout the software development process.
An overarching design language whereby emphasis on COTS components, likeable
aesthetics and safety concerns should permeate throughout the entire template’s
application when used to plan the concept of a companion robot.
2.7 CASE STUDY: CARMI
In the attempt to better understand the nuances and challenges of a multi-purpose injury-
preventing companion robot, an example prototype was developed using the proposed
Robot Planning Template in Figure 2-12 as a separate auxiliary project. The prototype is
intended to be a hands-on example accompanying literature review findings as well as
used as a testbed for this research to develop a navigation solution. The summary of its
inception and development details can be found at the beginning of Chapter 4.
Dubbed CARMI, the Companion Robot Avatar for Mitigation of Injuries operates similarly
to telepresence robots such as the Double (Double Robotics 2014), but was supposed to
autonomously follow a child from a distance while continuously monitoring her for possibly
dangerous actions. If such an action is detected, a caregiver is alerted who can then start
a video call via the robot to survey the situation and address the child. This was to fulfil
the requirements of a Robot-Based Injury Prevention Strategy that was based on the
triadic interaction model (Colton, Ricks & Goodrich 2009). Figure 2-13 shows the initial
40
CARMI prototype as it was intended for vision-based autonomous detection of possibly
injurious activities, while serving as an on-demand telepresence robot for facilitating
audio-visual calls to a mobile device on the same Local Area Network.
Figure 2-13: An early prototype of the CARMI robot as the planning template's proof of concept.
The Robot-Based Injury Prevention Strategy (Figure 2-14) requires the active
involvement of the caregiver who is free to handle their own daily routines while the robot
watches over the child autonomously. In the event of an alert, it is the caregiver’s
responsibility to survey the situation and speak to the child as a means of intervening
before the injury occurs. Within five months, the research effort was successful at
planning and implementing a roughly working prototype that demonstrated rudimentary
position tracking and vision-based gesture recognition.
The main structure of the CARMI prototype consists of an indoor mobile robot
development platform equipped with a variable drive system and basic bump switches.
Rapid implementation of multiple redundant proximity sensors and an actuated turn table
were carried out, entirely relying on the Arduino microcontroller development kit. A
41
Microsoft Kinect sensor unit was adapted into the activity tracking system, utilizing the
software development kit’s gesture recognition facilities as a means of identifying
matching body motion profiles of possibly injurious activities such as jumping, falling,
punching and pushing. The early prototype alongside its concept were showcased during
PECIPTA 2015, garnering positive responses from the public and receiving the bronze
award (Borneo Post Online 2016).
Figure 2-14: The Robot-Based Injury Prevention Strategy.
Its success during the research expo has demonstrated that the template’s effectiveness
at rapid planning as well as implementation of most of the subsystems required for
42
fulfilling CARMI’s role within the Robot-Based Injury Prevention Strategy. The robot
platform was commercially acquired, having been built with mounting provisions for a
variety of sensors and components needed for most general-purpose operations in indoor
environments. The variable drives were included with the platform, so there was little
effort needed for calibrating mobility. The turntable had to be custom built, but consists of
laser-cut acrylic sheets, servo drive system and supplied power using locally bought parts.
Finally, the Kinect unit was easily acquired because it existed as a common product for
adding vision-based motion capture to a popular video game console platform called
Microsoft Xbox (Microsoft 2014).
While most of the necessary hardware components were easily acquired commercially,
there are some aspects of the companion robot that must be built from the ground up.
These are subsystems that operate specifically according to how the robot was built and
its operational conditions. From examining the performance of the initial CARMI
conceptual prototype, the following findings were drawn:
a) The human activity tracking system frequently lost track of the user, either because
the primary target moved outside its Field of View (FOV) too quickly, or the
crowded FOV caused the sensor to switch targets.
b) Due to limited orientation data from the Kinect-based tracking system, the turntable
mechanism was not able maintain the primary target within the sensor’s FOV for
continuous tracking.
c) The rudimentary navigation system was only able to steer the robot towards the
current target by wall-following algorithms using immediate proximity data of the
immediate surroundings. This means that CARMI often begins circumventing
furniture in the wrong general direction, leading it away from the user.
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d) The combined inability to maintain visual lock on the user and the unreliable
rudimentary obstacle avoidance method resulted in the robot constantly falling
behind during escort routines.
These findings present critical flaws that prevent the current CARMI build from being an
effective companion robot. Since most indoor companion robot projects involve a similar
form factor and indoor escort requirements, it can be assumed that they also share similar
problems with their implementation efforts. Thus, the findings are distilled into two major
challenges:
a) A more reliable human activity tracking system is needed, preferable one that can
also be implemented using COTS components.
b) An indoor navigation method or strategy that goes beyond simply considering the
immediate surroundings. This can help route the robot so that it maintains a set
escort distance while circumventing obstacles with the least effort required.
The next objective of this research is to explore available options to meet these
challenges under the same COTS and design language requirements as defined by the
Companion Robot Planning Template. For continued application and testing of proposed
solutions to these challenges, the CARMI robot is used as an ideal case study for the
remainder of the research.
2.8 CHAPTER SUMMARY
Over the years, robots have come a long way to assert itself as a viable assistive
technology in the service of the elderly, disabled patients and children with developmental
disabilities. Its applications span a broad stroke from direct augmentation of therapeutic
functions to robot-mediated social sessions, active play and long-term QOL assistance.
There had been an unclear divide between which applications are deemed companion
robots from amongst the multitude of assistive robotic projects.
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This literature survey of assistive robotics state-of-the-art had helped form an
understanding of a set of characteristics exclusive to companion robots. The robot must
be able to maneuver within the confines of typical indoor living spaces, operate for periods
longer than therapy sessions, carry out its duties autonomously without a full-time
operator, consistently maintains a suitable proximity between itself and the user while
ensuring that it doesn’t present any form of harm during runtime.
This research narrowed its scope upon strictly indoor and living room equivalent
environments, exclusive use by cognitively disabled children and focuses on injury
prevention as its paramount goal. The first step in attempting to suggest an improvement
upon companion robots within this scope definition was to create a roadmap to help plan
and guide their development. This Companion Robot Planning Template achieves this by
setting a few principles in place: Object Oriented Approach in planning structure and
software design, encapsulated solutions for each requirement made entirely from
standalone COTS components and a design language that is concerned with human
factors, safety and user acceptance.
The effectiveness of this template was gauged by applying it in creating CARMI, a
companion robot that acts as an integral part in a Robot-Based Injury Prevention Strategy.
Its ability to visually identify a target user’s actions and scans them for any matching
dangerous profiles, had earned it public acceptance during PECIPTA 2015.
Despite positive feedback, the conceptual experiment revealed several flaws that could
not be mitigated by the planning template alone. Chief among these is the frequency of
the system losing sight of the intended user. On its own, the commercially available
human tracking device could not adapt to changing lighting conditions, crowds and erratic
user movement. In addition, the robot platform could not maintain consistent escort
distances with the user as its rudimentary pathfinding system was not enough in finding
a least impeded route around obstacles between itself and the target. It was assumed
45
that other similar companion robot projects suffer similar problems in attempting to create
a multi-purpose companion robot platform for indoor use.
Despite introduction of a companion robot planning template and a development strategy
based on COTS and OOA, its experimental implementation’s findings were distilled into
two remaining challenges that cannot be easily overcome from existing homogenous
solutions: a need for an effective human tracking, and a robust indoor robot navigation
method. The solutions for both challenges combine into a novel autonomous human
following system that is still scalable to robots built according to the planning template.
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Chapter 3: INDOOR ROBOT NAVIGATION AND HUMAN FOLLOWING
The previous literature survey identified two major challenges faced by developers of
indoor companion robots meant for affordable multipurpose use. The first is a need for a
navigation method for traversing the lay of indoor environments while avoiding dynamic
obstacles. The second is a reliable human tracking system that can help lock onto the
intended target user to be followed by the robot. Unlike the remainder of the robot
characteristics outlined in the companion robot planning template in Chapter 2, these two
challenges could not be solved via homogenous Commercial Off-The-Shelf (COTS)
solutions because current effective solutions had to be tailored according to the
combination of robot platform (hardware and software) characteristics and operating
variables introduced by different environments and specific application.
This chapter continues to expound on the findings by elaborating on the specific nuances
of these two challenges, through surveying existing works that encounter similar
predicaments. After which, this research proceeded to explore current technologies that
may be useful for formulating a solution to both challenges.
3.1 CHALLENGE 1: INDOOR NAVIGATION
Indoor autonomous navigation for mobile robots persists as a seasoned and wide area
for study today. The scope of challenges in this field is matched only by the multitude of
attempts at implementing possible solutions. However, exclusive coverage navigation
problems in general is not commonly researched and published. Most related research
work present novel methods to circumvent specific maneuvers or sensory problems.
While this helps contribute to the pool of possible technological solutions, there is little
guidance in how they can be used for solving combinations of problems faced by an
autonomous companion robot in an indoor human-populated environment. At the very
least, almost every published works agree that robotic pathfinding and obstacle
avoidance is a complex problem (Budiharto, Purwanto & Jazidie 2011) acerbated by the
47
fact that their intended working environment is constantly affected by an amalgamation
of dynamic motion, clutter and sensor noise. After reviewing a collection of related robot
wayfinding research works, common themes of navigation problems could be found.
These are grouped into the following categories:
a) Location-Specific issues
b) Autonomous wayfinding dilemmas
c) Sensory and actuation hardware dilemmas
The following subsections help summarize the literature survey findings for each of these
categories, as well as an overview of possible navigation methods that can offer hints to
a workable solution for this research effort.
3.1.1 Location-Specific Issues
One of the most common issues mentioned in robot navigation research work is the
structural and logistical complexities involved when dealing with real-world human-
populated environments. At the macro level, the human indoor environment can consist
of multiple levels, each containing a combination of rooms with different obstacle layouts
depending on what they are purposed for. Each level is connected via stairwells and
structural cavities, while rooms lead from one to another via doors and windows of varying
dimensions. Moving between these obstacles, rooms, levels and orifices is extremely
challenging for ground-based mobile robots as well as airborne ones (Shen, Michael &
Kumar 2013).
Observing each room, static arrangements of furniture and dynamic obstacles such as
toys and people create an organic environment that constantly morphs operating
variables. Passages between obstacles close while new ones are formed, ambulation
over uneven surfaces cause oscillating sensor readings and noise leading to unreliable
perception of the immediate vicinity are to be expected (Budiharto, Purwanto & Jazidie
48
2011). This shows that even moving through a single room can have varying degrees of
difficulty, before scaling up when the robot attempts to traverse across rooms and levels.
Traditional pathfinding is done with knowledge of the present topography in hand, referred
to as “a priori”. While parts of the environment can be modeled, it is impossible to maintain
in the real-world effectively for long because those aspects would undergo changes over
time (Thrun 1998). Due to the dynamically changing operating variables in human
populated environments, the previously complete a priori knowledge becomes rapidly
outdated and invalid (Sgorbissa & Zaccaria 2012).
The other method is to plot a course towards a goal position via reflex behavior, sensing
the immediate surroundings and finding an immediate opening when it presents itself.
This may be workable, but is prone to deviating from known paths and getting into
deadlock situations (Sgorbissa & Zaccaria 2012).
Even the basic characteristics of the environment such as presence of sunlight, indoor
lighting zones, sound absorption in obstacles, reflective surfaces, and uneven surface of
the floor can cause an unknown distribution of noise (Thrun 1998) and sensor misreads.
Static calibration values are not viable since the dynamic environmental characteristic
changes will call for a shift in sensory threshold. Some form of sensor redundancy to
combat noise, or a machine-learning solution to actively adjust the sensor thresholds in
real-time is needed.
3.1.2 Autonomous Wayfinding Dilemmas
There are two ways of approaching the classical problems of pathfinding and obstacle
avoidance: global and local methods (Mohammad Khansari-Zadeh & Billard 2012).
Global methods cover algorithms that best represent the iconic area of computational
search optimizations. It requires a complete or near complete a priori knowledge of the
operating environment. This top-down snapshot of the environment is then parsed using
any select flavor of search algorithms such as Brute Force, Breadth-First-Search, A*,
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Bayesian Search or the like, finding a best-fit or shortest path between the robot and a
goal if at least one exists. Most global methods involve some form of search optimization,
Fuzzy Logic or Neural Network to adapt for incrementally faster path searches. One
popular method represents the operating environment as a potential field where obstacles
are sources of repulsive force (Khatib 1986). Only the goal position exists as an attractive
force. Thus, the layout of the potential field presents a workable path as a flow of attraction
shaped by the repulsive forces around them.
Unfortunately, global wayfinding is difficult to implement due to a multitude of reasons.
Firstly, a priori knowledge is hard to acquire without the use of expensive scanning
hardware (Budiharto, Purwanto & Jazidie 2011) such as Light Detection and Ranging
(LIDAR) lasers, and is computationally intensive to process (Lapierre & Zapata 2012;
Mohammad Khansari-Zadeh & Billard 2012). Also, the previous sub-section had also
discussed how a priori knowledge alone will not be effective in a dynamically changing
environment that companion robots are often expected to operate in.
The local method is the other extreme end of the wayfinding spectrum. All environmental
perceptions are limited to the immediate surroundings of the robot. This immediately
garners the advantage over global methods in terms of usable sensor selections. This
can range from rudimentary InfraRed and Ultrasonic ranging modules to laser range
finders, and even LIDAR. Since the coverage of the snapshot is much smaller than a
priori floorplans, wayfinding between obstacles towards the goal position is carried out
using simple search algorithms that aim for the next available passageways between
obstructions. Other local methods exist including Bug’s Algorithm, Vector Field Histogram
and Curvature-Velocity method that help make local pathfinding decisions in the event of
dynamic changes to the immediate vicinity, called perturbations (Mohammad Khansari-
Zadeh & Billard 2012). This reflex behavior (Lapierre & Zapata 2012) in path selection is
often modeled after insects and biological creatures exploring the environment using only
current knowledge of their immediate surroundings as opposed to preplanning using
global methods.
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In return for lower computational overhead and flexible sensor selections, local wayfinding
methods do suffer several severe disadvantages. First, it lacks any internal source of
localization (Shen, Michael & Kumar 2013). Localization is of paramount importance to
autonomous robots operating in both indoor and outdoor environments. This allows it to
identify its current location and orientation so that it can determine the heading towards
its goal position. Outdoor systems have the benefit of the satellite network powered
Global Positioning System (GPS) but this does not work indoors. Under overhead cover,
GPS-centric robots switch to Inertia Measurement Units (IMU) that measure forces from
robot motions to approximate its deviation since the last point of GPS contact. This
method, called Dead Reckoning, suffers from inaccuracies due to incrementing errors
over time. Fully indoor systems must rely on alternate ways to identify their location and
heading, with limited degrees of success. Outside of installing localization beacons
throughout the environment, there are currently no onboard-only approach to enabling
indoor robots to self-localize.
Secondly, the local approach suffers from not having access to the wider scope of
topography granted by global methods. This means that purely reflex choices of
wayfinding between obstacles will frequently lead to dead ends and longer routes. Also,
there is no prior knowledge whether a viable path between the robot and the goal is even
available. In worst cases, the robot will iterate in an infinite loop around its general vicinity
as there is no available path. Local adaptations of the Potential Field method or other
biologically inspired algorithms may face the dilemma of sensory plateaus where pursuing
the choice paths leads the robot to circle within the same area not recognizing that it is
another form of deadlock condition. This situation has been commonly referred to as the
local minima problem (Budiharto, Purwanto & Jazidie 2011; Lapierre & Zapata 2012;
Sgorbissa & Zaccaria 2012).
Similar to the outcome on Location-Specific issues, it is necessary for an autonomous
robot’s wayfinding system to possess the ability to adapt to perturbations in its operating
environment (Mohammad Khansari-Zadeh & Billard 2012). Given that both global and
local methods have their disadvantages in terms of coverage scope, computational
51
requirements and differing sensor needs, perhaps the solution to this comes in the form
of a combination between both.
On the other hand, there are also issues exclusively related to obstacle detection and
avoidance – an integral part of wayfinding. At the very base of autonomous behavior, a
mobile robot should be able to sense an impending obstacle and attempt to avoid it. Early
works involve using simple bump switches or proximity sensors to trigger an action that
simulates reversing away from a wall or object. Today, obstacle avoidance is usually
found as an important part of any robot navigation system, dodging obstructions while
making its way towards a goal position. The effectiveness of an avoidance system heavily
depends on the quality of feedback from the sensors and how it perceives them. Most
obstacle avoidance algorithms assume obstructions as entities possessing circular
profiles for ease of representation in simulations (Alsaab & Bicker 2014). However,
obstacles in the real-world are not always circular. Various furniture such as sofas and
shelves have possibly irregular rectangular shapes with sharp protruding corners that will
collide with a robot if the programmed maneuvers exclusively registers them as circular.
In this case, a popular way to mitigate this problem is by relying on depth data rather than
just an array of ranging modules. Depth data offers more resolution to adequately profile
the shape of obstructions, thus making a detailed maneuver around it possible after
building a collision cone (Alsaab & Bicker 2014). However, depth data requires more
computing power to process and may suffer from input latency compared to reading
bursts of data from ranging sensor arrays.
3.1.3 Sensory and Actuation Hardware Dilemmas
Hardware is yet another avenue for developmental issues when it comes to creating
indoor autonomous robots. As most of the requirements for multifunction indoor
companion robots can be met using COTS components, this section focuses on hardware
dilemmas specific to sensors and actuator implementations for robot navigation and
obstacle avoidance.
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One of the most common problems with sensors for navigation is the inherent hardware
limitations that each possess. For instance, vision-based sensors such as RGB cameras
and depth cameras suffer from limited Field-of-View (FOV), lens occlusions, susceptibility
to changing lighting conditions, and input latency (Budiharto, Purwanto & Jazidie 2011).
Hardware centering on optical sensors have limited FOV because of the way light-
sensitive modules are arranged in standard manufactured packages. Limited FOV is a
major problem especially when the robot must keep track of human targets while being
in motion. The combined erratic motions of human subjects and the opposing movements
of the robot’s platform will cause the target to be truncated or occluded frequently (Choi,
Pantofaru & Savarese 2011).
This FOV can be modified by selecting different degrees of wide-angle lenses. The
drawback of choosing a lens that offers the widest viewing angle is that the image will
become distorted. In robot navigation, some extent of image distortion may be acceptable
if the image is usable for developing collision cones and for visually identifying targets. A
balance must be struck between having an optical sensor that has the widest viewing
angle possible, while maintaining acceptable clarity of the objects that it is intended to
view. However, there has yet to be an optical sensor suite that can rival the consistent
effectiveness of the human eye in both adjustable focus width and image clarity. The
problem of limited FOV also means that the robot will have to roam the environment more
actively than a typical human being would, just so that it can profile a room or search for
a target for human following (Thrun 1998).
Another hardware dilemma is the lack of context garnered from the raw data fed from
current sensors (Thrun 1998). Low level RGB and depth cameras could only return raw
frames that need to be parsed manually before a captured object could be identified and
treated as an entity in navigation algorithms. Ranging sensor arrays consisting of
ultrasonic modules or IR ranging LEDs return raw converted analog-to-digital values that
must be calibrated before they indicate proximity of obstacles correctly. This additional
layer of operations add overhead onto autonomous robots, especially companion variants
53
that have to handle all processing on-board in a standalone unit (Shen, Michael & Kumar
2013). Fortunately, there are COTS sensor modules available today that come as a
combination of camera and image processing unit. One successful example is the
CMUcam (Carnegie Mellon University 2018), which can be programmed to modulate the
image and perform object detection on its own, sending high-level contextual data for the
robot host’s use in navigation. There are also depth camera suites that come bundled
with software developer kits that handle the necessary (and proprietary) optimizations in
human detection and tracking on behalf of the robot (Microsoft 2014; ASUSTeK Computer
Inc. 2014; Creative Technology Ltd. 2014). While there are more areas in sensor
hardware technology that is yet to be upgraded with contextual data output, the steps in
the right direction has already been taken. These existing systems can be considered for
formulating the sensory hardware component of the solution for this research.
Another avenue of robot hardware dilemma is the issue of actuation. There is a multitude
of research in different ways a mobile robot can ambulate. These range from basic
wheeled or tracked ground vehicles and bipedal humanoid platforms to Unmanned
Amphibious Vehicles and multirotor copter drones. While it is tempting to adopt a
humanoid (A. Jung Moon 2014) platform for the more degrees of freedom, this available
range of motions scales proportionally to development costs. Most robot navigation
projects are planted upon rudimentary dual-drive robot platforms such as Microsoft
Robotics Developer Studio Reference Platform (Microsoft 2012b). While these platforms
can perform basic motions to move around a room, it cannot simply perform sidestep
motions or tilt its body in case it is stuck in a confined place (Shen, Michael & Kumar
2013). One study specifically identifies the problem of under-actuation, where a robot has
less degrees of freedom than necessary to perform maneuvers its intended use requires
(Lapierre & Zapata 2012). On the other hand, over-actuation (oversaturation of actuators)
could result in unintended coupling of both longitudinal and rotational velocities in motion.
Both conditions are undesired, so enough thought must be put into the design of actuation
during robot implementation.
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Throughout the literature survey, it can be observed that a majority of robot navigation
and obstacle avoidance projects base their performance findings on successful simulation
runs. While this is the primary method of validating their system’s functionalities, most
companion robots are intended for operation under long periods of use. It is expected that
a robot be fully utilized in a typical session for at least over the duration of the patient’s
waking hours (typically 12 hours a day). This feat is difficult to accomplish because of the
presence of sensor drift and error accumulation in the robot controller over time
(Sgorbissa & Zaccaria 2012). It is not rare for an autonomous robot to experience
performance degradation due to compounded errors from multiple sources, as was
explored in the previous discussions. In all cases, such degradation becomes severe over
time, and can be mitigated simply by a watchdog timer that periodically resets the system.
The downside of doing this is that the reset procedure requires a calibration step that may
not be possible without the assistance of an operator. Other hands-off methods of allaying
the issue include using machine learning or adaptive integral algorithms to offset the
sensor drift by studying the robot’s real-time behavior.
Much of the robot hardware issues stem from the constraints of having all sensor and
mobility systems situated in a standalone body (Shen, Michael & Kumar 2013). The
reason for such restriction is because companion robots are meant to be portable and
applicable in whichever location the intended users are in. Thus, all navigation and human
following operations must be carried out by relying solely on compact, self-contained
computation, sensors suite and actuated platforms while being unencumbered enough to
maneuver around domestic indoor environment conditions.
3.1.4 Overview of Possible Autonomous Navigation Solutions
It is paramount to consider the nuances of autonomous wayfinding in an indoor human-
populated environment when attempting to format the solutions brainstorming process.
The operating environment is assumed to be cluttered with a collection of walls (acting as
borders), furniture, and miscellaneous objects of various sizes. To limit the scope of the
research, the intended companion robot is confined within a single room. There may be
55
more than a single person in the room at any one time, but there will always be one
primary target. Hence, an ideal navigation system is one that can help a suitably
maneuverable robot traverse between these obstructions (furniture, objects and people)
to stay within a set proximity from the primary target. The obstructions are expected to
move dynamically, fitting the description of environmental perturbations (Mohammad
Khansari-Zadeh & Billard 2012).
The issue of maneuvering between obstructions can be tackled using either local or global
methods. Global methods are predominantly more effective in charting shortest paths
given that an a priori snapshot of the environment is made available to the robot. This can
be done using rotating imaging sensors such as LIDAR, but this equipment can be
expensive and unwieldy on a small robot frame. A popular group of global pathfinding
systems are based on the Potential Field Method (Khatib 1986), which assigns repulsive
forces to perceived obstructions, and using the flow of attractive forces between the robot
entity and a goal position as the travel path. However, global methods like these are
susceptible to the local minima problem (Lapierre & Zapata 2012), and environmental
perturbations will invalidate snapshots and generated paths unless there is little latency
between renewed a priori datasets (which in turn, requires more expensive hardware and
computational power).
Local methods may be a better choice in dealing with dynamically shifting environmental
conditions. The simplest version is to adopt a purely reflex behavior: sampling the
immediate environment with a proximity or ranging sensors array, then selecting a
direction that is least obstructed. This will most likely head to a dead end or seemingly
random direction that will lead away from the primary target. One study proposed
improving reflex obstacle avoidance by adding a Bayes Estimator to weigh the probability
of a selected path against presence of noise and sensor glitches (Budiharto, Purwanto &
Jazidie 2011). However, the major disadvantage of using a fully local method is the limited
topographic knowledge for localization making it necessary for the robot to perform more
vigorous roaming (Thrun 1998).
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A possible alternative between the global-local method debate is to adopt a global-style
potential field paradigm and apply it in a local method fashion. The Virtual Force
Histogram (VFH) assigns magnitudes of repulsion to data feed from ranging sensor
arrays. This way, a direction with least repulsion will indicate a possible maneuver path
between the robot and the goal. However, this does not allay the inability of local
wayfinding to consider mid-to-long range topographies. Also, applications such as VFH
are very affected by the problem of relying on static sensor thresholding in an environment
full of perturbations (Budiharto, Purwanto & Jazidie 2011).
Partial knowledge of the topography or localization may help in reducing the margin of
error inherent between local and global wayfinding. A standalone version of area imaging
can be accomplished via Simultaneous Localization and Mapping (SLAM) (Atia et al.
2015). LIDAR is used to constantly scan the immediate visible surroundings of the robot,
which provides a partial map that can be applied with search algorithms to chart a best-
fit path. Alternatively, Radio-Frequency (RF) emitting beacons can be mounted
throughout the environment to act as waypoints (Atia et al. 2015). Once a robot is trained
to self-localize via sampling Received Signal Strength (RSS), it can be coupled with a
rudimentary obstacle avoidance routine while being somewhat aware of the correct
heading that will lead it towards the primary target. The downside of opting for partial
knowledge is the high cost of imaging equipment or embedding the environment with
active beacons, leveraged against wayfinding reliability somewhat between fully global
and local methods.
Focusing on obstacle avoidance, it is important that the issue of representing obstruction
profiles be carried out thoroughly but within conservative computational costs. One study
suggested the use of depth maps which provides higher resolution to better define visible
obstructions as collision cones to maneuver around. While formatting raw depth maps is
computationally intensive at high frame rates, there exists motion tracking cameras and
software development kits such as Microsoft Kinect (Mankoff & Russo 2013) that can
output high-level body tracking and processed low-level depth frames simultaneously.
Selecting this option affords the possibility of performing both robot navigation and human
57
tracking at the same time using the same hardware. The processed depth map can be
used to extract relative shapes using significantly less computing resources (Alsaab &
Bicker 2014).
The issue of under or over-actuation is beyond the scope of this research. It involves
detailed study of structural concerns, drive systems and traction design that balances
between development cost and the appropriate degrees of freedom to circumnavigate
around the living environment. For this research, a standard dual-drive holonomic robot
package will be used as the baseline platform for indoor mobility. This also allows this
research to gauge performance between an implemented solution against existing
projects that adopt the same platform.
Finally, there are a multitude of existing robot navigation works that suggest various
degrees of integration between technologies and techniques. Mechatronics employ
combinations of redundant components to sample or produce an effect using different
hardware so that the drawbacks of each component is overcome by the other. One
example study proposed the use of accurate but complex grid-based maps but organized
and accessed using a topographic-inspired overlay (Thrun 1998). Another study
attempted to breach the gap between global and local wayfinding methods. This was
done by first establishing a priori mapping to define the primary travel paths throughout a
hall. Then when the robot encounters an obstruction, it switches to an offline obstacle
avoidance mode which deviates from its original path until it has passed the obstruction
(Sgorbissa & Zaccaria 2012). One of the most interesting integration attempts was
presented as a fusion between an environment embedded with RF beacons and onboard
LIDAR for combined indoor self-localization (Atia et al. 2015). Hardware fusion between
human tracking systems are also possible, such as the combination of motion capture
and face-voice recognition (Hu et al. 2010) for overcoming the problem of subject
occlusion and clipping (Choi, Pantofaru & Savarese 2011). Perhaps it is entirely possible
to perform indoor robot wayfinding simultaneously with human following using the same
hardware via tracking system fusion.
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3.2 CHALLENGE 2: HUMAN TRACKING
The second challenge identified in Chapter 2 points to the need for a more reliable method
to detect, identify and track a specific human target as the intended user for the
companion robot to follow. Combined with autonomous navigation, a companion robot
can perform its primary function of human following. This can be carried out by using
motion tracking systems that were fundamentally developed for studying how a body
naturally moves. They achieve this by identifying the individual limbs and joints that a
body consists of. Using marker suits, worn beacons or a standalone software shape-
identifier algorithm, the characteristic articulation and motion profile can be captured
directly and rapidly processed for rigging animations or physically reproduced using
Mechatronics.
Assistive Technologies today has the benefit of consumer access to sophisticated motion
capture devices that used to be astronomically costly and only employed for industrial
movie making and ergonomics testing. Gaming and Human Interface Device products
such as Microsoft Kinect (Microsoft 2014), Asus Xtion controller (ASUSTeK Computer Inc.
2014), and Intel RealSense-based devices (Creative Technology Ltd. 2014) are example
systems that can be easily acquired and retrofitted for tracking human bodies and their
positions. These products come packaged with complete proprietary motion tracking
capabilities encapsulated within software development kits (SDK) which can be
harnessed for a variety of applications that depend on tracking the human body and its
gestures.
Having access to these technologies at hand may invite the assumption that human
tracking can be easily done via COTS. However, this is untrue because the field of
machine vision has continued to struggle with the complexities and nuances of this
problem despite advances in both related hardware and software. The difficulties faced
here are similar in magnitude as indoor navigation problems as there are seemingly
infinite environmental variables which dynamically affect the performance of tracking
systems.
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One study discussed how a single human tracking hardware solution is not effective
because it is limited by hardware specifications, how human subjects move erratically as
well as contending with shifting environmental conditions (Choi, Pantofaru & Savarese
2011). Another study suggested that a more viable alternative was to complement motion
tracking devices with other systems that may assist in reducing false detections, multiple
decoys and cluttered environments. It proposed a joint subject tracking system that
separately recognizes the person’s face, tracks the upper body profile, and confirms the
position of the subject using an omnidirectional microphone array (Hu et al. 2010). The
findings revealed varying degrees of success at overcoming the FOV and subject
occlusion. It is possible that instead of combining multiples of same-natured tracking
systems as redundancy, more success can be found by fusing different human tracking
methods (Tee, Lau, Siswoyo Jo & Sian Lun 2016).
This research pursues this line of inquiry by continued literature survey into various facets
of human tracking technology research, grouped into the following categories:
a) Self-Localization
b) Body Tracking
c) Biomonitoring
This subsection also covers some cross-category fusion examples as applied in some
Assistive Technology projects.
3.2.2 Self-Localization
Localization is a distinct icon of tracking technologies commonly paraded in Sci-Fi and
industrial robot use. Applications in areas such as security, healthcare, construction and
safety have always valued the ability to locate a target without manual human labor,
however the methods to make this happen are rarely simple to implement. The most basic
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of these methods is localization via Line of Sight (LOS). For instance, an intruder must be
physically within the viewing angle and viewable distance threshold of a security guard
before he elicits the raising of alarms.
This method has since been augmented today via developments in optical sensor
technologies and computer vision processing algorithms that help format captured frames,
identify body profiles of potential trespassers, and send alert notifications to warn security
staff of the situation. Networks of cameras and sensors can work together to monitor the
location of workers in high risk workplaces to issue warnings that prevent anyone from
inadvertently entering danger zones.
Some technologies can even provide approximate indoor localization without the need
for LOS, such as Radio Frequency (RF). RF beacons can be installed throughout the
environment to broadcast their position-tagged signals to be picked up by onboard
receivers on mobile robots. These robots examine the tag and the Received Signal
Strength (RSS) to assess its distance from the signal’s source location. Using
triangulation, a robot could theoretically calculate its indoor position by examining three
or more received signals per cycle.
Localization can be carried out in one of two ways: via range-free or range-based methods
(Chen et al. 2008). Ranged-based systems work using fixed installation of nodes that are
programmed with their individual location information. The RF example discussed
recently is one example of a range-based system. The robot itself is considered as a blind
node, which is a node that lacks position data. It acquires this information by sampling
the broadcast signals from all contactable fixed nodes and attaches their RSS to their
addresses. Thus, the blind node’s position is calculated using the acquired signals via
algorithms such as Triangulation and Trilateration (Chen et al. 2011). Other related
studies explore possible improvements using alternate implementations of nodes and RF
technologies, or via algorithm optimizations.
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The range-free method examines the characteristics of mostly wireless sensor networks,
relying on relative distances between anchored and non-anchored nodes. These nodes
are not fixed with position data, but the system helps estimate the relative position of a
node within proximity of other nodes. In the case of the RF example, raw RSS feedback
from contacted nodes can be applied with Angle of Arrival or Time of Arrival calculations
to assess both the angle and estimated distance between nodes (Chen et al. 2008).
One study presented a unified approach that combines the best elements from both
range-based and range-free approaches to localization. It begins by performing location
estimation with ranged-based sampling of location data from all contactable nodes. Then,
it improves and refines that estimation using the range-free assessments of the wireless
connection characteristics between nodes (Quattrone, Kulik & Tanin 2015).
Outdoor localization is dominated by reliance on the Global Positioning System (GPS), a
satellite network that acts as ranged-based fixed nodes across the Earth. GPS-enabled
devices receive broadcast signals from at least four visible satellites and examines each
one for Time of Arrival. The period between transmission and receipt times for each signal
help indicate the distance between the node and the originating satellite. By repeating
this process with at least four sources, the node’s longitude and latitude on Earth can be
estimated. As the number of visible satellites increase, the accuracy of the estimated
position is further improved. Today, fitness trackers such as the Polar M400 (Polar Electro
2015) and Garmin Vivoactive (Garmin Ltd. 2015) products have built-in GPS modules
that help gather location history data. Almost every smartphone manufacturer including
Samsung, Apple, Xiaomi, and Nokia has similar GPS functionality integrated as standard
issue for their products, enabling consumer access to location-based services.
Unfortunately, there are some inherent limitations of GPS that needs to be addressed
when considering this technology for human tracking. The most glaring problem is that
GPS requires direct LOS with the receiver module for it to function. Various factors such
as cloud cover, weather conditions, and overhead structural coverage will adversely affect
the performance of this position tracking technology, resulting in miscalculations known
as GPS glitches, or total loss of service.
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There is a way to enable momentary GPS position tracking if the receiver is under
temporary indoor locations or the LOS is negatively affected by some reason. The
localization system can be complemented with an Inertial Measurement Unit (IMU) which
is a suite of accelerometers and gyroscopes that measures the changes in experienced
forces when the host is moving (Seo et al. 2012). These measured changes can be
translated into estimated vector deviations from the last known location pinpointed via
GPS, using a method called Dead Reckoning. The downside of this system is that the
measured values only provide a rough position estimation. The error in this method
accumulates over time, making the localization data rapidly decay while the target
remains outside of GPS reach.
One study attempted to rely solely on the IMU system without GPS for a car localization
system that is integrated with WiFi and GSM connectivity found in most smartphones.
Called Dejavu, it uses the same Dead Reckoning method to approximate the location of
the car as it travels along roads forming a multi-modal sensor database (Aly & Youssef
2013). The databases from all users are combined via crowd-ware to create a composite
representation of roads and potential choke points or potholes while helping the system
collect performance data to reduce drift errors resulting from prolonged Dead Reckoning.
Another major issue of GPS is that most receiver modules consume high amounts of
energy, hence were rarely included in a lot of wireless Internet of Things (IoT) applications.
A study presented an energy efficient localization method that combines GSM and WiFi
networks as an alternative to GPS (Oguejiofor et al. 2013). The method also incorporates
RSSI of signals from cell towers to improve its sensor database, thus resulting in over
347% improvement to battery performance over the use of GPS.
As reliable and commonly used GPS is, this technology is severely limited in indoor
environments that do not have viewable access to the sky. Hence, the area of indoor
localization experienced more technological diversity in the absence of an easily
accessible range-based node network. The two most predominant types of solutions
applied for indoor localization are RF-based and vision-based systems. Each type comes
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with its own advantages and disadvantages that must be addressed on a case-by-case
basis.
Continuing where the RF example left off, it should be noted that RF localization involves
active broadcasting of multiple nodes over long periods of time which translates to high
energy consumption. A study carried out at Carnegie Mellon University Qatar attempts to
undermine this disadvantage by tracking localized clusters rather than individual devices
(Neishaboori & Harras 2013). Instead of custom-programmed RF hardware, the research
relies on WiFi, which is a wireless networking technology that can be implemented using
consumer accessible infrastructure components based on IEEE802.11 and Bluetooth.
Instead of performing multiple RSS examinations and triangulation techniques for each
receiver node, the study proposes an umbrella approach to tagging nearby devices as a
single cluster. The standard RF localization process is only performed on a per-cluster
basis, significantly reducing total energy consumption. This method is most effective for
heavy-traffic areas where people tend to move in clusters, such as train stations and malls.
Another RF related issue is the need for calibrating RSS to distance estimations. This is
carried out as an online training phase where each node communicates with the tracked
receiver and records the results as corresponding to the physical gap between them.
Thus, every possible target position is recorded as a combination of RSS reads from
every node into what is called a Radio Map. However, this Radio Map can be invalidated
by dynamic presence of people, shifting obstructions and multiple receiver devices. One
study presented the idea of including Line of Sight (LOS) into defining the Radio Map,
thus reducing the effect of multipath signal distortions (Guo et al. 2014).
The other popular alternative is vision-based localization that rely on optical sensors. This
form of solution is more appropriate in the case of companion robots as they do not rely
on embedding the environment with wireless nodes. One of the most effective forms of
vision-based localization is carried out via Simultaneous Localization and Mapping
(SLAM). SLAM involves consistent imaging of the surrounding environment and then
processing the image for viable paths, identifying obstructions, matching it to a priori maps
to self-localize and more (Kim & Lee 2013). The imaging is often done using a rotating
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LIDAR, depth cameras or similar optical sensor array that collects 2D depth maps of the
surroundings.
A drawback of SLAM is that there is a notable mismatch between the periodic mapping
and the actual position of the host. This can be caused by hardware nuances,
irregularities of the environment surface, and orientation of the imaging devices on both
the robot or the target user which culminates in accumulating drift errors. A study
conducted by the University of Massachusetts attempted to solve this problem by
complementing a SLAM localization system with an overhead facing camera that tracks
and identifies Quick Response (QR) markers pasted on the ceiling throughout the
obstacle course (McCann et al. 2013). The overhead camera identifies and examines the
angle of the viewed CR marker to determine the severity and vector of the robot’s path
deviation. This information is used to skew the SLAM map and reduce the accumulated
drift error.
Combinations of RF and vision-based technologies for self-localization are also a viable
option for simultaneously mitigating the weaknesses of both types. One example involves
the fusion of RF and Pyroelectric Infrared (PIR) sensors for tracking the indoor location
of a patient. On its own, the PIR module is mounted overhead and is triggered whenever
it detects motion within its zone of detection. However, these devices are inaccurate
because they are susceptible to detecting changes in lighting conditions as movement.
By combining a ZigBee IEEE802.15.4 receiver to each PIR node and placing an RF
transmitter on the patient, the system’s reliability is elevated dramatically. As the patient
approaches the node, the PIR module triggers and prompts the receiver to sample the
RSS of the emitted signal. This will help filter out false PIR detections when the patient is
nowhere nearby.
While there are numerous examples and advances in self-localization technologies, it is
observed there currently is no portable solution that can be worn by the human target for
human tracking without incurring significant obtrusiveness. However, these techniques
may be viable when applied to the companion robot for determining its own location within
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the operating environment’s complex. In addition, the use of QR markers as a corrective
measure to complement the robot’s SLAM localization could be applicable for tracking
the orientation of a human target relative to the companion robot in a range-free fashion.
3.2.3 Body Tracking
The motion capture technology discussed earlier in this chapter is one of the key
components in the field of human body tracking. Over the last three decades, there has
been great strides in the effort to record and study the natural motion of humans and living
creatures. The applications of this study are vast, ranging from creating believable
animations, reproducing natural gaits in humanoid and bioinspired robotics, to gait
corrective therapies, motion-activated video games and interactive telecommunications.
In the past, motion capture could only be done via cameras capable of high framerate
videography, bodysuits with marker points and processing software banking on blob
detection. The recorded videos were processed to remove everything except for the
marker points that are registered as blobs. These blobs correspond to the joints and limbs
of a tracked person which can be rigged to an animated body to visualize the natural
motion and gestures that a human subject is capable of. The entire operation used to be
time consuming and the actors had to wear uncomfortable marker bodysuits throughout
the recording process while being expected to move and behave naturally.
Today’s motion capture devices rely on simulating depth perception using a variety of
techniques. The captured frames are depth maps, with each pixel representing a distance
between the sensor and the physical point in the real-world. These depth maps offer a
3D representation of the world within the device’s view-space, including people, obstacles
and boundaries. These shapes can then be compared for shape profiles matching human
bodies, which then have a virtual skeleton rigged to them. Thus, subsequent motion
capture and study can be done by tracking and recording how the rigged skeletons move.
This markerless method is far less obtrusive than the use of bodysuits, making them a
popular technology to be adapted for entertainment purposes.
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One of the most iconic consumer-grade motion capture devices available off the shelf is
the Microsoft Kinect (Microsoft Corporation 2013). It was primarily marketed as a next
generation video game controller for the Xbox console, capable of markerless body
gesture tracking for up to 4-6 people (Mankoff & Russo 2013). When applied for research
use, an accompanying Windows development kit has made it possible for researchers to
acquire an affordable depth camera capable of visual mapping. The applications for this
technology spans from SLAM to rapid imaging real world objects into 3D models.
Other alternative motion capture products are also available, such as the Intel RealSense
(Creative Technology Ltd. 2014) which was intended to be a hardware template for PC
manufacturers to implement their own desktop productivity motion capture. Integrated
into consumer computers and smartphones, RealSense enables gesture recognition as
a supplement Human Interface Device alongside mice, keyboards, webcams and
microphones to facilitate a whole host of possible productivity functions. The RealSense
hardware template indicates the use of stereoscopic cameras to emulate depth
perception as opposed to using and IR-based depth camera that Microsoft Kinect
implements.
Another COTS motion capture device was produced by Sony for their PlayStation series
of video game consoles. Called the PlayStation Eye, the device is basically a single RGB
camera with accompanying image processing suite that conditions the captured frames
and extracts shapes that corresponds to human profiles before attempting to find
matching gestures. Unfortunately, this device is highly susceptible to environmental
lighting and sensor noise. An updated version has since been released, implementing an
RGB-D depth camera similar to Microsoft Kinect (Sony Computer Entertainment Inc
2013). Although single camera depth perception performs tenuously at best, it may be a
viable option for applications that demand some degree of motion capture but under
limited mounting space. Once such study attempted to examine the performance of a
monocular camera setup after modifying it to vary the focus of the captured frames so
that the depth component of a viewed object can be approximated. This method of depth
perception from a single optical sensor is referred to as the Depth from Focus technique
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(Gaspar et al. 2015). The system was able to approximate the motion of a small blimp as
it floats in 3D space. The success of this study shows that there are viable alternatives
aside from the stereoscopic and IR array depth cameras whose performance depends
largely on triangulation strategies but suffer from ‘blind spot’ situations where a tracked
target is within the FOV of one camera but not in the other.
The rising availability of these devices has of great benefit to vision-based robot
navigation, machine vision, physiotherapy, and assistive technologies. Augmented with
existing microelectromechanical sensors such as the Inertial Measurement Units and
biomonitoring sensors, these systems have helped elevate explorations into previously
unknown avenues for studying human behavior.
To gauge the performance between consumer grade motion capture devices like
Microsoft Kinect and industrial standard offerings, a study was carried out by applying
both systems to assess gait and standing balance (Yang et al. 2014). At the time of writing,
pioneering consumer grade motion capture devices such as the Kinect had already been
in commercial circulation for a few years and subjected to a multitude of firsthand
accounts on the grounds of body tracking. The industrial benchmark selected for the study
was the NDI Optotrak Certus provided by Northern Digital (Northern Digital Inc. 2015). To
carry out the experiment, subjects were instructed to assume three different standing
poses for each system. Posture and balance are determined by factoring variables such
as the person’s center of mass via kinematic data output from software. Traditionally, this
test is carried out manually by a physician and it demands full attention and prior
assessment history to produce a viable assessment. The study showed that the industrial
benchmark reported higher kinematic readings, but similar variance in measurements
between all posture sets are reported by both systems. This shows that even though the
more expensive Certus system produced more accurate readings, the Kinect proved to
be equally as effective for body posture assessments.
RGB-D depth cameras had also been involved with application experiments for
augmenting physiotherapy sessions held in home environments. One example used the
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device as part of a personal rehabilitation tool to help facilitate reinforced exercises at
home without requiring the presences of a physician (Su 2013). The exercises were
carried out in a series of repetitive strength-building challenges that helps to gradually
increase the user’s motor capabilities. Performance data for each session helps measure
the patient’s progress and actively adjust the exercise tolerances for future attempts. The
motion capture device helps record the patient’s posture and motion throughout the
sessions, and then supply the performance data to a machine learning component that
determines the settings for progressive attempts. The goal of the machine was to offer
rehabilitative exercises that continually challenges the user without resulting in injury.
However, this study has also reported that conventional motion capture devices suffer
from lens occlusions, insufficient adaptability to changing lighting conditions and glitches
inherent to the body tracking software. These drawbacks had unfortunately limited the
potential of the prototype.
When applied to specific body tracking and activity monitoring needs, conventional depth
cameras had resulted in significant successes. A study was carried out to examine the
applicability of a Kinect device for upscaling an existing test which evaluates the upper
extremity motion in children diagnosed with Cerebral Palsy (Rammer et al. 2014). The
Shriners Hospitals for Children Upper Extremity Evaluation is a comprehensive test for
measuring a child’s capability in carrying out Activities of Daily Living (ADL). Once the
motion capture device locates the child’s body and assigns the virtual skeleton to it,
individual limbs can be examined for degrees of alignment and limb control. Meanwhile,
the hands can be tracked for grasping and releasing actions. The markerless vision-
based device had proven to be of great help in providing quantifiable measurements for
quality of motor control and physical action executions.
This subsection has presented some useful options for developing the human tracking
portion of this research’s solution. While depth cameras are susceptible to hardware
limitations, the ability to perform vision-based markerless body tracking and motion
capture may enable a companion robot to perform range-free localization between itself
and the target human. The availability of consumer grade motion capture devices such
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as Microsoft Kinect that performs comparatively with industry standard versions also
mean that there is little sacrifice of performance over price. In addition, this move also
supports the companion robot planning template’s objective of relying on COTS
components.
3.2.4 Biomonitoring
This third category of human tracking technologies is a collection of miscellaneous
biometrics and health monitoring systems that do not directly contribute towards
localization nor motion capture. Even so, some of these projects may offer indirect
metadata that could complement existing human tracking systems. Having biofeedback
alongside markerless body tracking may eliminate false detections. For example, having
smart wearable periodically transmit heart rate readings to the robot can prevent it from
inadvertently switching focus to another body that is currently engaged in an activity that
does not match the current readings. Existing biomonitoring technologies enable the
possibility for long term tracking of health signatures such as heart rate, body temperature,
respiratory rate, galvanic skin response and more (Singleton, Warren & Piersel 2014).
Recent trends seem to indicate a rising prevalence of research surrounding the
applications of Electroencephalography (EEG), biofeedback and popularity of consumer
grade smart wearables.
In 2014, a study was carried out to explore the link between positive health experiences
and spending time in outdoor natural environments. The proposed system relies on a
Blackberry smartphone which is carried by a test subject throughout the day. Whenever
the subject spends time at a park, the GPS module logs the occurrence and triggers data
collection from onboard accelerometers to gauge the vigorousness of the outdoor activity.
Meanwhile, Experience Sampling Methods (ESM) is used to elicit feedback from the
subject on his current emotion and state of mind (Doherty, Lemieux & Canally 2014).
While the experiment did not intend to attempt quantification of positive wellbeing, it did
demonstrate how sensor fusion and consumer devices help open new avenues for
exploring human biomonitoring.
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EEG is a neurodevelopmental monitoring tool that offers non-invasive tracking of
electrical activity at the surface of the brain. It is important to note that the device is only
sensitive enough to detect the electrical activities that are closest to the scalp as that is
where the electrodes are placed. While there have not been concrete findings over the
possibility of mapping EEG readings to human behavior, the technology does allow
researchers to observe repetitive signatures whenever the subject attempts repeated
emotions and thought exercises. For instance, a study was conducted to observe the
emotional responses from Autistic children when they interact with a robot that showed
pet-like behavior (Goulart et al. 2014). Due to impaired social interaction skills, it was
difficult to elicit feedback from these children. Using a head harness to collect EEG
readings from them as they interacted with the robot, it was possible to roughly model the
change in emotions as read patterns corresponding to shifting emotional states.
Another interesting biomonitoring research was carried out using Electrooculography
(EOG) to study the micromovements of the eye in children suffering from Cerebral Palsy.
These children had faced difficulty in using the computer mouse due to inadequate motor
control, so a head-mounted system called EagleEyes uses EOG to track the eye motions
and blinks as an alternative Human Interface Device (McMurrough et al. 2012).
Unfortunately, the headgear was reportedly commented as being too bulky and
cumbersome for extended use.
Biofeedback and haptics are electromechanical means of providing physical feedback to
a user via the sense of touch. One example use of biofeedback is for naturally alerting a
user about the internal state of the body that is not actively monitored. Children with
Cerebral Palsy were commonly observed to be favoring a stronger limb while neglecting
to exercise the other. The current approach to rehabilitating this behavior is by constricting
the stronger limb, forcing the child to frequent using the other limb for ADL. A biofeedback
system was developed that prompts the user whenever a stronger limb was used
subconsciously (Bloom, Przekop & Sanger 2010). An armband embedded with
Electromyography (EMG) electrodes is strapped to the stronger limb and detects active
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nerve pulses whenever it is about to be used for an activity. The controller will then issue
an obvious vibration, alerting the user and reminding her to use the other limb instead.
Due to the behavior plasticity of the human brain and the need to prevent motor detriment
to the stronger limb instead, the device was used in 5-hour periods each day.
As initially mentioned, there is no direct benefit of biomonitoring on discerning the position
of a human target within a human following strategy. However, exploring its various
avenues of application show that there is sizeable potential at involving some degree of
smart wearable that complements a human tracking system, for the purpose reducing or
eliminating performance defects caused by vision-based hardware.
3.2.5 Examples of Human Tracking Technologies Fusion in Existing Research
The following subsection will discuss several projects in Assistive Technologies that
demonstrate sensor fusion and technique combinations between localization, human
tracking and biomonitoring. Exploring how these integrations were implemented and used
may offer insights on how fusion can be used for solving the human tracking challenge
that this research is focused on.
A prototype system was built to help cognitively disabled people overcome their struggle
with task sequencing, which is the ability to mentally dismantle a task into a sequence of
steps. Oftentimes, they had to be fully supervised so that they do not inadvertently
become distracted and deviate from their original tasks. Called the Teeth Brushing
Assistance (TEBRA) system, the machine was made to guide the person in the task of
brushing teeth. It was expected that the user would lose concentration, deviate from the
sequence or possibly injure herself. Thus, the machine had to be able to monitor the
accomplishment of each sequence while offering context sensitive instructions to the
person to mitigate any deviation. TEBRA was constructed around a washbasin set,
augmented with an LCD display “wash mirror”, speakers and proximity sensors. And IMU
module is integrated into the toothbrush to monitor active use. A pair of cameras, one
overhead and the other facing the user, are used for body tracking motion capture. The
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tap is attached with a flow sensor as well. As the user enters the washbasin set, step-by-
step interactive instructions are presented to guide the accomplishment of teeth brushing
without the need for a supervisor. Deviations are detected by the combination of motion
capture and biomonitoring sensors while the appropriate corrective instructions are
determine via machine learning (Peters et al. 2014). While the TEBRA system was
applied with some success, it could not stop the user from outwardly ignoring the audio-
visual prompts or exiting the washbasin set entirely. Ultimately, TEBRA was entirely
purpose-built to guide accomplishments of a single task. It would not be feasible to
produce a version for the multitudes of ADL.
Another study demonstrated that any technology can be repurposed for tracking humans
if its features coincide with the intended characteristic for logging. The experiment aimed
to study behavioral differences between baseline children and those diagnosed with
Autism Spectrum Disorder (ASD) when placed in a group play environment. The
monitoring was done using a Noldus EnthoVision-XT, an outdoor camera nest that is
originally intended for wildlife filming and study of repetitive behavior in the absence of
humans. This system was mounted overhead and set to monitor the children who wore
color-coded clothes for easy identification. Through video processing, the Region of
Interest (ROI) and Turning Bias (TB) could be examined in each child. The findings show
that children with ASD tended to stay away from large groups of other children (Cohen et
al. 2014). Similar forms of repurposing motion capture devices for the use of human
following may be possible.
Georgia Institute of Technology introduced the term “Behavioral Imaging” by exploring
the potential of merging behavioral science and computational technology. The study of
natural behavior has traditionally been carried out manually and is limited by the available
fieldwork time span for human researchers. Today, the process of observation and
building behavior portraits can be automated using multi-modal sensor networks. These
networks can be outfitted with a variety of sensors that take respiratory, cardiovascular
and electrodermal readings. This allows collection of long-term observation data into
Multimodal Dyadic Behavior (MMDB) unified data-sets which can be gradually improved
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by other study sessions that involve the same subject. A pilot study applied this system
to the Child Study Lab, sampling body orientation, actions, and speech from children with
ASD while they were engaged in social interaction exercises. These were done using a
combination of motion capture devices, smart wearables and microphones (Regh et al.
2014). Continued use can help enable investigations in a variety of facets surrounding
ASD and behavioral study.
These existing applications in Assistive Technologies has shown that it is a viable practice
to integrate tracking technologies of various natures to circumvent individual hardware
limitations and in some cases, exceed the functionalities inherent in separate systems
when used in fusion. It is also possible to repurpose a tracking system hardware to suit
another human tracking strategy, if its raw functionality can be adopted as part of that
strategy.
3.3 PROPOSED COMBINED HUMAN TRACKING AND INDOOR NAVIGATION SOLUTION
After reviewing surveyed current works between indoor robot navigation systems and
human tracking techniques, the garnered findings have helped shape a potential solution
to the two challenges identified in this research. Working backwards, the solution
formation begins with solving the problem of reliable human tracking while catering to the
COTS requirement of the robot planning template in Chapter 2.
The literature survey has indicated in more than several occasions, the potential and
applicability of consumer grade motion capture devices such as Microsoft Kinect. These
vision-based sensor systems can identify and track the body profiles of human subjects
without the need of bodysuits or expensive studio setups of old. However, the drawback
to these systems include susceptibility to lens occlusions, latency and false detections
that are caused by limited hardware FOV, sensor refresh rates, erratic human subject
motions, cluttered environments and dynamic ambient lighting. Augmenting standalone
motion capture devices with similar redundant systems may help reduce the frequency of
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false detections. However, the use of QR code markers in a surveyed study helped inspire
the idea of a candidate redundant system that tracks active markers worn by the primary
target. By matching the tracked position of bodies and the target’s markers, false
detections and miscellaneous bodies can be eliminated, singling out the primary target.
The fusion of RGB-D based motion capture and a combination of vision-based marker
tracking, and smart wearables could result in greater reliability and performance.
With the ability to keep track of the primary subject’s position, this information can be
made available as a component in tackling the indoor robot navigation problem. Instead
of expending resources in implementing embedded environments and beacons, a range-
free method is adopted. Considering both the robot and the primary subject as nodes, the
distance and heading between both can be acquired by means of extracting and
approximating depth data. Computational resources may be limited because the entire
robot control system must reside on a standalone platform, so a LIDAR-based SLAM
approach to environmental perception is not feasible. However, the ability of scanning the
immediate environment can be carried out by extracting the raw depth frames from the
RGB-D motion capture device. Inspiration is drawn from yet another covered study by
attempting to create a multi-layer map of the environment consisting of the primary target
position awareness, immediate proximity landscape and mid to long-range depth
landscape.
Since one of the most popular wayfinding algorithms revolve around the Potential Field
Method, perhaps a vertical adaptation like the Virtual Force Histogram (VFH) could be
generated using the transformed layers (subject position, immediate proximity and mid to
long-range landscape). The resulting composite array could be used as an indicator for
the best direction to head towards, in case an obstruction is encountered while the robot
attempts to relocate itself so that it is within escort distance to the primary target.
This proposed solution will be further developed into a formal indoor companion robot
navigation system in the next chapter.
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3.4 CHAPTER SUMMARY
This chapter aimed to explore existing research that had encountered the two challenges
while implementing indoor robots, then examining the solutions presented. Beginning with
indoor wayfinding and obstacle avoidance, the survey has found that the indoor
navigation problem had to be categorized into location, autonomous pathfinding and
hardware related categories due to variations of complications. The primary issue with
indoor navigation is that it must contend with a dynamically shifting environment that
makes preplanning both computationally taxing and rapidly invalidated. More success
could be found in adopting organic algorithms related to the Potential Field Method that
react and adapt to dynamic environment changes. Hardware wise, SLAM is the most
prominent solution for real-time imaging of the surround environment for planning
maneuvers between obstacles, but it is also the most expensive and computationally
demanding.
The second half of the chapter explores existing human tracking technologies in the
attempt to identify possible solutions to implement a more reliable and effective way of
consistently tracking a human target’s position relative to the companion robot. The
survey findings were interesting because they documented a rising prevalence of
consumer accessible motion capture devices that do not require worn markers.
Unfortunately, most of these devices are vision-based hardware that suffer from common
limitations such as lens occlusions, refresh rates and shifting lighting conditions. The
literature survey continued to explore other monitoring technology avenues in the attempt
to find alternative tracked human attributes that can complement vision-based motion
capture. The results have shown that there are a multitude of research work documenting
the idea of sensor fusion and its successes at both mitigating hardware limitations of
individual sensors while allowing functionality repurposing.
The result of this chapter is the formation of a solution based on reviewing the existing
technologies covered throughout the literature survey. It proposes composite human
tracking fusion between a COTS motion capture device and a redundant marker tracking
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system involving an optical camera and active markers mounted as smart wearables.
Having both tracking systems homed into a human target will help eliminate false
detections as well as mitigate subject occlusions that tend to plague single vision-based
systems. The use of an RGB-D motion capture device also allows extraction of raw depth
maps that can be processed as a mid to long-range snapshots of the environment.
Combined with immediate proximity data and constant update on the primary target’s
position, these components can be fed into an adapted Potential Field algorithm to help
guide the companion robot around obstacles towards reaching the correct escort distance.
This solution will be expanded and implemented in the next chapter.
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Chapter 4: DESIGN AND PROTOTYPING OF THE MULTI-SENSOR FUSION-BASED NAVIGATION MODEL
4.1 INTRODUCTION
Chapter 2 presented the results of a literature survey on the current applications of
assistive robotics in aiding children, the elderly and people with disabilities. It elaborated
on the way robots are used to augment therapies, improve quality of life and serve as a
buffer to compensate for absence or shortages of caregivers. This need for robotic
assistance is most evident in the care for cognitively disabled children, as emphasized in
application area of this research. Children with ASD and Cerebral Palsy are very
susceptible to self-inflicted injuries and there is a need for more prevalent companion
robots to counter these occurrences. Hence, the chapter ends with a proposed
companion robot planning framework that focuses on Commercial Off-The-Shelf (COTS)
hardware and software components.
In accordance to the framework, the Companion Robot Avatar for the Mitigation of Injuries
(CARMI) was planned and designed as an application case study for this research (Tee,
Lau, Siswoyo Jo & Then 2015). CARMI is intended to act as an observer that tracks and
follows a child autonomously. It identifies the child’s actions using motion capture and
image processing to see if it matches any predefined activities that commonly lead to
injuries (such as punches, jumps and falls) (Lau, Ong & Putra 2014). This system is
extended by adding a notification feature that wirelessly warns a caregiver whenever such
an action is detected. The caregiver may also choose to initiate a video call through the
robot, using CARMI as an avatar to facilitate conversation between the child and
caregiver. Hence, CARMI satisfies the requirements for implementing the Robot-Based
Injury Prevention Strategy as outlined in Chapter 2.
To accomplish this in line with the robot planning framework, the activity tracking system
must be mounted on a basic indoor mobile robot platform that is actuated with dual-drive
DC motors. It senses the child and environment using a combination of depth sensor,
ultrasonic sensor modules and an RGB camera. Video-calling is enabled using a
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microphone and a dedicated webcam that come bundled in an on-board computer. These
hardware component selections are laid out in Table 4-1.
Table 4-1: Initial hardware plan for CARMI (Tee, Lau, Siswoyo Jo & Then 2015)
Hardware
Input Output
The primary software module for CARMI governs the operation of the autonomous activity
tracking system in tandem with robot navigation. Essentially, CARMI needs to relocate
and re-orientate itself to optimally detect and track the child’s actions. The human-
following and activity-tracking work must be carried out without direct human control.
There is also no option for direct interaction between the child and robot, however
triggering the video-call feature will cease this module’s operation. This planned behavior
is outlined in Table 4-2.
Direct - Audio/Visual
Communication o Microphone o Embedded Webcam
Indirect - Collision Detection
o Ultrasonic Sensors
- Subject Position Tracking o Depth Sensor o Webcam
Physical - Relocation
o Motor-Actuated Wheels (variable drive system)
Non-Physical - Audio/Visual
Communication o Tablet\Laptop
Speakers o Tablet\Laptop Display
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Table 4-2: Initial software plan for CARMI (Tee, Lau, Siswoyo Jo & Then 2015)
Module Layer Configuration
Automated
Injury
Detection
Mode
Interaction
Autonomous – Full reliance on camera, depth sensor and
proximity sensors to track the subject and avoid collisions
Interface
• Motion Tracking using vision processing
• Dedicated Vision-based Injury Prevention System
• Embedded-level collision detection suite
Operation
Augment Only – use mobility and separate camera-based
tracking to minimize Injury Prevention System Field-Of-View
limitations
Intervention
Monitoring Only – No pre-programmed intervention
routines. Caregivers are notified when the Injury Prevention
System detects a possible situation.
Embedded
Control
Closed-Loop – All tracking, following and collision
avoidance routines are autonomously carried out by
embedded microcontrollers.
The CARMI inception process and planning using the framework has highlighted the
importance of human-following for companion robots. In all applications of assistive
robots that accompany a human user, some form of autonomous human tracking and
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following is required so that they may continue functioning in the appropriate proximity of
their target.
Table 4-3: Injurious gesture detection performance (Tee, Lau, Siswoyo Jo & Wong 2016).
Gesture/Activity Samples Success Accuracy (%)
Punch (L/R) 30 11 36.667
Push (Both) 30 19 63.333
Jump 20 18 90.0
Fall (Backwards) 20 16 80.0
Overall Accuracy 58.425 %
Thus Chapter 3 encompasses a survey of the state-of-the-art for human tracking methods,
particularly in indoor localization using various combinations of wireless, vision-based and
wearable technologies. One study involving human motion tracking via Microsoft Kinect
has shown great promise in vision-based injury detection (Ann & Lau 2013). This method
was reproduced for the CARMI prototype to examine its performance as part of the
autonomous activity tracking system (Tee, Lau, Siswoyo Jo & Wong 2016). The system’s
visual gestures detection algorithm uses a neural-network that can be trained to identify
possibly injurious motions such as falls, punches, pushes, and jumps with decent
performance, as shown in Table 4-3. More detail of this experiment can be found in
section 4.5.5.
However, the performance of the activity-tracking system is hampered by the limited zone
of optimal detection in addition to optical occlusions, susceptibility to sunlight and effects
from environmental lighting. The depth sensor’s optimal zone for detection is a 57.5° cone
projected in front of it (Figure 4-1). Only subjects situated between 1.2-3.5m within that
cone can be tracked properly (Microsoft Corporation 2013). Other human activity tracking
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systems such as Asus Xtion (ASUSTeK Computer Inc. 2014) and Intel RealSense
(Creative Technology Ltd. 2014) also share the similar hardware limitations.
Figure 4-1: Microsoft Kinect documentation of hardware limitation. (Microsoft
Corporation 2013)
The literature survey also revealed that there have been numerous attempts to improve
human activity and position tracking performance by augmenting a sensor system with
another redundant one. The auxiliary device is meant to take over sensory perception in
case the primary fails. This technique of sensor-fusion is explored as a possible solution
to overcome that optimal zone limitation, resulting in the attempt to implement a human-
orientation tracking system using an active InfraRed marker.
4.2 HUMAN ORIENTATION TRACKING USING AN ACTIVE INFRARED (IR) MARKER
The primary problem with optical depth-based motion tracking hardware is the narrow
Field of View (FOV). Coupled with the general struggles of vision-based systems such as
susceptibility to environmental lighting and optical occlusions, these systems suffer from
a limited zone for optimal operation.
The first attempt to solve this problem is to find a suitable redundant tracking system that
can help identify the relative position and orientation of the target with reference to the
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robot. This study resulted in a hybrid wearable and optical based method (Tee, Lau,
Siswoyo Jo & Lau 2015). The target wears a vest that is lined with IR LEDs that form an
active marker which is visible to the IR camera. OpenCV is used to identify the visible
light blobs and assess their relative positions to form the marker (Figure 4-2).
Figure 4-2: First prototype of the active IR marker. The vest is equipped with hook &
loop strips that allow the IR modules to be mounted in a variety of patterns. An example of a pattern as perceived by the camera is shown. (Tee, Lau, Siswoyo Jo & Lau 2015)
The raw camera feed is processed to correct tilt, cropping and scale so that a standard-
sized marker is acquired. This preconditioning process is illustrated in Figure 4-3. The
mapped blobs are rotated so they form an upright square with two inner blobs indicating
the orientation (midpoint and top-left quadrant for upright configuration). Finally, the outer
blobs are removed so the inner blobs form the standard pattern that can be used for
matching saved templates once scaled to the correct size.
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Figure 4-3: IR Active Marker preconditioning process. (Tee, Lau, Siswoyo Jo & Lau
2015)
Once this conditioned marker output can be acquired in real-time, a database set of
patterns can be recorded, each corresponding to a different target orientation relative to
the camera as shown in Figure 4-4. This data set is acquired at the beginning of each
runtime, using a calibration rig depicted in Figure 4-5.
Figure 4-4: Example of an orientation pattern data set. (Tee, Lau, Siswoyo Jo & Lau
2015)
Figure 4-5: Calibration rig for the active IR marker and camera. (Tee, Lau, Siswoyo Jo &
Lau 2015)
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During operation, the system was expected to see the target’s constellation of LEDs,
precondition the raw video frame into a pattern, then perform a comparison between this
pattern and each calibrated data set. This comparison process is carried out using the
Template Matching technique (Anderson & Schweitzer 2009). The closest matched data
set should indicate the approximate orientation of the target relative to the robot. This
information can then be used for visual servoing the robot, relocating to where the target
is facing. The use of ultrasonic sensors was initially intended for determining the distance
between the robot and target.
Table 4-4 shows the IR-based system’s detection zone, as sampled during morning and
evening conditions in Office and Laboratory environments. Each sample is collected by
gradually moving the marker away from the camera until it is no longer detected. The
distance and angle at which the marker was last visible were recorded. While the
orientation tracking system did operate as intended, it could not maintain consistent
accuracy beyond an average of 1.67m. This did not exceed the optimal zone of detection
for the Microsoft Kinect benchmark device, thus eliminating this system as a viable option
for the sensor-fusion solution. However, it should be noted that the raw hardware
detection performance of the full pattern is double of the orientation tracking. Figure 4-6
illustrates the hardware detection zone with respect to the orientation tracking
performance.
Table 4-4: Performance results of the human-orientation tracking system. (Tee, Lau, Siswoyo Jo & Lau 2015)
Criteria Office Laboratory
Average Morning Evening Morning Evening
Horizontal FOV (degrees) 54 56 68 61 68 63.25
Vertical FOV (degrees) 63 54 62 54 52 55.5
Hardware Detection
Zone
Straight-Line Distance (meters)
2.6 4.04 4.14 3.2 2.44 3.455
Left-Extreme Distance (meters)
- 2.86 2.63 2.48 2.62 2.648
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Right-Extreme Distance (meters)
- 2.35 3.09 2.62 2.51 2.643
Orientation Tracking
Zone
Straight-Line Distance (meters)
- 1.59 1.92 1.61 1.56 1.67
Left-Extreme Distance (meters)
- 1.34 1.47 1.15 1.48 1.36
Right-Extreme Distance (meters)
- 1.21 1.58 0.95 1.32 1.265
Further attempts were taken to gauge the maximum distance between the camera and
IR marker before none of the LEDs are left visible. The results showed that the raw IR
marker can be tracked by the camera to more than 10 meters under ideal indoor lighting
conditions. By extending the constellation of IR LEDs across the entire vest and ignoring
target orientation, the wearer’s relative position can be tracked by the camera within 10m
of the robot. This development has led to the inception of the following multi-sensor
fusion-based navigation model.
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Figure 4-6: Illustration of the hardware detection zone performance, relative to
orientation tracking. (Tee, Lau, Siswoyo Jo & Lau 2015)
4.3 SENSOR-FUSION BASED ROBOT NAVIGATION MODEL
The fusion model proposed in this project is aimed at improving the performance of
human activity tracking systems that utilize single depth sensor solutions, by adding an
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additional tracking system that relies on an Active Marker. Aside from the benefit of adding
redundant sensors to compensate for loss of tracking, this model presents two algorithms:
a) A method to identify and lock onto the body that represents the primary subject of
the tracking system, utilizing the tracking of the active Infrared Marker.
b) A fusion mechanism that combines the imaging sweeps of the environment using
both ranging sensors array and the depth sensor in addition to the locked position
of the Primary Subject.
4.3.1 Identification and Locking of Primary Subject
When used for human tracking, the depth sensor functions by identifying and tracking
objects in front of it that fit the profile of a human body. Once a person is identified, it is
registered as a body by the sensor system. Often, human tracking depth sensors can
track multiple bodies at a time. However, without any additional data, it is difficult for it to
continuously track a single primary subject’s body, especially in situations where there
are multiple secondary bodies moving around. This model relies on the addition of an
Active IR Marker worn by the primary subject. The position of the IR Marker is picked up
by the IR camera and used to help identify which body belongs to the primary subject.
4.3.1.1 The Depth Sensor Camera Space
To begin, the camera space of the depth sensor must be understood. For expressing this
model, the depth sensor’s camera space or view space is illustrated in Figure 4-7.
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Figure 4-7: The depth sensor's camera space illustration.
The camera space is projected as a 2D inverted Cartesian plane, with the origin set at
the top right corner (𝑈𝑜,𝑉𝑜) . ′𝑈𝐿𝑒𝑛𝑔𝑡ℎ′ and ‘𝑉𝐿𝑒𝑛𝑔𝑡ℎ′ represent the horizontal and
vertical spans of the camera space, typically in pixels. Most depth sensor systems identify
the positions of bodies by their individual centroids with reference to the (𝑈𝑜,𝑉𝑜). Thus, the
bodies would be tracked as (Eq. 4-1):
(𝑈1, 𝑉1), (𝑈2, 𝑉2), (𝑈3, 𝑉3) … (𝑈𝑛, 𝑉𝑛) (Eq. 4-1)
However, this method is wholly dependent on the sensor hardware’s Field of View (FOV).
To decouple the positioning method from hardware dependence, the coordinate system
(𝑈𝑉) is transformed to ′𝑢𝑣′, by defining the new origin (𝑢𝑜,𝑣𝑜) (denoted by the Δ) at
the middle point of the camera space (Eq. 4-2).
𝑢0 =𝑈𝐿𝑒𝑛𝑔𝑡ℎ
2 ; 𝑣0 =
𝑉𝐿𝑒𝑛𝑔𝑡ℎ
2 (Eq. 4-2)
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Thus, the position of each tracked body will be expressed in terms of the 𝑢𝑣 coordinate
system, using the following transformation (Eq. 4-3):
𝑢 = 𝑈 − 𝑢0 ; 𝑣 = 𝑉 − 𝑣0 (Eq. 4-3)
The adjusted position of all tracked bodies will now look as the following (Eq. 4-4):
(𝑢1, 𝑣1), (𝑢2, 𝑣2), (𝑢3, 𝑣3) … (𝑢𝑛, 𝑣𝑛) (Eq. 4-4)
4.3.1.2 The IR Active Marker Tracker Camera Space
Next, the camera space of the IR Active Marker tracking system is illustrated in Figure
4-8.
Figure 4-8: Illustration of the Active Marker IR Camera's view space.
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Since both IR camera and Depth sensor are vision-based systems, they share the same
definition of camera space in terms of inverted 2D Cartesian coordinate system. The
origin (𝑋𝑜,𝑌𝑜) is located at the top left of the plane, while ′𝑋𝐿𝑒𝑛𝑔𝑡ℎ′ and ′𝑌𝐿𝑒𝑛𝑔𝑡ℎ′
represent the horizontal and vertical spans of the camera space. To differentiate the
discussion from that of the Depth sensor, the figure uses the ′𝑋𝑌′ notation to represent
the IR Marker system’s camera space.
Unlike the Depth sensor, this tracking system only has one Active Marker to look for. The
Active Marker consists of a series of Infrared light sources (most typically LEDs) arranged
in a pattern around the Primary Subject’s body. When worn, multiple IR LEDs should be
visible to the IR Camera so long as the primary subject (the wearer of the IR Marker) is
within the vicinity of the tracking system. The position of each LED blob is expressed with
reference to the ′𝑋𝑌′ coordinate system’s origin (𝑋𝑜,𝑌𝑜) (Eq. 4-5).
(𝑋1, 𝑌1), (𝑋2, 𝑌2), (𝑋3, 𝑌3) … (𝑋𝑛, 𝑌𝑛) (Eq. 4-5)
The position of the Active Marker is indicated by the centroid of the cluster of visible IR-
LEDs with respect to the IR camera’s view space. This can be found by finding the
Arithmetic Mean of the set of visible LED positions (Eq. 4-6).
(𝑋𝐶𝑒𝑛𝑡𝑟𝑜𝑖𝑑
𝑌𝐶𝑒𝑛𝑡𝑟𝑜𝑖𝑑) = ∑
1
𝑛(
𝑋𝑛
𝑌𝑛)
𝑛
𝑖=1
(Eq. 4-6)
Like the case of the depth sensor, the raw position data from the IR camera is dependent
on the hardware’s FOV. To decouple it, the coordinate system of ′𝑋𝑌′ must be
transformed to ′𝑥𝑦′, whose origin is (𝑥𝑜,𝑦𝑜) (denoted by the Δ) centered at the midpoint
of the camera space (Eq. 4-7).
𝑥0 =𝑋𝐿𝑒𝑛𝑔𝑡ℎ
2 ; 𝑦0 =
𝑌𝐿𝑒𝑛𝑔𝑡ℎ
2 (Eq. 4-7)
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Finally, the Active Marker position (𝑥𝑀𝑎𝑟𝑘𝑒𝑟,𝑦𝑀𝑎𝑟𝑘𝑒𝑟) is found after adjusting the centroid
position to the ′𝑥𝑦′ coordinate system (Eq. 4-8).
𝑥𝑀𝑎𝑟𝑘𝑒𝑟 = 𝑋𝐶𝑒𝑛𝑡𝑟𝑜𝑖𝑑 − 𝑥0 ; 𝑦𝑀𝑎𝑟𝑘𝑒𝑟 = 𝑌𝐶𝑒𝑛𝑡𝑟𝑜𝑖𝑑 − 𝑦0 (Eq. 4-8)
4.3.1.3 Calibration: Finding the Calibration Offset and Scaling Factor
Even though the coordinate system for both the depth sensor and IR camera has been
decoupled from dependence on the hardware screen dimensions, the perceived Marker
position will not match the appropriate Body position due to the displacement between
sensors as well as the differing lens specifications of each device. All these factors can
to be addressed by applying appropriate transformations to the position data of one
sensor, so that it matches the scale and orientation of the other.
First, we address the issue of sensor displacement. Since this model uses a multi-sensor
fusion approach, it is assumed that both sensors exist as separate hardware components
mounted at a fixed displacement between each other. Thus, if the primary subject is
positioned at the origin of one sensor, that person may be perceived at a position away
from the origin of the second sensor. Figure 4-7 and Figure 4-8 show a good example
for this scenario. In this case, the Primary Subject is tracked by the depth sensor as Body
3, which is currently positioned at the origin (𝑢𝑜,𝑣𝑜). However, due to the position and
orientation of both sensors, the IR Camera finds that the Marker position is slightly off to
the bottom right of the origin (𝑢𝑜,𝑣𝑜). To adjust the IR Camera position data to match the
depth sensor’s, an offset (𝑥𝐶𝑎𝑙𝑖𝑏,𝑦𝐶𝑎𝑙𝑖𝑏) must be applied. Since the position and orientation
of both sensors should not change during the operation, the offset values can remain as
a constant variable. Thus, the following mapping is applied (Eq. 4-9):
(𝑥𝑀𝑎𝑟𝑘𝑒𝑟 − 𝑥𝐶𝑎𝑙𝑖𝑏
𝑦𝑀𝑎𝑟𝑘𝑒𝑟 − 𝑦𝐶𝑎𝑙𝑖𝑏) ⇒ (
𝑢𝑃𝑟𝑖𝑚𝑎𝑟𝑦
𝑣𝑃𝑟𝑖𝑚𝑎𝑟𝑦)
(Eq. 4-9)
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During initial calibration of the sensor fusion system, the Primary Subject must wear and
activate the Active Marker, then be positioned in front of the depth sensor so that the
Primary Body is located at the origin (𝑢𝑜,𝑣𝑜). Then, the marker position is collected from
the IR camera. This offset from the origin is used as the Calibration Offset (𝑥𝐶𝑎𝑙𝑖𝑏,𝑦𝐶𝑎𝑙𝑖𝑏) .
Thus, when the Primary Subject is positioned at the depth sensor’s origin (𝑢𝑜,𝑣𝑜), the
marker position (after application of the ‘Calibration Offset’) should also be at its origin
(𝑥𝑜,𝑦𝑜).
While the positions of the Primary Subject’s Body and Marker coincide at the origin, the
displacement of both may not be the same after he moves to another position. The use
of different lens between the devices introduces the influences of differing apertures,
angles and occlusion. This means that even if the Primary Subject moves to a new
position, the displacement between the new position and the origin would not be the same
between different sensors. Barring the effects of extreme lens occlusion and angles, this
model assumes that the difference in perception of Subject displacement between
sensors is a roughly linear problem that can be compensated by application of a Scaling
Factor ′𝑘′ (Eq. 4-10).
𝑘𝑥 =𝑢𝑃𝑟𝑖𝑚𝑎𝑟𝑦
𝑥𝑀𝑎𝑟𝑘𝑒𝑟 − 𝑥𝐶𝑎𝑙𝑖𝑏 ; 𝑘𝑦 =
𝑣𝑃𝑟𝑖𝑚𝑎𝑟𝑦
𝑦𝑀𝑎𝑟𝑘𝑒𝑟 − 𝑦𝐶𝑎𝑙𝑖𝑏 (Eq. 4-10)
After successfully finding the Calibration Offset, move the Primary Subject to another
position that is still within the camera space of both depth sensor and IR camera. Collect
the Primary Subject’s Body Position ( 𝑢𝑃𝑟𝑖𝑚𝑎𝑟𝑦 , 𝑣𝑃𝑟𝑖𝑚𝑎𝑟𝑦 ) and Marker Position
(𝑥𝑀𝑎𝑟𝑘𝑒𝑟 , 𝑦𝑀𝑎𝑟𝑘𝑒𝑟). Using the Calibration Offset obtained from the previous step (𝑥𝐶𝑎𝑙𝑖𝑏,
𝑦𝐶𝑎𝑙𝑖𝑏), the Scaling Factor (𝑘𝑥, 𝑘𝑦) can be found. The Scaling Factor k is the ratio of
subject displacement from the origin, between sensors.
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4.3.1.4 Transforming the Active Marker Position and Finding the Primary Body
After the calibration phase, the Primary Subject is free to roam around the sensor fusion
human tracking system. During typical operation and sampling, there can be multiple
bodies registered by the depth sensor, either due to the addition of other people in the
same room, or cases of false detections because of environmental factors. The system
will attempt to find the body that best represents the Primary Subject by comparing each
registered Body’s position against the transformed Marker position.
During each sampling of Active Marker position using the IR camera, both the Calibration
Offset and Scaling Factor must be applied. Successful application will transform the
Marker position to match the coordinate system, midpoint and scale of the Depth sensor’s
camera space (𝑢𝑀𝑎𝑟𝑘𝑒𝑟 , 𝑣𝑀𝑎𝑟𝑘𝑒𝑟) (Eq. 4-11).
(𝑢𝑀𝑎𝑟𝑘𝑒𝑟
𝑣𝑀𝑎𝑟𝑘𝑒𝑟) = (
𝑘𝑥 00 𝑘𝑦
) (𝑥𝑀𝑎𝑟𝑘𝑒𝑟 − 𝑥𝐶𝑎𝑙𝑖𝑏
𝑦𝑀𝑎𝑟𝑘𝑒𝑟 − 𝑦𝐶𝑎𝑙𝑖𝑏) (Eq. 4-11)
Now that the Active Marker position (𝑢𝑀𝑎𝑟𝑘𝑒𝑟 , 𝑣𝑀𝑎𝑟𝑘𝑒𝑟 ) can be expressed within the
camera space of the depth sensor, comparison can be carried out in terms of assessing
the Euclidean Distance between each Body position and the Marker position via
Pythagoras Theorem, where n represents the total number of registered Bodies during a
sample (Eq. 4-12):
𝐹𝑜𝑟 𝑖 = 1, 2 … 𝑛: 𝐷𝑀𝑎𝑟𝑘𝑒𝑟,𝑖 = √(𝑢𝑖 − 𝑢𝑀𝑎𝑟𝑘𝑒𝑟)2 + (𝑣𝑖 − 𝑣𝑀𝑎𝑟𝑘𝑒𝑟)2 (Eq. 4-12)
The set of Euclidean distance measurements can be evaluated to find the Body that has
the least difference with respect to the Marker position. That Body would be assumed as
the one representing the Primary Subject (Eq. 4-13).
𝑃𝑟𝑖𝑚𝑎𝑟𝑦 𝑇𝑎𝑟𝑔𝑒𝑡′𝑠 𝐵𝑜𝑑𝑦, 𝐵(𝑢, 𝑣) = min𝑖=1
𝐷𝑀𝑎𝑟𝑘𝑒𝑟,𝑖 (Eq. 4-13)
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4.3.2 Pathfinding and Obstacle Avoidance
Autonomous pathfinding, especially in indoor environments, has been the singular subject
of study for numerous researchers over many years. Unmanned robot navigation must
contend with a complex combination of terrain, obstacle and dynamic motion problems
which demand equally elaborate solutions. While GPS has been integral in its role for
outdoor localization, it cannot help with navigating indoor locations without a clear view
of the sky.
Recent years have witnessed a growing number of studies that explore the fusion of
sensors for indoor localization and navigation, with specific combinations of algorithms
and strategies according to the nature of the operating environment and application. This
model is one such case: having already pinpointed the location of the Primary Subject
with reference to the robot’s camera view, ‘𝐵(𝑢, 𝑣)’, this information can be used in
conjunction with a depth sensor array to navigate around the room and get into a suitable
position where the Subject is within the optimal zone of detection.
To establish an examinable container for which this navigation model is to be built in,
some assumptions must first be made. While the Target Locking portion of the model is
designed to accommodate position tracking in both horizontal and vertical planes (x and
y axes), the Navigation portion will only utilize the horizontal component (x axis
coordinate) because the robot platform is assumed to be a ground unit that moves across
the floor.
In addition, all sensors involved in navigation are mounted on the robot itself, with no
reliance on markers, transmitters, cameras or devices placed around the environment.
4.3.2.1 Wandering Standpoint Algorithm (WSA)
With reference to navigation methods of embedded robotics outlined by (Bräunl 2006),
this model assumes that the robot will traverse its immediate environment using the
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Wandering Standpoint Algorithm (WSA). Utilizing this method as shown in the Figure 4-9
example, the robot will primarily move towards the Goal in a straight line if unobstructed.
If a barrier is detected, such as at position 1, it will stop and decide which direction to turn
to. It will turn towards position 2 because that direction will require the least amount of
adjustment. Its direction of adjustment is always determined by the general position of the
Goal with reference to itself, as seen at positions 2, 3, and 4.
Figure 4-9: Wandering Standpoint Algorithm.
This sort of robot navigation is both popular and simple to implement, because it only
relies on basic proximity or ranging sensors. These sensors determine the range between
itself and a solid obstruction using ultrasonic, Infrared or laser reflection. However,
ranging sensor arrays are constrained in terms of mounting, resolution and environmental
interference. Figure 4-10 shows an array of eight ultrasonic sensors mounted on a robot.
Note that the size of the detection cones as well as the sensor mountings are not
consistent, resulting in poor resolution. However, this arrangement is usable for roughly
identifying obstructions within the general directions (front, back and sides).
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Figure 4-10: Detection zones for an array of ultrasonic sensors on a robot.
Assume that the robot is currently at position 1 of the navigation example in Figure 4-9.
Using the ultrasonic sensors layout of Figure 4-10, the raw ranging feedback can be
represented by Table 4-5.
Table 4-5: Example of raw distance input from ultrasonic sensors array.
Sensor 1 2 3 4 5 6 7 8
Distance 10 25 60 70 75 72 40 35
The raw data can be rearranged into Table 4-6, adjusted according to the general
directions around the robot. The South position feedback (Sensor 5) is ignored as it is
assumed that the robot would not consider a reverse maneuver.
Table 4-6: Adjusted example of ultrasonic sensors feedback.
Direction SW W NW North NE E SE
Degrees -120° -90 -60° 0° 60° 90° 120°
Sensor 6 7 8 1 2 3 4
Distance 72 40 35 10 25 60 70
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The feedback value of ranging sensors shows the distance between an obstruction and
itself. Thus, a lower value indicates a fast-approaching barrier, while a higher value hints
at possible open spaces. In the Table 4-6 example, the Forward (North) sensor detected
an obstruction (at distance of 10). To proceed, the robot will have to turn either left or right,
and it decides based on the readings of the NE and NW sensors, both showing 15 and
35 respectively. NW has a higher value, so the robot decides to turn Left (Westward)
towards position 2. In a way, it is possible to visualize the way the robot perceives its
environment by placing the ranging sensor readings into a 1-Dimensional matrix or an
array. Figure 4-11 shows an illustration of the ranging sensor array S, if only the front 5
sensors are considered. The Wandering Standpoint Algorithm will move the robot
forwards towards the Goal direction if there are no obstructions. If the S1 detects a barrier,
the left and right front sensors (S2 and S8) would be compared. The side with the higher
value indicates the clearer direction to turn to. Once the coast is clear, the robot is turned
towards the Goal direction again.
Figure 4-11: Simple visualization of the ranging sensor array, S.
While the WSA is rather robust for simple navigation, it is subject to a host of caveats due
to the way it is implemented using sensors. The ideal case would be to mount the sensors
at equal angles from one another, e.g. at -90°. -60°, -30°, 0°, 30°, 60° and 90°. However,
this may not be feasible due to the design of the robot frame not being able to
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accommodate equal mounting angles. The resolution could be improved by adding more
ranging sensors, but this is not possible due to the risk of overlapping sensor cones.
Ranging sensors consist of an emitter and receiver. By emitting an ultrasonic, Infrared or
laser pulse in a cone then receiving the echo after it bounces off solid barriers, the sensor
can correlate distance with the time or intensity of the received pulse. Clustering the
sensors close together will overlap the cones, causing resonances and receiving stray
pulses from neighboring modules.
Functionally, WSA is good for short-range navigation, allowing robot to roughly scan its
immediate vicinity, then identifying which way to turn to in case it runs into an obstruction.
The algorithm depends on the general direction of the Goal position to influence its
decision. In the ideal scenario, this refers to the Primary Target’s position with reference
to the robot’s camera view. Unfortunately, having a constantly moving Goal and not
knowing the general topography may end up causing the robot to take a longer route.
4.3.2.2 Fusion with the Depth Component and Target Lock
A possible solution to improve this method’s effectiveness is by augmenting it with a more
informed sensing system that presents knowledge of the shape of obstructions within
view. This system updates the Goal position for the Wandering Standpoint Algorithm in
real-time, biasing its decision-making process to favor directions that lead to less medium-
range barriers. When applied to the example in Figure 4-9, the robot may instead favor
the alternate route A, for fewer obstructions later.
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Figure 4-12: Template of a depth map.
The raw data from the depth sensor used in the Target Locking phase can be extracted
in the form of an image frame called a depth map, as shown in Figure 4-12. The dimension
of the depth map is the same as the depth sensor’s camera view space as used during
the Target Locking phase. Thus, the horizontal and vertical dimensions are also
expressed by ‘𝑈𝐿𝑒𝑛𝑔𝑡ℎ’ and ‘𝑉𝐿𝑒𝑛𝑔𝑡ℎ’. Each pixel of the image (shown as squares within
the template) contains a depth value that indicates the distance between the sensor and
a solid surface at that position.
Figure 4-13:Example of a depth image frame.
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Figure 4-13 shows an example of an 8-bit depth map taken of a person between two
boxes. The box on the left is closest to the depth sensor, followed by the person and then
the box on the right. Note that the color of the pixels representing each of the three objects
vary from very dark to very light indicating the distance from close to far. Since each pixel
holds an 8-bit value, this ranges between 0 (black) to 255 (white). The values are unit-
less and should be scaled according to the depth sensor’s detection range.
The depth sensor is considered as a high-resolution vision-based rangefinder, which
snapshots the depth between all objects and itself within its view space. While this may
seem useful as a replacement for the sensor array used by the WSA, the depth map is
expressed as a 2-Dimensional array that visualizes obstructions from the Vertical plane.
To map itself in a useful form onto the existing ranging sensor array S, the depth map
must be transformed into a 1-D array that represents itself in the Horizontal plane, as
illustrated in Figure 4-14.
Transformation can be as simple as experimental selection of a row of depth data to
gauge the immediate surroundings for possible ways around an obstacle. However, this
method is dangerous because the vision-based nature of depth sensor produces noisy
depth readings due to environmental lighting and optical effects. Blending several rows
of depth data is also possible to reduce the effect of noise, but since the depth sensor is
to be mounted high enough to capture the full body of the subject from a distance, it would
be unlikely that its view space is wide enough to accommodate short-range obstructions.
This eliminates the option of using the depth sensor as a replacement of the ranging
sensor array. However, the sensor should be able to detect medium-range obstacles
between the robot and the Primary Subject.
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Figure 4-14: Illustration of the transformation problem from the Vertical to Horizontal
Plane.
The position of the Primary Subject is also expressed with reference to the depth sensor.
Blending the position of the Primary Subject as well as knowledge of the surrounding
obstacles can be used to process a Goal position that help guide the Wandering
Standpoint system towards a path that is biased for less obstructions while keeping the
Primary Subject within the human activity tracking system’s detection zone.
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4.3.2.3 Introducing the Potential Field Method (PFM)
This is where the Potential Field Method (PFM) comes into play. Proposed by Khatib in
1986, this method was to be a low-level motion planning strategy for mobile robots that
incorporates simultaneous pathfinding and obstacle avoidance. It assumes that the robot
has complete awareness of locations and shapes of boundaries, obstructions, starting
and goal positions in the form of a map. Each component (boundary, obstruction, starting
position or goal) is visualized to emanate a potential field, like how a magnetized object
has a magnetic field surrounding it. Boundaries, obstructions and the starting position
have a repulsive potential field, whereas the goal has an attractive potential field. When
placed in a map, each cell holds a value that is a sum of all attractive and repulsive fields
of its surroundings. The magnitude of the fields experienced by a map’s cell increases as
it is nearer to a component. Hence the map appears as an overall artificial potential field
similar to the illustration excerpt from (Bräunl 2006) in Figure 4-15(a).
Figure 4-15: Illustration of the Potential Field Method. (a) An overhead depiction of a
potential field. (b) The same field reimagined as a contoured slope. (Bräunl 2006)
(Khatib 1986) describes this method of collision avoidance and path planning as applied
to guiding a robot arm’s end effector towards a specific position. The artificial potential
field ‘𝑈𝑎𝑟𝑡’ is the sum of the potential fields exuded by the goal position ‘𝑈𝑋𝑑’ as well as
obstructions ‘𝑈𝑂’ (Eq. 4-14).
𝑈𝑎𝑟𝑡 = 𝑈𝑋𝑑+ 𝑈𝑜 (Eq. 4-14)
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To help formulate a visible motion path, the artificial potential field expression is
transformed into a vector command form ‘𝐹𝑎𝑟𝑡∗’ representing the forces that act upon the
end effector (Eq. 4-15).
𝐹𝑎𝑟𝑡∗ = 𝐹𝑋𝑑
∗ + 𝐹𝑂∗
𝐹𝑋𝑑
∗ = −𝑔𝑟𝑎𝑑[𝑈𝑋𝑑(𝑋)]
𝐹𝑂∗ = −𝑔𝑟𝑎𝑑[𝑈𝑂(𝑋)]
(Eq. 4-15)
(Eq. 4-16)
(Eq. 4-17)
The attractive force ‘𝐹𝑋𝑑
∗’ is applied to the end effector at position X so that it reaches
towards the goal position ‘𝑋𝑑’ (Eq. 4-16). Likewise, ‘𝐹𝑂∗’ represents the repulsive force
that emanate from the surface of obstacles (Eq. 4-17). These forces are expressed as
attractive and repulsive potential fields adapted as negative gradients as shown in Figure
4-15(b). The reason for this is that the sum of forces acts on the robot end effector so that
it gravitates from its current position towards the goal. Repulsive forces emanating from
obstacles along the path are represented by the hills and peaks. Thus, the valleys from
the top to the bottom of the slope represent the pathway for the robot to take.
The attractive potential field ‘𝑈𝑋𝑑(𝑋)’ described by (Khatib 1986) appears to be inspired
by the elastic potential energy function of springs, with some notable differences.
‘(𝑋 − 𝑋𝑑)2’ indicates that the magnitude of the attractive potential field is proportional to
the square of the distance between the robot and the goal, thus imposing a dissipative
force that slows it down as it approaches its destination. The constant ‘𝑘𝑝’ is a proportional
gain for the motion’s velocity (Eq. 4-18).
𝑈𝑋𝑑(𝑋) =
1
2𝑘𝑝(𝑋 − 𝑋𝑑)2 (Eq. 4-18)
It can also be observed that the repulsive potential field is structured similarly to its
attractive counterpart. This expression considers the shortest distance to the obstacle P
and the potential field’s limit distance ‘𝑃0’. Khatib (1986) acknowledges that the effects of
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the repulsive field should be made negligible if the shortest distant between the robot and
the obstacle ‘𝑃’ is beyond the field’s limit distance ‘𝑃0’. Note that the repulsive field’s
magnitude is inversely proportional to the square of the distance between the robot and
the obstruction. Finally, the constant ‘𝜂’ represents a situational gain (Eq. 4-19).
𝑈𝑂(𝑋) = {𝑤ℎ𝑒𝑛 𝑃 < 𝑃𝑜 :
1
2𝜂(
1
𝑃−
1
𝑃𝑜)2
𝑤ℎ𝑒𝑛 𝑃 > 𝑃𝑜 : 0
(Eq. 4-19)
4.3.2.4 Adapting PSM for Transforming the Depth Component
Application of PSM in its entirety to a self-contained mobile robot would not be feasible
due to several factors. The robot is only equipped with ranging sensors to roughly scan
its immediate surroundings, and a depth sensor nest that constantly pivots to face a
primary target. This arrangement is not enough to supply PSM with an overall awareness
of the obstructions, boundaries and target of the entire room. Thus, a complete artificial
potential field is not available to calculate the traversable pathway between the robot and
the subject.
Figure 4-16: Only the obstructions and target within field of view is considered when
deciding which direction to take. (Tee, Lau, Siswoyo Jo & Lau 2016)
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Instead of scanning the entire environment and plotting a path for the robot to maneuver
to, a snapshot of its immediate medium-range obstacles and the target is acquired at
regular intervals, such as illustrated in Figure 4-16. Since only the immediate
surroundings are acquired, the process should be less resource consuming. Each pixel
of snapshot is examined for the robot’s proximity to the target and obstructions. The
results can then be used to bias the robot’s decision when it comes to deciding which
direction to turn when meeting an obstruction between itself and the target. Because of
this, the PSM’s reliance on calculating the artificial potential field to plot a traversal path
around obstructions is no longer necessary. This is replaced with a 1D array indicating
the direction leading towards the target with least amount of obstructions.
Figure 4-17: Illustration of the transformed depth map into horizontal form.
The goal of transforming the vertical depth map is to produce its 1-Dimensional array form
in the horizontal plane. Each element corresponds to a visible direction relative to the
robot field of view, as simply illustrated in Figure 4-17. The elements or cells numerically
indicate the likelihood of reaching the target with minimal obstructions if the robot moves
towards the corresponding directions. This likelihood can be represented by a sum of
attractive forces ‘𝐹𝐴𝑡𝑡 ’ (emanating from the Primary Target) and repulsive forces ‘𝐹𝑅𝑒𝑝’
(emanating from walls and obstructions) (Eq. 4-20).
𝑆𝑢𝑚 𝑜𝑓 𝑓𝑜𝑟𝑐𝑒𝑠 𝑖𝑛 𝑎 𝑐𝑒𝑙𝑙, 𝐹𝐶𝑒𝑙𝑙 = 𝐹𝐴𝑡𝑡 + 𝐹𝑅𝑒𝑝 (Eq. 4-20)
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The analogy of using linear spring potential energy may not be suitable for this adaptation,
because the sum of forces on each cell is not relative to the potential fields, but to direct
proximity of obstructions and the direction of the target. A closer analogy can be drawn
from Coulomb’s Law, which describes the interacting forces between two charged
particles (Eq. 4-21).
𝑆𝑐𝑎𝑙𝑎𝑟 𝑒𝑙𝑒𝑐𝑡𝑟𝑜𝑠𝑡𝑎𝑡𝑖𝑐 𝑓𝑜𝑟𝑐𝑒, 𝐹 = 𝑘𝑒
|𝑞1𝑞2|
𝑟2
(Eq. 4-21)
Coulomb’s Law states that electrically charged particles induce a magnetic field on each
other, being attractive or repulsive depending on the particles being opposite or like
signed. ‘|𝑞1𝑞2|’ is the scalar product of the magnitude of both charges, while ‘𝑟2’ is the
square of the distance between the particles. Just like the treatment of repulsive forces
from obstructions in PSM, the strength of the electrostatic force is inversely proportional
to the square of that distance. ‘𝑘𝑒’ is the electric force constant. Coulomb’s Law can be
used as an inspiration for defining a general form ‘𝐹’, for the attractive and repulsive forces
in this model (Eq. 4-22).
𝐺𝑒𝑛𝑒𝑟𝑎𝑙 𝑓𝑜𝑟𝑐𝑒 𝑓𝑜𝑟𝑚, 𝐹 = 𝐶𝑇
𝑟2 (Eq. 4-22)
The general force form shares similar nature to that of Coulomb’s Law, beginning with the
numerator ‘𝑇’ representing the sign to indicate a repulsive (negative unit) or attractive
force (positive unit). ‘𝑟2’ represents the square of a distance variable, which depends on
application. The constant 𝐶 is a scaling factor to adjust the significance of the force values
according to implementation.
To begin formulating the repulsive force model, the depth sensor’s camera view will have
to be revisited, as in Figure 4-18. The depth map pixels are referenced by ‘𝑈𝑉’ coordinates,
but not all rows will be considered for processing. This is because of lens occlusions and
environmental clutter that may constitute too much noise for the top and bottom rows of
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data. Thus, the upper/ceiling (G) and lower/floor (H) trims must be specified. These values
are determined through experimentation and varies according to hardware.
Figure 4-18: Revisited depth sensor camera view with top and bottom trims.
To process the repulsive force for a cell, the distance component consists of the average
depth values contained within a column of the depth map, limited by both ceiling and floor
trims (Eq. 4-23).
𝑟𝐷𝑒𝑝𝑡ℎ,𝑈 =1
𝑉∑ 𝐷(𝑈, 𝑉)
𝐻
𝑉=𝐺
(Eq. 4-23)
Referring to the general force form, the repulsive force ‘𝐹𝑅𝑒𝑝’ assigns a negative unit to
‘𝑇’, molding the repulsive force component as a subtractive value when solving for the
sum of forces ‘𝐹𝐶𝑒𝑙𝑙’. The magnitude of repulsive force for a cell is inversely proportional
to the square of the average depth value for a column of the trimmed depth map (Eq. 4-
24).
𝑅𝑒𝑝𝑢𝑙𝑠𝑖𝑣𝑒 𝐹𝑜𝑟𝑐𝑒, 𝐹𝑅𝑒𝑝 = 𝐶𝑅𝑒𝑝
−1
𝑟𝐷𝑒𝑝𝑡ℎ2 (Eq. 4-24)
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Since the dimension of the transformed depth array corresponds to the horizontal scale
of the depth map, the contents of each cell can be defined by ‘𝐹𝑅𝑒𝑝,𝑈’ (Eq. 4-25).
𝑅𝑒𝑝𝑢𝑙𝑠𝑖𝑣𝑒 𝐹𝑜𝑟𝑐𝑒 𝑜𝑓 𝑐𝑒𝑙𝑙 𝑈, 𝐹𝑅𝑒𝑝,𝑈 = 𝐶𝑅𝑒𝑝
−1
𝑟𝐷𝑒𝑝𝑡ℎ,𝑈2 (Eq. 4-25)
By having the repulsive force be inversely proportional to the square of the distance
between the robot and obstruction, the surface of the barrier will result in value closest to
-1. The constant ‘ 𝐶𝑅𝑒𝑝 ’ is used to experimentally adjust the scale of the repulsive
component for performance adjustments.
4.3.2.5 Adapting PSM for Transforming the Target Array
The attractive forces emanate from the Primary Target’s horizontal position ‘𝑈𝑃𝑇 ’ as
shown in Figure 4-19. The awareness of the target’s position can be expressed in the
form of a 1-dimensional Target array ‘ 𝑃 ’ that shares identical dimensions as the
transformed depth array. The magnitude of the attractive forces for its cells are inversely
proportional to the square of the distance between the cells’ indices and the Primary
target’s horizontal index ‘𝑈𝑃𝑇’.
Figure 4-19: Example of populating the Target Location array.
To begin, the distance component is calculated by finding the absolute distance between
the current cell and the index that corresponds to the target’s position, then incrementing
that value by one. This will ensure that processing the cell representing the target’s
position will not result in a division by zero when calculating for its attractive force (Eq. 4-
26).
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𝑟𝐸𝑙𝑒𝑚,𝑈 = |𝑈 − 𝑈𝑃𝑇| + 1 (Eq. 4-26)
Referring to general force form, the attractive force component assigns a positive unit to
‘𝑇’, molding the attractive force component as an additive value when solving for the sum
of forces ‘𝐹𝐶𝑒𝑙𝑙’ (Eq. 4-27).
𝐴𝑡𝑡𝑟𝑎𝑐𝑡𝑖𝑣𝑒 𝐹𝑜𝑟𝑐𝑒, 𝐹𝐴𝑡𝑡 = 𝐶𝐴𝑡𝑡
1
𝑟𝐸𝑙𝑒𝑚2 (Eq. 4-27)
Since the dimension of the target location array corresponds to the transformed depth
array, the contents of each cell can be defined by ‘𝐹𝐴𝑡𝑡,𝑈’ (Eq. 4-28).
𝐴𝑡𝑡𝑟𝑎𝑐𝑡𝑖𝑣𝑒 𝐹𝑜𝑟𝑐𝑒 𝑜𝑓 𝑐𝑒𝑙𝑙 𝑈, 𝐹𝐴𝑡𝑡,𝑈 = 𝐶𝐴𝑡𝑡
1
𝑟𝐸𝑙𝑒𝑚,𝑈2 (Eq. 4-28)
As with calculating for the transformed depth array, each cell of the Target array contains
an attractive force value that is inversely proportional to the square of the index distance
between itself and the square representing the target position. Thus, the cell representing
the target position will hold the maximum value of 1, whereas cells radiating away from it
will hold gradually diminishing values. The constant ‘𝐶𝐴𝑡𝑡’ is used to experimentally adjust
the scale of the attractive component for performance adjustments.
4.3.2.6 Resolving the Sum of Forces
By having both sets of attractive and repulsive force arrays, it is now possible to combine
them into a bias array that is used to provide a goal direction bias for the Wandering
Standpoint Algorithm (WSA) outlined earlier in this model. To achieve this while providing
flexibility of performance adjustments, a gain constant ‘ 𝐶𝐹 ’ is added to the present
equation for the sum of forces for each cell of the Bias array, ‘𝐵’ (Eq. 4-29).
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𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑠𝑢𝑚 𝑜𝑓 𝑓𝑜𝑟𝑐𝑒𝑠 𝑓𝑜𝑟 𝑒𝑎𝑐ℎ 𝑒𝑙𝑒𝑚𝑒𝑛𝑡 𝑖𝑛 𝐵, 𝐹𝐵 =𝐹𝐴𝑡𝑡 + 𝐹𝑅𝑒𝑝
𝐶𝐹 (Eq. 4-29)
Interpreting the values in the Bias array is a matter of identifying the index of the element
that holds the maximum value throughout the set. That index corresponds to the direction
that leads towards the target while presenting the least visually perceived medium-range
obstructions. Others range from very negative (expect most obstructions) to positive
values (indicating possible alternative routes) (Eq. 4-30).
max𝑖=0
𝐵𝑖 (Eq. 4-30)
4.3.2.7 Aligning the Bias and Sensor Arrays to Apply the Goal Direction Bias
This model is developed for the use with a mobile robot that has a pivoting head which
houses the depth sensor nest. Since the tracking strategy involves the sensor nest doing
its best to constantly turn and face the Primary Target, at most instances it will not be
aligned with the bottom half of the robot which houses the ranging sensors array and
drive systems. The Bias array must be transformed into the same reference point as the
Ranging Sensors array before the Goal Bias can be finalized.
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Figure 4-20: Example of the Bias and Sensor arrays alignment process.
Figure 4-20 illustrates this process in three steps. If the midpoint of the ranging sensor
array ‘S’ is the origin, the magnitude and direction of the angle mismatch between both
arrays is represented by ‘θ’. This information can be acquired via integration of a
potentiometer or other angle feedback sensor into the turning mechanism of the sensor
nest. Next, the index of the Bias (B) Array’s cell with the highest sum of forces can be
transformed by adding an offset that is based on the mismatch angle ‘θ’, adjusted using
a suitable scaling factor ‘𝐶𝐵’ (Eq. 4-31).
𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑖𝑛𝑑𝑒𝑥 𝑓𝑜𝑟 𝑠𝑒𝑙𝑒𝑐𝑡𝑒𝑑 𝑐𝑒𝑙𝑙 𝑖𝑛 𝐵𝑖𝑎𝑠 𝑎𝑟𝑟𝑎𝑦 𝐵 = 𝑈 + 𝐶 𝐵𝜃 (Eq. 4-31)
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The final step involves processing a set of results that represent the decision-making
tendencies of the robot when faced with a barrier between itself and the target. The
tendency towards favoring the direction indicated by the ranging sensor array is
represented by ‘𝛽’, while ‘𝛼’ represents the Bias array counterpart (Eq. 4-32).
𝛼 = 𝑘𝐵(𝑈 + 𝐶𝐵𝜃) (Eq. 4-32)
The tendency towards the direction indicated by the Bias array is determined by the
adjusted index for its selected cell. To recap, this cell represents the direction that is
perceived to lead towards the Primary Target with the least amount of medium-range
obstructions. The adjusted index ‘𝑈 + 𝐶𝐵𝜃’ is augmented by a tendency gain ‘𝑘𝐵’. The
tendency gain is applied experimentally for performance adjustments (Eq. 4-33).
𝛽 = 𝑘𝑆(𝑆𝑈) (Eq. 4-33)
The tendency value for favoring the direction indicated by the ranging sensors array is
similarly structured, but it’s defining component is the magnitude of the sensor reading
from the favorable side ‘𝑆𝑈 ’. Thus, the tendency to turn towards the ranging-sensor’s
favored side will be higher if the perceived short-range clearance of that side is bigger. A
tendency gain ‘𝑘𝐵’ is also experimentally applied.
With this model in place, the pathfinding decision making process is reduced to the
following 2 possibilities:
a) There is no obstruction between the robot and the target: align with the sensor nest
and move forwards.
b) Short-range obstruction detected. Check for target location then sample for
ranging and depth arrays. Either:
i. Both tendencies point to one direction: Turn towards that direction to
navigate around the obstruction.
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ii. Both tendencies point to opposite directions: turn towards the direction
indicated by the highest tendency.
Either outcomes from (b) is then used to re-orientate the robot before initiating any choice
of wall-following or obstacle avoidance algorithm. This ensures less change of a local
method leading into a possibly more complicated path or away from the target. This
research’s proposed model results in suggesting the best direction to initiate maneuvers
but leaves the choice of algorithm implementation open for individual robot customization.
4.4 EXAMPLE MODEL APPLICATION
This section illustrates basic examples of how the navigation model can help decide on
which direction to execute maneuvers to begin towards. Figure 4-21 starts with a baseline
scenario whether there is no obstruction between the robot and the target human. Even
though pathfinding mode is not engaged in this case, the example will help describe how
the decision-making process is done via calculations. The diagram shows the human
entity being within the right-side quadrant of the robot’s Field of View (FOV), so the human
tracking system is assumed to report her relative position as corresponding to index 3.
The target array sized at 10 elements (calculated in Eq. 4-27) shows the contents of
attractive forces as determined by its index distance from element 3. Split into Left and
Right halves, the totaled values (solely determined by relative position of the Primary
Target) indicate a higher tendency for navigation towards the Right.
Figure 4-22 builds upon the previous example by adding input from the proximity sensors
array which detected a single obstruction ahead of the robot. The Ultrasonic Sensors
Array shows the contents of this input (assuming the resolution of the FOV is also 10
elements). Eq. 4-33 is applied here to determine the repulsive forces resultant from the
immediate vicinity. Note that the repulsive forces are strongest in elements corresponding
to the detected obstacle’s proximity. By summing the Left and Right sided forces in
addition to the previously calculated attractive forces, the verdict still leans towards the
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Right-side. This indicates that the path of least resistance involves navigating towards the
right side of the obstacle.
Figure 4-21: Example Model Application 1 – No Obstruction.
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Figure 4-22: Example Model Application 2 – Single Primary Obstruction.
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Figure 4-23: Example Model Application 3 – Obstruction and Clutter Encounter.
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Figure 4-23 further extends the discussion by adding a clutter field in the Right-quadrant
of the FOV. Rudimentary proximity based local navigation that leans towards a right-sided
route will encounter the clutter field prompting additional routing calculations. This model
acquires the raw Depth Map and processes it into a horizontal average depth histogram,
via Eq. 4-23. This is then used in Eq. 4-25 to create the Depth Array of repulsive forces.
Once again, this is summed in Left and Right halves together with the sensor array
repulsive forces and the relative target position’s attractive forces. The resultant verdict
advices a Leftward route that will bypass having to contend with the detected clutter field
ahead.
4.5 ROBOT CONTROL PROTOTYPE IMPLEMENTATION
Once the multi-sensor fusion-based navigation model is formulated, it is integrated into
the design of the robot system in accordance to this research’s case study. Following the
findings and conclusions of Chapters 1 and 2, the proposed Robot-Based Injury
Prevention Strategy requires the use of a companion robot avatar to acts as a mediator
between a child and caregiver. This Injury Prevention Telepresence System was first
modeled in a Use Case Diagram shown in Figure 4-24.
The model depicts which events or actions in the physical environment will trigger the
functions of the robot, e.g. the child performing a dangerous pose, once detected by the
robot, will prompt it to notify the caregiver. At the time when this model was made, motion
tracking hardware limitations dictated that the subject must be facing the sensor device
for the pose recognition to function optimally. Because of this, the child’s orientation and
position are two separate stimuli that will prompt the robot to relocate itself so that the
target stays within the optimal conditions for the device’s operation. The caregiver may
request to engage telepresence mode at any time, while being provided updated
monitoring logs of the child target. This general expression of the required robot system
was refined into a Use Case model as the first stage for software design.
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Figure 4-24: Use Case model illustrating the general functions of the required robot
avatar.
4.5.1 Use Case Model
The interaction design of the system assumed that there are three active entities: the child
target, a guardian (parent or caregiver) and the robot (which acts as a pawn). The Injury
Prevention Telepresence System existed as an external component which perceive the
actions from the other entities and interacts with them via the robot pawn. The interaction
model is shown in Figure 4-25.
The child entity does not have direct interaction with the robot pawn. Instead, the robot
passively monitors the child’s activity (and in the event of detecting a dangerous action,
sending an alert) for the guardian entity. On the other hand, the guardian has direct control
over the system, manually initiating telepresence so that a conversation can be had with
the child entity via the robot pawn.
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Child
GuardianRobot
Monitor Activity
Alert Guardian
<<include>>
Have Conversation
Get Activity Report
Figure 4-25: The interaction model for the Injury Prevention Telepresence System.
The interaction model had helped define a set of six Use Case stories to develop the
required system functionalities, as listed in Table 4-7. The table lists functions both
rudimentary (the starting and stopping of the monitoring process) and primary
(autonomous monitoring, activity reporting, alert notification and initiation of telepresence).
The initiating actor, level of complexity and development priority are indicated for each
case.
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Table 4-7: Summary of generated Use Case stories.
Use Case ID Use Case Name Primary Actor Scope Complexity Priority
1 Begin Monitoring Guardian In Low 3
2 Monitor Activity Robot In High 1
3 Get Activity Report Guardian In Med 1
4 Alert Guardian Robot In Med 1
5 Have Conversation Guardian In High 2
6 Stop Monitoring Guardian In Low 3
Since this research is ultimately concerned with applying the novel navigation model to
the case study, only the Monitor Activity case will be highlighted. This Use Case can be
found in Table 4-8. The full scope coverage for the Injury Prevention Telepresence
System’s use case model can be found in Appendix A of this thesis.
Table 4-8: Use Case story encompassing the autonomous monitoring function.
Use Case ID 2
Application ANIMA
Name Monitor Activity
Description The Robot attempts to look for the Child, detect the Child’s current
activity and adds the result to the monitoring log.
Primary Actor Robot
Precondition The Robot is currently in Monitoring Mode.
Trigger Activation of Monitoring Mode.
Basic Flow 1. Robot checks to see if Child is visible.
2. Child is visible, so robot locks camera position.
3. Robot checks to see if front of Child is visible.
4. Child’s front is visible. Robot remains stationary.
5. Robot checks to see if Child matches any recognizable
activity.
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6. Child’s activity is recognized. Robot takes a picture and
adds entry to Monitoring Log.
7. Robot checks to see if entry type is dangerous.
8. Entry type is not dangerous, Robot resumes monitoring.
9. Robot checks to see if Monitoring Mode is deactivated.
10. Monitoring Mode is active, Robot repeats step 1 – 9.
Alternate
Flows
1 – Child is not visible.
2. a. Robot adds ‘Visibility Lost’ entry to Monitoring Log.
2. b. Robot begin wall-following room exploration.
2. c. Go to step 9.
4 - Child’s front is not acquired.
4. a. Robot adds ‘Orbiting’ entry to Monitoring Log.
4. b. Robot orbits locked position and avoid obstacles.
4. c. Go to step 9.
6 – Child’s activity is unrecognizable.
6. a. Robot takes a picture and adds ‘Unrecognized Activity’ entry
to Monitoring Log.
6. b. Go to step 9.
8 – Entry type is listed as dangerous.
8. a. Robot Alert Guardian.
8. b. Go to step 9.
This Use Case story is essential to the research effort as it centers on the robot’s ability
to autonomously follow and maintain the target child within the tracking system’s optimal
zone of detection. The story starts right after calibration is done and the monitoring
process has been started.
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The robot starts by scanning the environment to see if the target child is visible. Once the
target is acquired, the robot will remain stationary if the child is within the optimal detection
zone. The activity tracking system will continue operating, logging perceived actions. If a
dangerous activity is detected, the robot will search through its database of recorded
trouble profiles. Otherwise, it will continue statically monitoring until manually deactivated.
The story elaborates on the various deviations from the basic flow, e.g. if a child is no
longer visible, the robot will log the loss of tracking and immediately switch to physically
searching around the room. This behavior coincides with the navigation model’s Phase 1,
where the robot will perform a 360° scan of the room until the child’s IR Marker and Body
profile are reacquired for Subject Locking.
Another story deviation occurs when the child is no longer ‘facing’ the correct angle or is
not in the appropriate range of the optimal detection zone. This prompts the robot to enter
‘orbiting mode’ where it is expected maneuver around obstacles to get into a proper
monitoring location. Again, this behavior mirrors the outcome of the navigation model’s
Phase 2, where the robot adjusts its position and makes decisions on how to circumvent
obstacles using the least effort.
The Use Case stories have been instrumental in determining the sequence of actions
necessary for each of the Injury Prevention Telepresence System’s functions. This
information was used to distill the following activity model.
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4.5.2 Activity Model
The activity model of the system is expressed using UML flow charts. The primary
execution loop flow chart is shown in Figure 4-26.
[Monitoring Mode Active]
Monitor Activity
[Request for Activity Report] Get Activity Report
[Request to Alert Guardian] Alert Guardian
[Request for Conversation] Have Conversation
[None]
[None]
[None]
[Monitoring Mode Deactivated]
Figure 4-26: Main execution loop of the Injury Prevention Telepresence System.
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[Child Visible]
Lock Camera Position
[Front is Visible]
Set Strategy to Stationary
[Activity Recognizable]
Take Picture and Log "Recognized Activity"
[Activity is not Dangerous]
[Child is not Visible] Log "Visibility Lost" Set Strategy to Roam
[Front is Not Visible] Log "Orbiting"Set Strategy to
Orbiting
[Activity Unrecognizable]
Take a Picture and Log "Unrecognized
Activity"
[Activity is Dangerous]Request Alert
Guardian
Figure 4-27: Expression of the "Monitor Activity" subsystem.
The main execution loop represents the primary software operation sequence, directly
responding to the guardian’s commands and activating the corresponding function
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subsystems. These subsystems have been abstracted into individual models that can be
found in Appendix B. For furthering the design of the robot navigation system, the “Monitor
Activity” subsystem is excerpted in Figure 4-27.
The deviations in the corresponding Use Case story are graphically expressed clearly in
this subsystem activity model. The activation of this function begins with tracking the
position of the child. If the child is not visible, the robot will switch to a “Roam Strategy”
that can be satisfied using any existing room-searching algorithm.
The legacy requirement of facing the front of the child subject is present in this model.
However, technological advancement at this time has granted the availability of motion
tracking hardware that can operate without needing constantly face the front of the subject.
Hence, this portion of the model triggers whenever the child is outside the optimal tracking
range. This prompts the robot to active an “Approach Mode” instead of Orbiting. Again,
this coincides with the navigation model’s Phase 2 operation of approaching the child and
circumventing obstacles along the way.
Finally, the model encompasses dealing with the event of detecting a possibly injurious
activity. This event leads to a logging and notification action that will exit this subsystem
loop and return focus execution focus to the main operation loop where the appropriate
subsystem will be engaged in turn. Otherwise, the execution of this subsystem loops back
to checking for the child’s visibility once more.
With the conceptual control model defined, there is enough information for integration
with an existing vision-based injury detection model. The product of this integration is a
Class diagram depicting the proposed robot’s object-oriented software structure for the
robot, shown in Figure 4-28.
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4.5.3 Software Structure Model
The role of the activity tracking system was originally intended to be filled by an existing
vision-based monitoring project for disabled people. The IRESY 2.0 prototype performed
at 91.85% accuracy while mounted statically (Lau, Ong & Putra 2014). This system was
tasked with tracking the child’s activity and triggering the periodic reports to the main robot
control software, renamed to the AutoNomous Injury Mitigation Avatar (ANIMA).
The robot hardware control was modeled to be a separate entity so that ANIMA can be
developed as a generic top-level robot behavior controller that is not tailored to any
specific robotics products. This makes the system modular and workable with an
assortment of consumer and custom robot platforms. The robot hardware control entity is
named “RobotBase”. The robot platform for this case study is modified to have a rotating
sensor nest in its head, actuated using a turn table mechanism shown in the subsequent
hardware development section. The “RobotBase” implementation was planned as an
embedded microcontroller application.
The active IR Marker tracking subsystem from the Human Orientation Tracking system
was extracted and encapsulated into its own standalone subsystem, called the
“IRTrackingSystem”. No longer concerned with template matching for determining the
orientation of the wearer, this system concentrates on visibly locating the marker within
the room and instructing the RobotBase to center the robot head on the target’s position.
At the time of this software structure’s design, the IRTrackingSystem was decoupled from
IRESY 2.0, and was intended to be run using a separate compact PC solution.
The main robot control entity ANIMA is responsible for orchestrating the execution of the
robot’s primary functionalities, which include responding to activation and deactivation of
the monitoring mode, receiving data from IRESY and the IRTrackingSystem via
RobotBase as well as provide the telepresence provisions. ANIMA was also supposed to
directly instruct RobotBase for all actuations required during relocation, obstacle
avoidance and roaming.
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Figure 4-28: ANIMA System Structure.
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4.5.4 Development of the RobotBase Prototype Platform
The next step was to develop a physical prototype robot platform to function as a
foundation for rapid testing of the new Active IR Marker Tracking System, integration with
IRESY and cross-device communications with ANIMA on an external computer. The
survey on state-of-the-art indoor companion robots in Chapter 1 has shown that there is
an obvious similarity in design between the various case studies. Most of these robots
rely on a cylindrical multi-layer platform and dual-actuator variable drive systems to
maneuver around common rooms and furniture while minimizing impact in case of
collisions with obstacles and people.
The prototype was developed using a DFRobot HCR Mobile Robot Kit which provided a
basic set of these features. However, due to the planned use of two external computing
and sensor systems, additional levels and modifications are required. In addition, the
development of a custom actuated turn table mechanism and rotating robot head
compartment were also required. The custom parts were designed and fabricated using
laser-cut acrylic sheets. The schematics for these custom parts can be found in Appendix
C.
Figure 4-29: RobotBase development montage. (a) Drive and Controller assembly. (b) Reworked mounting and custom power distribution. (c) Completed ultrasonic sensor array. (d) 3D printed housings for the robot head. (e) Completed actuated turn-table
mechanism.
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Figure 4-30: Electronics component block diagram.
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Figure 4-29 shows a picture montage of the various development stages that was
undergone for the development of the RobotBase. Significant effort was spent to plan and
implement a signal network between embedded components, controller and the onboard
computer. A power distribution system was also created to cater to all 3 systems via
battery and hardline tethering during laboratory testing.
The component selection process was extended based on the system block diagram
shown in Table 4-1 and Table 4-2. The resulting electronics component layout is shown
in Figure 4-30. An Arduino Mega microcontroller development board was chosen as the
embedded robot control solution as it possesses the number of Input and Output ports to
facilitate communications between two computers, the variable drive system, the actuated
turn-table mechanism and the ultrasonic sensor array. The active IR marker tracking
system was intended to be managed by a System-On-a-Chip solution such as a
Raspberry Pi or an Intel Galileo system. Meanwhile, the ANIMA system will reside on an
onboard computer package such as a tablet PC or an Intel NUC unit. The resulting
detailed wiring diagram was created using EAGLE and can be found in Appendix D.
Figure 4-31: The completed version 1 of the RobotBase.
Figure 4-31 shows the first version of the RobotBase completed and ready to be used as
a platform for implementing the subsequent subsystems for testing. The platform was
built to accommodate 6 ultrasonic sensors, physical bump sensors, an actuated robot
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head with space for a depth-based motion capture device, an IR camera, two computing
units, and a variable drive system. The onboard computer provides wireless connectivity
for manual remote control while housing the ANIMA software for autonomous operation.
4.5.5 Development of the Human Activity Tracking System
The first application of RobotBase was to serve as a physical foundation for developing
a new version of the IRESY that is optimized for operating on a mobile platform. The
former IRESY system performs activity identification by manually comparing the body
skeleton to a set of predefined poses. While this may be effective in a statically mounted
setting, the system experiences drastic performance detriment due to changing lighting
conditions, rapid subject movement and shifting of the device’s FOV.
The new activity tracking system makes use of an updated version of the sensor hardware
that comes bundled with a suite of tools including the ability to train a neural network
gesture identification system based on AdaBoostTrigger and RFRProgress (Microsoft
2017). Using this feature, an attempt was made to train the system for identifying key
injurious activities as individual gestures, including punching, pushing, jumping and falling
(Tee, Lau, Siswoyo Jo & Wong 2016).
The most notable difference between IRESY and the new system is the integration of the
multi-sensor fusion-based navigation model’s Phase 1 functionality to augment the
activity tracking system. The depth-based motion capture device identifies humanoid
shapes as Body entities but does not have a reliable way to differentiate the identities
between the humanoids nor eliminate false detections. At the same time, the Active IR
Marker Tracking (AIRMT) system can track the relative position of the marker wearer so
long as the target is within its FOV. To recap, Phase 1 performs Target Locking by using
the AIRMT data to identify which detected Body corresponds to the target. Hence, only
the target will be the subject for human activity tracking. This new human activity tracking
system combined with mobile robot platform was renamed to the Companion Avatar
Robot for the Mitigation of Injuries (CARMI).
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For the pilot study, the system was trained by instructing a volunteer subject to perform
the injurious activities while being recorded within the device’s optimal zone of detection.
Each action was repeated 4 times per subject orientation (facing front (0°), left (-90°), right
(90°) and backwards (180°)). This ensures that the system’s database includes samples
of the subject performing the actions at different relative angles. A total of 20 samples for
each action were recorded as gestures. In more ideal circumstances, more sampling sets
should translate to better tracking accuracy since the neural network has more training
data.
Figure 4-32 demonstrates how the system operates during runtime. Once Subject
Locking is successful, the target performs an action which is perceived by the motion
capture device. This action is compared to each gesture stored in the database by means
of the neural network examining matches in skeleton pose configurations. The closeness
ratio between the captured action and a recorded gesture is expressed as a “Confidence”
value displayed in the yellow box (usually a value between 0 to 1.0). Similar actions like
punches and pushes may exhibit overriding confidences but can be easily differentiated
from jumps and falls.
Figure 4-32: Example of Visual Gestures sampling for identifying injurious actions. (Tee,
Lau, Siswoyo Jo & Wong 2016)
An experiment session was conducted using the database of 4 gestures, each refined
using only 20 samples. The subject was tasked to perform each of the gestures at varying
angles and distance within the device’s optimal zone of detection (58° cone at distances
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between 1.2m and 3m). The subject was also instructed to shift locations within a 110°
arch in front of CARMI to test the Subject Locking and turn-table centering operations.
The results are comprised of 30 samples for ‘punches’ and ‘pushes’, while ‘jumps’ and
‘falls’ have 20 recorded samples each. The increased sample sizes for ‘punches’ and
‘pushes’ were for more granular examination of the detection accuracy between the two,
because of the close similarities between the two gestures.
The results are tabulated earlier in Table 4-3, confirming the possible detection accuracy
issues between ‘punches’ and ‘pushes’, each registering only 36.667% and 63.333%
correct matches. Both gestures are largely similar, differing only in punches using a single
arm instead of both. Other aspects such as gait, body posture and head orientation during
execution of both gestures may have caused the dip in accuracy for ‘punches’ which often
get registered as ‘pushes’.
Figure 4-33: Functionality exhibition during PECIPTA 2015.(Borneo Post Online 2016)
‘Jumps’ and ‘falls’ presented favorable detection accuracies, at 90% and 80% tries
resulting in correct estimated gestures. The mean accuracy of the system being able to
identify correctly matching actions is calculated at 58.425%, which is above average.
Given that the samples were collected while the subject continuously moved and changed
orientation in real time, this result is optimistic. Possible optimization can be produced
with more training data to improve and refine the gesture profiles within the database.
The system was exhibited during the PECIPTA 2015 event and garnered positive
feedback from the public (Figure 4-33). The pilot experiment results confirm the
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improvements from applying the navigation model’s Phase 1 Subject Locking over the
initial performance of the human activity tracking system. The additional use of Visual
Gestures also helps in rapidly adapting the system to identify new actions when compared
to the former hard-coded profile approach.
4.5.6 Navigation System Implementation using MRDS
The successful construction of the RobotBase and the application of Phase 1 into the
CARMI system greenlighted the continued development of this multi-sensor fusion-based
navigation model into a robot control system. While this can be done by working on top
of the existing ANIMA software, a better alternative was required to overcome the
execution overhead resulting from sequential handling of the motion capture device,
cameras, ranging modules, and actuators. The solution was to switch to a decentralized
service-oriented architecture so that the individual subsystems of the robot may be
handled as separate threads concurrently.
Microsoft Robotics Developer Studio (MRDS) was selected as the development platform
because of its ready provisions for harnessing and simulating the functionalities of Kinect
- the motion capture device used during the human activity tracking system project. In
addition, building the CARMI robot system using MRDS will ease installation and usability
amongst the general populace are predominantly familiar with using Microsoft Windows
operating systems.
MRDS is described as a service-oriented robot control architecture consisting of the
Concurrency and Coordination Runtime (CCR) and Decentralized Software Services
(DSS) components. The CCR provides the syntax and a runtime framework that helps
developers create software services to handle individual robot hardware inputs and
outputs (Microsoft 2012a). These services communicate between each other using
CCR’s messaging system. Therefore, the timing issues and multitasking between
services are handled by the CCR.
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On the other hand, DSS is a communications service architecture that enables web
browsers and software applications interface with the CCR runtime. This allows custom
programs to extract real-time data and issue commands to the robot.
In addition to the CCR and DSS runtime environment, the software suite also includes a
proprietary visual programming language called VPL, and a physics-enabled Visual
Simulation Environment (VSE). VSE was instrumental as the simulations tool used for
testing the functionality and performance of this research’s navigation system.
Figure 4-34: Illustration of a standard telepresence robot services structure in MRDS.
(Microsoft 2012b)
Figure 4-34 is excerpted from the reference platform manual which serves as a guideline
for building indoor telepresence robots optimized for MRDS. The diagram shows an
example structure of services that the robot control system comprises of, including a
Dashboard software interface, interface services for communicating with the robot
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microcontroller and the Kinect device, as well as one for encapsulating the high-level
behavior of obstacle avoidance.
MRDS provides a modular robot operating system environment that can accommodate
transitions of control services between simulated and actual hardware system interfaces.
However, this research intends to gauge the performance of the navigation model as
implemented in the form of a simulated robot system. MRDS is primarily used as the
implementation vessel and validation tool in the form of the Visual Simulations
Environment (VSE). VSE provides a physics engine and visual editing facilities to rapidly
develop virtual environments for simulation scenarios.
The caveat of using MRDS 4 is that the software product had been discontinued since
2012. Setup and runtime issues arose due to incompatible or malfunctioning software
prerequisites such as legacy versions of DirectX, XNA and Silverlight. Nevertheless,
significant effort was invested in rendering the MRDS runtime sufficiently operational in
the current Windows 10 environment.
4.5.7 CARMI Navigation System State Machine
Following the definition of the navigation model and partial implementation during
development of the CARMI robot’s human activity tracking system, a robot system state
machine design has been established for construction using MRDS 4. Shown in Figure
4-35, the CARMI navigation system consists of two state machine loops that operate
concurrently.
The Subject Locking loop encapsulates the logic behind the existing human activity
tracking system. The robot head will be engaged in scanning mode until both the motion-
capture device and IR marker tracking system acquired the target and successfully
achieves locks onto the target. The robot head will swivel itself so that the sensor nest is
always centered onto the target. This process repeats itself whenever target visibility is
lost. Successful Subject Locking will trigger the execution of the Pathfinding loop.
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The Pathfinding loop switches between ‘Idle’, ‘Approach’ and ‘Pathfinding’ depending on
the distance of the target and whenever an obstacle is encountered during ‘Approach’. If
the target remains in the motion-capture device’s optimal detection zone, then it remains
‘Idle’. Else, the robot will center its body towards the sensor nest’s heading, then begin
moving towards or away from the target in ‘Approach’. If an obstacle is encountered, the
robot scans the short, mid and long-range vicinity and the ‘Path Decider’ decides on which
direction to begin maneuvering around the obstacle. This process was elaborated in the
navigation system model section.
Figure 4-36 illustrates the transformation of the state machine design into MRDS service
architecture. The implemented robot control and navigation system consists of 15 custom
services. Most of these services are responsible for interacting with built-in Application
Programming Interfaces (API) for the proximity sensors array, depth camera, IR camera,
hardware switches, variable drive system and the turn table actuation. A select handful
of services encapsulate the Phase 1: Subject Locking and Phase 2: Pathfinding
algorithms in the form of a nested state machine structure. The ‘AIRMT’, ‘Subject Locking’
and ‘Path Decider’ services were built to include UI controls that enable manual
adjustment of the variable constants.
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Figure 4-35: The CARMI navigation system state machine.
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Figure 4-36: Overview of the custom-build services for CARMI to be simulated using
MRDS and VSE.
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Figure 4-37: Example of the simulated CARMI and Child entities in VSE.
Finally, several services such as the ‘Simulations Engine’ and ‘Simulations Referee’ are
tasked for facilitating communications with VSE. These help stage custom-created
scenarios in which the CARMI, Child and Decoy entities can be placed into for testing the
performance of the navigation system. An example of a successful scenario set up in
VSE is shown in Figure 4-37.
4.6 CHAPTER SUMMARY
This chapter documented the evolution of the multi-sensor fusion-based navigation
system. Its inception stems from the initial application of the companion robot
development framework proposed in Chapter 1. The combination of COTS components
and amalgamation of existing technologies gave rise to the need for a workable robot
control system that can overcome the challenges of indoor navigation using only those
selections.
This model consists of two phases. Phase 1: Subject Locking utilizes an Active IR Marker
Tracking System (AIRMT) and a motion-capture device to lock onto a target for human
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following. Phase 2: Pathfinding was inspired by the Potential Field Method, Wandering
Standpoint Algorithm and Coulombs Law, a method of fusing sensor data for close, mid,
and long-range obstruction landscape was established. The landscape is a depth
snapshot of the surrounds between the robot and a human target, which is a product of
fusing data from proximity sensors, AIRMT and a depth camera. This information is used
by the robot to decide on turning left or right at meeting and obstacle, before proceeding
with avoidance maneuvers.
To test the applicability of this system, it was to be integrated into a case study of a
Companion Avatar Robot for the Mitigation of Injuries (CARMI). A pilot development effort
was undertaken to create a physical robot platform so that the application of Phase 1 can
be demonstrated. This was done successfully, with both acceptable human activity
tracking results and public reception.
The model is then implemented into the CARMI navigation system, using Microsoft
Robotics Developer Studio 4. This framework provided the runtime and simulation tools
to help create the CARMI navigation system as a real-time service-based robot control
system. From this point onwards, scenarios can be developed for testing the performance
of the implemented multi-sensor fusion-based navigation system.
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Chapter 5: TESTING AND BENCHMARKING RESULTS
5.1 INTRODUCTION
The previous chapter encapsulated the process of incepting the multi-sensor fusion-
based navigation model and its implementation using Microsoft Robotics Developer
Studio (MRDS). The model defined a set of two concurrently executing state-machine
loops that oversees the tracking of the target’s relative position and the robot’s relocation
into the Activity Tracking System’s optimal zone of detection, respectively. While the
development of a physical platform has helped validated the possibility of such a system
being built in physical space, the remainder of the model’s implementation and testing
are conducted via software simulation.
The purpose of this chapter is to document the simulation-based testing process, from
scenario design and implementation to runtime results. The objectives of this chapter are
as follows:
a) Identify the common indoor obstacle avoidance challenges.
b) Develop a set of simulation scenarios that reflect the listed challenges.
c) Create a testing plan to examine the functionality of the implemented navigation system.
d) Search and select suitable indoor robot navigation projects for use as performance benchmarks.
e) Develop scenarios equivalent to test environments used in the benchmark studies.
f) Conduct benchmark scenario simulations and compare performance between this system and the benchmark studies.
This research aims to demonstrate that the system can successfully perform autonomous
obstacle avoidance and path selection for human-following via functional testing. Next, it
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seeks to demonstrate the potential of this navigation model in exceeding the human-
following performance of other studies’ systems. This is carried out by recreating the
benchmark studies’ experiment setups and running this prototype system according to
those mission parameters. Thus, the travel distances of both human and robot entities
from both benchmarks and prototype can be compared to discern performance
differences. The recreation process can be carried out without much difficulty due to
VSE’s visual based editing and real-time simulation environment.
5.2 FUNCTIONAL TESTING PLAN
To recap, an indoor robot’s navigation system must be able to guide the robot through a
room and around its contents without any collision. The contents consist of both static
and dynamic obstacles, so relying on periodic snapshots of the environment is not enough.
The system can only utilize on-board sensor suites and contend with their limited
perception of its immediate surroundings. Since the navigation system is aimed for use
by companion robots, its goal is to circumvent the environment so that the robot can
maintain a set proximity from the target while ensuring line-of-sight.
Figure 5-1: Typical indoor environment simulated with VSE (Microsoft Corporation
2012).
Figure 5-1 shows a default living room scenario from the MRDS simulation suite called
the Visual Simulation Environment (VSE). The contents of this example can represent
most furniture and objects encountered in most rooms in a house where a companion
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robot can be expected to operate in. Figure 5-2 helps segregate the myriad of common
obstacles into three categories based on size and shape. High obstacles present
maximum hindrance in both mobility and visibility – dining tables and people break line-
of-sight and must be navigated around. Medium obstacles such as stools and shoe racks
also need to be navigated around but does not hinder the visual tracking of the target.
Low obstacles can be driven across or pushed out of the way. The navigation system
must accommodate these encounters, but it is assumed that an environment suited for
companion robot navigation will have minimal number and prudent placement of high
obstacles. Thus, the challenge is less about maintaining line-of-sight and more towards
circumventing obstacles to escort a target.
Figure 5-2: Classification of indoor obstacles. (Tee, Lau, Siswoyo Jo & Lau 2016)
A further classification step is taken by separating the obstacles based on shape. Thus,
some are uniform shaped (such as tables, stools, etc.) while others are non-uniform
(sofas, bookshelves etc.). Figure 5-3 (a) shows the baseline scenario created in VSE.
This empty scenario includes only the CARMI entity, a Child target and two Decoy entities.
The baseline was previously used to test the functionality of the Activity Tracking System
and the Navigation System’s target tracking loop. It was used to fine-tune the ability to
differentiate between the Decoys and Child entities by fusing the Active IR Marker and
Kinect body detection system feeds.
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Figure 5-3: A baseline and six obstacle scenarios. (Tee, Lau & Siswoyo Jo 2018a)
Building upon the baseline, scenarios (b) and (e) represent the encounters with uniform
and non-uniform obstacles respectively. When faced with a uniform obstacle, navigating
to either side of the block will present negligible difference if effort. But considering a
scattered group of obstacles to the left (c) or right (d) ahead of the uniform block, the
navigation system should be inclined to choose the side with least future impedances.
When facing a non-uniform obstacle (e), there is a big possibility that one or both the
edges of the obstacle are not visible to the robot. If an edge is visible, it should choose
that direction to begin wall-following so that it can be circumvented faster. However, the
presence of scattered obstacles ahead ((f) and (g)) could influence it to choose the longer
edge.
Now that the scenarios have been created via VSE, the testing metrics need to be defined
to validate the system’s ability to help CARMI choose a path of least effort/resistance
while approaching the optimal proximity to the target Child. In this case, the best path
leads CARMI to the Child in the least amount of time or distance. There are only two
possible horizontal routes around any obstacle (left or right of the block).
146
Figure 5-4: Baseline Scenario Sample. Unit for X-Z world coordinates in meters (m).
147
To supply simulation data, the navigation system is implemented with separate logging
facilities for the ‘Referee’ and ‘PathDecider’ services. The ‘Referee’ service logger outputs
the 2D location for both the CARMI and Child entities, while the ‘PathDecider’ service
logs the numerical content of the Left/Right tendency arrays and the path-decisions it
makes during each timestamp. When put together in a scatter-graph and overlaid with a
rough overhead screen-cap of the scenario, it is possible to chart the motion-path of
CARMI towards the Child, and examine the decisions made by the navigation system at
each point. The runtime for each sample can also be extracted from the logs for
documentation purposes.
Since the output results are graphical in nature, a Plot Digitizer is used to approximate
CARMI’s travel distance in each sample. An alternate route is charted and digitized to
present the opposing travel distance so that a performance comparison can be made.
The navigation system is examined for its consistency in deciding on the shortest path in
each scenario. An example of a runtime output can be seen in Figure 5-4. A composite
graph of all samples is shown and overlaid with an overhead view of the scenario in (a).
Scenario (b) shows a single sample log with both ‘Referee’ and ‘PathDecider’ logs
staggered to coincide based on timestamps. The following section will present the
functional testing simulation results for the six obstacle scenarios.
5.3 FUNCTIONAL TESTING SIMULATION RESULTS
All 7 scenarios as described in the previous section (Figure 5-3) were created and used
for an average of 5 runtimes each. Data collection for each runtime takes place in the
form of ‘Referee Log’ (containing positional data for simulated entities) and ‘PathDecider’
Log (containing tendency array contents and ‘PathDecider’ verdict), extracted from the
two similarly named services. The logs are combined and staggered according to a
universal timestamp, so that a record of tendency array contents and the resultant path
decisions can be correlated with the position of the CARMI robot and Child at each interval.
The combined results log and the graphed entity positions for all samples are compiled
in Appendix E. To help illustrate the findings, one of the sample datasets for each scenario
148
is presented here, along with a composite graph showing the CARMI paths from each
sample.
5.3.1 Scenario: Single Uniform Obstruction
For the first attempt at collecting functionality test data, a total of 10 samples had been
collected. The combined paths for the remaining 7 tries can be found in Figure 5-5.
Figure 5-5: Combined CARMI paths during single uniform obstruction test.
While both paths around a uniform obstruction should be indifferent to each other, the
navigation system seems to favour taking the left-side (5 out of 7 samples). Table 5-1
shows an excerpt from the logs of the Trial 1 sample. Areas highlighted in red indicate
timestamps where not all services have successfully loaded yet. Runtime countdown
begins after all services are operational.
9
10
11
12
13
14
15
3456789
Trial 1
Trial 2
Trial 3
Trial 4
Trial 5
Trial 6
Trial 7
Child
149
Table 5-1: Excerpt from the combined logs of Uniform-Obstruction-Clear Sample 1. Unit for X-Z world coordinates in meters (m).
The sets highlighted in yellow indicates the point where the navigation system
acknowledges the presence of an impending obstruction and evaluates the contents of
150
the Left and Right tendency arrays. Note that there was a strong preference towards the
Left between timestamps 4:25:19PM – 4:25:26PM. This may be the result of the system
considering the presence of the right-handed Decoy. This entity was placed slightly ahead
of the Child and the other Decoy, thus was registered as another obstruction ahead. This
prompts the system to favour the left-sided path which will potentially avoid that Decoy.
Table 5-2: Elapsed time for each sample in Uniform-Obstruction-Clear.
Samples Runtime (seconds)
Trial 1 60
Trial 2 43
Trial 3 48
Trial 4 47
Trial 5 53
Trial 6 42
Trial 7 45
Average Runtime 48.286
Finally, the elapsed time from each sample is presented in Table 5-2, indicating that the
average time it took for CARMI to complete an approach towards the Child while
encountering a single uniform obstruction is roughly 48s. Elapsed time in this case, is
largely influenced by the choice of actuators, robot build and the VSE physics settings. It
is recorded from the moment all services are activated until CARMI settles into the final
position. While elapsed time may not be indicative of performance at this stage, it will
become useful for comparing benchmark results in the later sections.
The results of the first scenario show that even in a simple encounter with a uniform object,
the navigation system also considers the proximity of nearby obstructions beyond its
immediate surroundings when deciding on which path direction to take. This alone
indicates marked improvement over most rudimentary navigation alternatives which
would have produced random outcomes since both paths do not present obvious
151
differences. The datasets for the remaining samples can be found in Appendix E. The
next few scenarios explores this capability further.
5.3.2 Scenario: Uniform Obstruction with Scattered Obstacles on the Left
The next scenario is a modification of the uniform obstruction, with inclusion of scattered
objects ahead of the primary one. This scenario’s scatter field is placed towards the left
of the field, so that the navigation system can contend with the additional depth objects
in mid-range. Presumably, the CARMI robot is expected to approach the Child and
encounter the uniform obstruction as before. However, with the inclusion of the scatter
field, it should consider the proximity of all perceived mid-range objects then produce a
right-biased tendency profile.
This behaviour was confirmed upon the completion of the five runtime sampling rounds,
resulting in the combined motion path graph as shown in Figure 5-6 (a). This time, all five
samples showed that CARMI veered towards the right-side of the main obstruction, even
with inclusion of the right-handed Decoy. Examining the excerpt log in Figure 5-6 (b), the
timestamps around the yellow-highlighted portion shows a strong inclination towards the
right. This indicates that the combined sensor fusion data perceived which the clearer
side is long before reaching the point of decision-making.
152
Figure 5-6: Combined motion graph and sample dataset for Uniform Obstruction with
Leftward Scatter. Unit for X-Z world coordinates in meters (m).
153
Figure 5-6 (a) also shows the results from Plot Digitizing both the average sample route
and the longer alternative. The favoured right-side route average at 3.234m when
compared to the route that had to travel 4.185m to circumvent the scatter field before
reaching the position for the optimal zone of detection. This behaviour clearly confirms
the sensitivity of the navigation system as first observed in the previous scenario.
Table 5-3: Elapsed time for each sample in Uniform-Obstruction-with-Left-Scatter.
Samples Runtime (seconds)
Trial 1 26
Trial 2 48
Trial 3 47
Trial 4 57
Trial 5 52
Average Runtime 46
The elapsed time for each of the five samples are tabulated in Table 5-3, along with an
average runtime of 46s. As before, all datasets can be found in Appendix E.
5.3.3 Scenario: Uniform Obstruction with Scattered Obstacles on the Right
The third scenario was also created to examine the robot navigation system’s response
to a scatter field ahead of the primary object. This time, the field is positioned to the right.
Since the system’s behaviour correctly guided the robot towards the side that mirrors the
scatter field, this scenario will help reinforce this validity by switching the side of the field
placement. Again, it is intended that the system help guide CARMI towards the left side
of the uniform obstacle, thus ending up on the shorter path.
As with the previous attempt, a total of five successful samples were collected to
supplement this functionality examination. The resultant motion paths from all samples
154
can be seen in the combined graph in Figure 5-7 (a). As anticipated, the CARMI entity
sided towards the left for all samples. Overviewing the excerpt from the log in Figure 5-7
(b), it can be observed that the system had a clear bias towards the left once the mid-
range view-space acquired the presence of the field as well as the right-most Decoy. This
predictably resulted in a strong bias towards the clearer left side of the obstruction. The
sample datasets are in Appendix E.
Table 5-4: Elapsed time for each sample in Uniform-Obstruction-with-Right-Scatter.
Samples Runtime (seconds)
Trial 1 45
Trial 2 44
Trial 3 39
Trial 4 45
Trial 5 39
Average Runtime 42.4
The plot digitization of the combined graph also shows that the average travelled path
measures 2.815m vs the estimated alternate route of 4.226m. Compared to the Left-
Scatter Scenario’s obstacle placement, this setup presented a much clearer shortest-path
to the robot. The elapsed time for all samples can be found in Table 5-4. The average
period of this scenario’s samples is 42.4s. The runtime differences between all three
uniform obstacle scenarios were not expected to be significant since only the
circumvention of the primary object is necessary before CARMI reaches the optimal
proximity to the Child entity. However, the following three scenarios are expected to
produce clearly differing runtime as a non-uniform object will be used as the primary
obstacle.
155
Figure 5-7: Combined motion graph and sample dataset for Uniform Obstruction with
Rightward Scatter. Unit for X-Z world coordinates in meters (m).
156
5.3.4 Scenario: Single Non-Uniform Obstruction
The third and subsequent scenarios will help examine the navigation system’s capability
of assessing the short and mid-range obstacle landscape when encountering a non-
uniformly shaped large obstruction, such as a bench or a short counter. To begin, a bare
environment with just the non-uniform obstruction is presented, along with the usual setup
for CARMI, Child and Decoy entities.
Figure 5-8: Combined CARMI paths during the single non-uniform obstruction scenario, along with plot digitization of the alternate route and their approximated travel distance
comparisons.
This time, when CARMI approached the obstacle in the attempt to close the distance
between itself and the Child, it arrived at the decision-making trigger point near one end.
This setting and the composite travel paths for all five samples are presented in Figure
5-8.
157
Table 5-5: A runtime sample excerpt from the combined logs of the Single Non-Uniform-Obstruction scenario. Unit for X-Z world coordinates in meters (m).
158
The results show an 80% tendency for the system to favour the right-sided route, as
portions of the right-end of the obstacle was visible within the sensor’s Field of View.
(FOV). However, one in five samples show that CARMI opted for the left-side route. One
possible explanation for this outcome is that the robot approached the obstacle at an
angle which obscured the nearer right-end. Given that the contents of the FOV showed
no clear ways around the immediate obstruction, the navigation system evaluated the
proximity of the Child and Decoy entities. Since the right-sided Decoy is nearer to the
robot, the navigation system decided to take the opposite direction. Table 5-5 shows an
excerpt from one of the sample’s adjusted logs. The highlighted timestamps indicate the
moment when the navigation system approached the trigger point for decision-making. It
appears that while the robot was sufficiently farther away, it sees the right-end of the
obstruction and tendency array verdicts favour the right-sided route. However, as it
approaches the obstruction, verdicts sway between left and right since no clear option is
present. With the presence of additional scatter fields, this result may be influenced, as
observed in the following scenarios.
Table 5-6: Elapsed time for each sample in Single Non-Uniform-Obstruction scenario.
Samples Runtime (seconds)
Trial 1 60
Trial 2 40
Trial 3 92
Trial 4 41
Trial 5 40
Average Runtime 54.6
Table 5-6 presents the elapsed time for all five samples collected throughout the single-
non-uniform obstruction scenario runs. Due to a momentary service delay during Trial 3,
the average runtime for this experiment is 54.6s. The average runtime is only slightly
longer than the previous uniform obstruction scenario tests, but this is due to the small
gap between the right-end of the obstruction and the robot. Figure 5-8 also includes the
159
digitized route for the alternate path, which shows a clear difference in travel distance
between both choices (2.815 vs 4.226m). An increase in travel distance and a standard
robot travel speed translates to longer time needed for CARMI to circumvent obstacles.
The raw datasets for all samples can be found in Appendix E.
5.3.5 Scenario: Non-Uniform Obstruction with Scattered Obstacles on the Left
This scenario continues the examination of the navigation system on circumventing a
non-uniform obstruction, this time with the addition of a scatter field ahead. The scatter
field is placed on the left-side of the entities. This should help reinforce the robot’s
decision-making to favour the closer right-end of the obstruction to begin wall-following.
Figure 5-9 shows the combined motion path results from a total of six sample runtimes
for this scenario. All dataset results are in line with this expectation, as CARMI decided
on only taking the right-side route. The diagram also includes the plot digitized alternate
route, showing a much larger difference in travel distances due to inclusion of the scatter
field. If the robot opted for the left-side route, it will need to travel 5.829m before reaching
the correct proximity while achieving clear line of sight to the Child. Table 5-7 shows an
excerpt from one of the combined logs. Timestamp records leading to the decision-point
(highlighted entries) show a strong tendency towards a right-sided route. The presence
of the left scatter field and an indeterminate left-end of the obstruction resulted in a
composite left tendency array that overcomes the closer proximity of the right-handed
Decoy.
160
Figure 5-9: Combined CARMI paths during the non-uniform obstruction scenario with
left scatter field, along with plot digitization of the alternate route and their approximated travel distance comparisons.
Table 5-8 presents the elapsed time for each sample for this scenario. Like the single
non-uniform obstruction scenario, the average runtime for this scenario is 51s despite
presence of the scatter field. This is because in both scenarios, the robot strongly opted
for the same route (right-side), ignoring the lengthier left-end of the obstruction and the
left scatter field entirely. The next scenario attempts to induce a left-side route decision
by repositioning the scatter field to the right-half of the room.
161
Table 5-7: A runtime sample excerpt from the combined logs of the Non-Uniform-Obstruction-with-Left-Scatter-Field scenario. Unit for X-Z world coordinates in meters
(m).
162
Table 5-8: Elapsed time for each sample in the Non-Uniform-Obstruction-with-Left-Scatter-Field scenario.
Samples Runtime (seconds)
Trial 1 67
Trial 2 45
Trial 3 46
Trial 4 51
Trial 5 50
Trial 6 47
Average Runtime 51
5.3.6 Scenario: Non-Uniform Obstruction with Scattered Obstacles on the Right
The final functionality testing scenario involves placing the scatter field in the right-half of
the room, between the Child and Decoy entities as well as the main non-uniform
obstruction. Figure 5-10 shows this arrangement, along with the combined motion paths
taken by CARMI throughout four sample runtimes. In contrast to the previous scenarios,
CARMI exhibited strong decisions on taking the left-side route around the main
obstruction. Considering the placement of the robot at the beginning of the test, it will
perceive a hint of the right-end being the closest edge, before reaching the position for
decision-making.
163
Figure 5-10: Combined CARMI paths during the non-uniform obstruction scenario with
right scatter field, along with plot digitization of the alternate route and their approximated travel distance comparisons.
164
Table 5-9: A runtime sample excerpt from the combined logs of the Non-Uniform-Obstruction-with-Right-Scatter-Field scenario. Unit for X-Z world coordinates in meters
(m).
Referee Log PathDecider Log
Time-Stamp
CARMI Child
Time-Stamp
Tendencies
Verdict X Z X Z L R
10:48:28 AM 6 9.5 6 14.5 10:48:29 AM 6 9.5 6 14.5 10:48:30 AM 6 9.5 6 14.5 10:48:31 AM 5.99592 9.500831 6.000019 14.4998 10:48:32 AM 6.009068 9.629572 6.000019 14.4998
10:48:33 AM -132.71 -121.923 Right
10:48:34 AM 6.018241 9.722364 6.000019 14.49996 10:48:34 AM -134.793 -120.342 Right
10:48:35 AM 6.018951 9.722609 6.000019 14.49997 10:48:35 AM -132.379 -120.669 Right
10:48:36 AM 6.019325 9.72325 6.000019 14.49997 10:48:36 AM -131.267 -122.602 Right
10:48:37 AM 6.020663 9.723968 6.000019 14.49996 10:48:37 AM -132.982 -121.241 Right
10:48:38 AM 6.021132 9.724427 6.000019 14.4998 10:48:38 AM -132.61 -121.392 Right
10:48:39 AM 6.022233 9.724319 6.000019 14.49996 10:48:39 AM -130.948 -122.342 Right
10:48:40 AM 6.022468 9.724886 6.000019 14.4998 10:48:40 AM -130.999 -121.761 Right
10:48:41 AM 6.022222 9.767302 6.000019 14.49985 10:48:41 AM -130.463 -121.9 Right
10:48:42 AM 6.016362 10.10283 6.000019 14.49985 10:48:42 AM -130.08 -125.872 Right
10:48:43 AM -125.861 -126.63 Left
10:48:44 AM 6.00977 10.48014 6.000019 14.49996 10:48:44 AM -117.647 -125.408 Left
10:48:45 AM 6.00391 10.81541 6.000019 14.49985 10:48:45 AM -108.618 -122.762 Left
10:48:46 AM 5.987459 11.06953 6.000019 14.49996 10:48:46 AM -109.612 -123.527 Left
10:48:47 AM 5.973427 11.06703 6.000019 14.49985 10:48:47 AM -116.899 -114.422 Right
10:48:48 AM 5.96084 11.07514 6.000019 14.49985 10:48:49 AM 5.952713 11.08417 6.000019 14.49997 10:48:49 AM -125.39 -112.528 Right
10:48:50 AM 5.985577 11.10983 6.000019 14.4998 10:48:50 AM -134.342 -108.657 Right
10:48:51 AM 6.028952 11.12534 6.000019 14.49985 10:48:51 AM -140.865 -110.024 Right
10:48:52 AM 6.087961 11.13871 6.000019 14.49996 10:48:52 AM -148.442 -114.168 Right
10:48:53 AM 6.13534 11.14533 6.000019 14.4998 10:48:53 AM -146.531 -109.406 Right
10:48:54 AM 6.191909 11.13982 6.000019 14.49997 10:48:54 AM -149.177 -109.559 Right
10:48:55 AM -155.428 -109.39 Right
10:48:56 AM 6.240949 11.13314 6.000019 14.49997 10:48:56 AM -158.071 -108.683 Right
10:48:57 AM 6.282056 11.11869 6.000019 14.49997 10:48:57 AM -159.71 -107.75 Right
10:48:58 AM 6.332334 11.09471 6.000019 14.4998 10:48:58 AM -159.748 -107.205 Right
10:48:59 AM 6.392854 11.07143 6.000019 14.49985 10:48:59 AM -158.088 -107.451 Right
10:49:00 AM 6.441599 11.0538 6.000019 14.4998 10:49:00 AM -158.056 -108.785 Right
10:49:01 AM 6.488751 11.03888 6.000019 14.49997 10:49:01 AM -159.709 -108.889 Right
165
10:49:02 AM 6.535773 11.02227 6.000019 14.49996 10:49:02 AM -160.04 -108.38 Right
10:49:03 AM 6.588978 11.00392 6.000019 14.4998 10:49:04 AM 6.636698 10.98684 6.000019 14.4998 10:49:04 AM -159.712 -109.505 Right
10:49:05 AM 6.680408 10.96762 6.000019 14.4998 10:49:05 AM -159.402 -108.284 Right
10:49:06 AM 6.72623 10.95802 6.000019 14.49997 10:49:06 AM -159.332 -107.888 Right
10:49:07 AM 6.77289 10.94839 6.000019 14.49997 10:49:07 AM -158.034 -108.114 Right
10:49:08 AM -157.738 -108.273 Right
10:49:09 AM 6.828001 10.93324 6.000019 14.49985 10:49:09 AM -157.738 -108.324 Right
10:49:10 AM 6.892042 10.92458 6.000019 14.49996 10:49:10 AM -157.022 -107.175 Right
10:49:11 AM 6.935096 10.91855 6.000019 14.49985 10:49:11 AM -156.743 -108.717 Right
10:49:12 AM 6.980083 10.90996 6.000019 14.4998 10:49:12 AM -157.483 -108.025 Right
10:49:13 AM 7.017516 10.90872 6.000019 14.49997 10:49:13 AM -155.416 -109.184 Right
10:49:14 AM 7.063556 10.90722 6.000019 14.49997 10:49:14 AM -155.407 -108.072 Right
10:49:15 AM 7.118571 10.89903 6.000019 14.4998 10:49:15 AM -156.236 -109.088 Right
10:49:16 AM 7.174786 10.89298 6.000019 14.4998 10:49:16 AM -156.419 -109.466 Right
10:49:17 AM 7.215933 10.88938 6.000019 14.49996 10:49:17 AM -156.236 -110.046 Right
10:49:18 AM 7.264478 10.89126 6.000019 14.49985 10:49:18 AM -155.73 -111.292 Right
10:49:20 AM 7.317478 10.88759 6.000019 14.49985 10:49:20 AM -156.758 -110.175 Right
10:49:21 AM 7.370412 10.88661 6.000019 14.4998 10:49:21 AM -156.802 -109.903 Right
10:49:22 AM 7.431563 10.88682 6.000019 14.49996 10:49:22 AM -156.407 -109.974 Right
10:49:23 AM 7.481662 10.88469 6.000019 14.49985 10:49:23 AM -156.403 -111.606 Right
10:49:24 AM 7.534972 10.89207 6.000019 14.49997 10:49:24 AM -157.08 -110.23 Right
10:49:25 AM 7.587667 10.88978 6.000019 14.4998 10:49:25 AM -156.744 -112.294 Right
10:49:26 AM 7.630478 10.89282 6.000019 14.49996 10:49:26 AM -157.09 -112.263 Right
10:49:27 AM 7.681082 10.89302 6.000019 14.49985 10:49:27 AM -157.731 -110.933 Right
10:49:28 AM 7.729269 10.88848 6.000019 14.4998 10:49:28 AM -159.713 -110.795 Right
10:49:29 AM 7.784951 10.88866 6.000019 14.4998 10:49:29 AM -159.737 -111.53 Right
10:49:30 AM -158.392 -110.936 Right
10:49:31 AM 7.830725 10.88566 6.000019 14.4998 10:49:31 AM -158.401 -110.239 Right
10:49:32 AM 7.890501 10.89056 6.000019 14.49985 10:49:32 AM -158.374 -110.504 Right
10:49:33 AM 7.939007 10.88898 6.000019 14.49997 10:49:33 AM -159.051 -111.423 Right
10:49:34 AM 7.986757 10.88636 6.000019 14.49985 10:49:34 AM -159.405 -111.284 Right
10:49:35 AM 8.039586 10.88745 6.000019 14.49996 10:49:35 AM -158.385 -109.397 Right
10:49:36 AM 8.094811 10.88987 6.000019 14.49985 10:49:37 AM 8.131251 10.89508 6.000019 14.49997 10:49:37 AM -158.523 -110.836 Right
10:49:38 AM 8.176563 10.90366 6.000019 14.49997 10:49:38 AM -155.523 -111.281 Right
10:49:39 AM 8.231293 10.91576 6.000019 14.49985 10:49:39 AM -152.779 -110.483 Right
10:49:40 AM 8.289344 10.94089 6.000019 14.49997 10:49:40 AM -153.349 -112.228 Right
10:49:41 AM -150.696 -110.81 Right
10:49:42 AM 8.345519 10.96112 6.000019 14.49996 10:49:42 AM -151.937 -110.94 Right
166
10:49:43 AM 8.389972 10.98054 6.000019 14.49985 10:49:43 AM -148.683 -111.759 Right
10:49:44 AM 8.431435 11.00328 6.000019 14.49996 10:49:44 AM -145.437 -111.531 Right
10:49:45 AM 8.469328 11.03376 6.000019 14.49985 10:49:45 AM -143.15 -109.378 Right
10:49:46 AM 8.503297 11.06351 6.000019 14.49997 10:49:46 AM -143.65 -110.372 Right
10:49:47 AM 8.532343 11.09614 6.000019 14.49996 10:49:47 AM -139.962 -113.601 Right
10:49:48 AM 8.55698 11.13955 6.000019 14.4998 10:49:48 AM -136.877 -107.859 Right
10:49:49 AM 8.586799 11.1834 6.000019 14.49997 10:49:49 AM -136.966 -110.191 Right
10:49:50 AM 8.60596 11.22488 6.000019 14.49996 10:49:50 AM -134.525 -112.419 Right
10:49:51 AM 8.620514 11.27305 6.000019 14.49997 10:49:51 AM -129.555 -106.039 Right
10:49:52 AM 8.636838 11.32127 6.000019 14.49996 10:49:52 AM -135.688 -106.03 Right
10:49:53 AM -138.026 -106.087 Right
10:49:54 AM 8.660669 11.37096 6.000019 14.49985 10:49:55 AM 8.680889 11.4193 6.000019 14.49997 10:49:55 AM -138.332 -105.745 Right
10:49:56 AM 8.696641 11.45739 6.000019 14.49985 10:49:56 AM -138.194 -105.767 Right
10:49:57 AM 8.714813 11.50523 6.000019 14.49997 10:49:57 AM -137.605 -105.56 Right
10:49:58 AM 8.733751 11.55114 6.000019 14.49997 10:49:58 AM -138.131 -105.691 Right
10:49:59 AM 8.753076 11.59394 6.000019 14.49985 10:49:59 AM -138.673 -105.829 Right
10:50:00 AM 8.768402 11.63455 6.000019 14.49985 10:50:00 AM -137.724 -105.804 Right
10:50:01 AM 8.777872 11.67525 6.000019 14.49996 10:50:01 AM -134.969 -106.888 Right
10:50:02 AM 8.787521 11.71886 6.000019 14.49996 10:50:02 AM -134.061 -107.205 Right
10:50:03 AM 8.797791 11.76655 6.000019 14.4998 10:50:03 AM -133.896 -106.367 Right
10:50:04 AM 8.798552 11.81849 6.000019 14.49996 10:50:04 AM -133.149 -107.397 Right
10:50:05 AM -131.328 -107.105 Right
10:50:06 AM 8.801516 11.87918 6.000019 14.49996 10:50:06 AM -128.457 -108.196 Right
10:50:07 AM 8.792281 11.92729 6.000019 14.49996 10:50:07 AM -125.662 -109.687 Right
10:50:08 AM 8.782799 11.97552 6.000019 14.4998 10:50:08 AM -125.944 -108.996 Right
10:50:09 AM 8.772814 12.01702 6.000019 14.49997 10:50:09 AM -120.094 -109.701 Right
10:50:10 AM 8.753618 12.06511 6.000019 14.4998 10:50:10 AM -120.897 -109.682 Right
10:50:11 AM 8.738868 12.10582 6.000019 14.49997 10:50:12 AM 8.71206 12.1451 6.000019 14.4998 10:50:12 AM -119.367 -109.212 Right
10:50:13 AM 8.68756 12.1848 6.000019 14.4998 10:50:13 AM -118.161 -111.606 Right
10:50:14 AM 8.671518 12.20762 6.000019 14.49997 10:50:14 AM -109.393 -112.875 Left
10:50:15 AM 8.502569 12.34295 6.000019 14.49996 10:50:15 AM -111.599 -112.478 Left
10:50:16 AM 8.243203 12.55266 6.000019 14.49996 10:50:16 AM -109.938 -114.899 Left
10:50:17 AM 8.059642 12.70112 6.000019 14.49996 10:50:17 AM -108.31 -115.247 Left
10:50:18 AM 8.059722 12.70107 6.000019 14.4998 10:50:18 AM -109.198 -115.031 Left
10:50:19 AM -109.188 -115.021 Left
10:50:20 AM 8.059722 12.70107 6.000019 14.49985 10:50:20 AM -109.185 -115.018 Left
10:50:21 AM 8.059722 12.70107 6.000019 14.49996 10:50:21 AM -109.191 -115.031 Left
10:50:22 AM 8.059722 12.70107 6.000019 14.49997
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Table 5-10: Elapsed time for each sample in the Non-Uniform-Obstruction-with-Right-Scatter-Field scenario.
Samples Runtime (seconds)
Trial 1 91
Trial 2 104
Trial 3 104
Trial 4 63
Average Runtime 90.5
The left-side of the obstruction extends beyond the boundaries of the system’s FOV, so
close-range depth evaluations will point strongly towards the right-side route. However,
the mid-range components register the presence of the scatter field and right-hand Decoy.
This can be observed by examining the timestamps leading to the highlighted decision-
point entries in the excerpted logs (Table 5-9). During this point, the tendency arrays show
that the combined presence of the scatter field and nearest Decoy is enough elements to
motivate the ‘PathDecider’ to attempt exploring the left-side route despite the indefinite
length of the main obstruction’s left-end.
The plot digitization in Figure 5-10 shows that both the paths present almost negligible
difference in distance (6.069 vs 6.756m). However, graphical examination of both paths
show that the right-side route will require more bouts of reorientation when compared to
the left-side. Elapsed time records for all four runtimes can be found in Table 5-10,
averaging at 90.5s. The sample datasets can be found in Appendix E.
5.3.7 Functional Testing Simulation Findings and Discussion
The baseline scenario was created to serve as the testbed for fine-tuning the behaviour
of the navigation system to perform Subject Locking in addition to serving as the initial
template for spawning subsequent obstacle avoidance situations. Repeated tests and
adjustments made during the baseline runtime has ensured that the simulated CARMI
robot, armed with the navigation system, can search and identify the correct target, before
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proceeding to approach the Child entity until she is within the programmed zone of optimal
tracking.
The first scenario recreates a basic situation where the robot encounters a medium
obstacle while performing the ‘Approach’ action. The target Child is still being tracked by
CARMI, but a direct route to reach the appropriate proximity between them is not possible.
The test presented a uniform block, examining the state machine’s ability to interrupt the
‘Approach’ state, engage the ‘PathDecider’ to evaluate the environment via all sensor
feeds, decide on a direction, then begin wall-following. This test was largely successful,
with 7 out of 10 runtime attempts completed without service failures. It was found that the
failures were due to insufficient time between runtimes to properly terminate and restart
every single service. In actual physical operation, this will not be an issue if the robot’s
operating system is correctly set up at the beginning of use.
The next two scenarios were created to examine the navigation system’s tendency array
functionality, and how the close-to-mid-range sensors feed are transformed, fused and
used to help decide on a direction that is least impeded. Since the main obstruction is
uniformly shaped, the close-range perception data is negligible. The scatter fields and
incidental closer proximity of the right-hand Decoy presents mid-range depth obstacles
that were factored into the tendency array calculations. Thus, the resulting decisions were
translated into the motion path results presented in the previous sections. All ten runtime
attempts resulted in the robot choosing the side opposite of the scatter field, indicating
100% confirmed functionality of the navigation system’s ‘Subject Locking’ and
‘Pathfinding’ state machines.
The next set of scenarios were aimed at examining the tuning reliability of the navigation
system’s performance variables. These affect the modifiers to each sensor feed as well
as how the tendency arrays’ contents are used to make a path decision. First, the non-
uniform obstruction scenario helps in testing the navigation system’s ability at
contemplating the trade-off between a direction that has an indefinite end and the other
which has a visible end, and a single Decoy obstruction ahead. The results show that the
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system has an 80% tendency to favour the closer right-end despite the presence of the
single Decoy. The single sample runtime that resulted in the left-side decision could have
occurred because the robot entity arrived before the obstruction at a skewed angle,
leading it to attempt the indefinitely long left-end.
The next two scenarios attempt to influence the navigation system’s ‘PathDecider’ in
reinforcing its decision on taking the nearer right-end or attempting the indefinitely long
left-end. The former is achieved by adding the left scatter field. On top of the close-range
sensors perceiving the nearer right-end, mid-range components register the leftwards
scatter field, solidifying the right-side route as the least impeded choice. The addition of
a right-side scatter field is aimed at convincing the navigation system to attempt a left-
side route. In every sample for all three scenarios involving a non-uniform obstacle, the
navigation system has performed according expectation. This translates to 100%
functionality correctness and reliability.
However, it must be noted that during the freeform simulation tests, a peculiar navigation
problem was discovered. Part of the navigation model’s implementation process includes
consideration of when to abort a wall-following routine in case the maneuver started to
lead the robot farther from the Primary Target. After deciding on a direction then initiating
wall-following, if the robot’s heading exceeds a threshold of 120°, the operation will be
terminated. The robot will be reoriented to face the child then reattempt evaluation of the
environment before deciding and reattempting the maneuver. This theoretically helps in
aborting a fruitless obstruction-circumvention from a side with indefinite end.
However, if the encountered obstruction has a plateau/lagoon shaped profile, the robot
may potentially get stuck in an infinite loop. To examine the existence and extent of this
condition, a scenario is built to represent the Local Minima problem. The layout is shown
in Figure 5-11, along with the runtime motion path of the robot. This result confirms the
existence of this condition in which the robot’s navigation system could not exit the lagoon
profile and fail to reposition itself so that the Child is within the optimal zone for tracking.
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Currently, there are two possible solutions to address this condition. First, the heading-
based abortion threshold can be removed entirely, allowing the robot to eventually
complete its wall-following routine despite the process resulting in a much longer journey.
The next solution is to include a timer-based leeway, allowing the robot to continue
pursuing the wall-following routine away from the child, but only until a set grace period.
Beyond that, the system will abort the operation and reattempt the pathfinding process.
Implementing any of these solutions will require a retooling of the existing scenarios and
is considered outside the planned scope for this research effort. It is assumed that in the
future physical implementation of the navigation system will include a requirement that
the furniture within operating environment be arranged to avoid such a plateau/lagoon
profile.
Figure 5-11: CARMI motion path logged during the Local Minima Problem recreation
scenario.
9
10
11
12
13
14
15
3456789
CARMI Child
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5.4 EXISTING INDOOR ROBOT NAVIGATION STUDIES AND BENCHMARK SCENARIOS SELECTION
The functional testing simulations have shown that a companion robot entity equipped
with the multi-sensor fusion-based navigation system is able to select a shortest (least
impeded) route around an immediate obstacle between itself and the target, by means of
considering a combination of close and mid-range depth profiles of its surroundings. The
system has been operating according to expected outcomes in 31 out of 35 simulation
runtimes, translating to a cognition-dependant decision-making accuracy of 88.571%.
However, this result only indicates the system’s ability to function correctly when
encountering single obstacles as separate events. Its pathfinding capability can only be
gauged by observing its behaviour when put through a more elaborate environment. In
addition, its actions need to be measured against suitable alternative robot navigation
projects so that a quantifiable measure of performance difference can be seen and
indicative of its advantages to developing future autonomous robot companions.
To carry this out, a literature survey was conducted and identified three studies that
presented alternative sensor-fusion methods and navigational algorithms which
contribute towards bettering autonomous indoor robot navigation. Each of the selected
works include a pathfinding and obstacle avoidance experiment to observe the
performance of their systems. This research attempts to recreate the setup of each
experiment in hopes of inducing a similar or better performance by using this proposed
navigation system instead.
5.4.1 Benchmark Study 1
The first benchmark study selected was a multimodal navigation system to enable
autonomous person-following for telepresence robots used in audio-video calls (Pang,
Seet & Yao 2013). The aim was to alleviate the robot controller from having to manually
drive the robot via telepresence, in addition to concentrating on speaking with an escorted
target. Doing this adds more cognitive load on the caller and can be avoided if the robot
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autonomously escorts the target from a leading, flanking or following position. This
navigation system operates on a fully vision-based combination of three depth cameras
that simultaneously records the colour HSV histogram of the target and uses this
information to perform subject tracking. Their system was able to perform the escort
function while avoiding obstacles using a version of potential field like how this research’s
navigation system processes perceive objects as sources of repelling forces.
Figure 5-12: Graphical test results of the Multimodal Person-Following System. (Pang,
Seet & Yao 2013)
Figure 5-12 shows the experiment setup that was used to test the escort and obstacle
avoidance capabilities of the telepresence robot. The navigation system was able to
emulate an escorting human by keeping the depth cameras centered on the target while
maintaining a set social distance between them. The outcome of the experiment showed
that the motion of the robot does not imitate the human target because of the need to
maintain it within the system’s optimal zone and distance for tracking. This caused the
motion path to be less conservative than necessary. The movement rate of the robot and
maintained distance between itself and the target were not stable because of fluctuations
in the navigation system’s calculations of the person’s orientation.
While the performance of this benchmark system is satisfactory for telepresence robots
escorting a human target and differentiating between him and other subjects by means
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of color histogram profiles, this method is highly susceptible to dynamic lighting conditions.
Changing lighting conditions and a crowded environment may jeopardize its following
performance if it loses LOS or erroneously follows the wrong person.
5.4.2 Benchmark Study 2
The next benchmark study attempts to tackle this challenge by presenting a robot
navigation system which tracks and follows a person in a crowded environment (Harada
et al. 2017). This is achieved by using yet another sensor-fusion technique involving a
depth camera and a Laser Range Finder (LRF). The implementation of this project is
done via RT Middleware which helps encapsulate the functionalities of this navigation
system into reusable packages that can be applied for other robots that support the same
framework.
Their navigation algorithm perceives and records only the torso depth data for use in
tracking the human target. Once this information is acquired, the system actively scans
each frame for a matching pattern that corresponds to that torso profile. This method
helps the robot identify the correct target from other adjacent human entities. The LRF is
used to acquire horizontal depth snapshots of the immediate surrounds, registering
furniture, humans and miscellaneous objects as obstacles to be avoided.
Figure 5-13: Experimental pathfinding test results of the Meemo robot for tracking a
person in a gathering. (Harada et al. 2017)
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Figure 5-13 shows the results of the study’s implemented robot navigation system at
following a designated human target around several obstacles. It was noted that while the
human-following action was consistent, the set proximity between both entities fluctuated
and could not be maintained due to the robot taking too long to reorient itself towards the
human target after avoiding obstructions.
This benchmark study helps represent a suitable comparison for examining the prototype
navigation system’s ability to improve pathfinding by use of a separately rotating robot
head which simultaneously tracks the human target while the body maneuvers around
obstacles. Also, it appears that this benchmark system relies solely on the visual depth
component for identifying the correct target human. This method is also susceptible to
environmental lighting interferences and hardware lens occlusions.
5.4.3 Benchmark Study 3
The third selected benchmark study concentrates on presenting a fuzzy logic robot
controller that autonomously makes path-direction decisions while exploring ambiguous
environments (Montaner & Ramirez-Serrano 1998). It only relies on a fusion of seven
ultrasonic ranging sensors but perceives the obstacles in the immediate environment as
repulsive forces, like the first benchmark and this research’s navigation system. The fuzzy
logic controller uses an inference engine to sort through the sensor array feed and
decides on the best path direction to take after encountering an obstruction.
While this benchmarks study does not involve any component of human following, it
presents an applicable navigation model for exploration robots that roam disaster
environments alongside human rescue workers. In this case, an explorer robot will mimic
the travel path of a human target while its fuzzy logic controller oversees circumventing
the obstacles around them. As a benchmark study, its test results represent how an
autonomously roaming robot will behave when following a human worker who is exploring
an unknown environment. The robot will attempt to stay close while choosing the best
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heading to avoid being stuck, however, the proximity and behavior of the controller forces
it to mimic the human’s motion path.
Figure 5-14: Experimental pathfinding results of the fuzzy logic controller's performance
simulation. (Montaner & Ramirez-Serrano 1998)
The research’s navigation system can track a target human’s position beyond simply
mimicking his motion path, but its performance over the latter must be proven through
comparative experimentation data. The study’s simulation scenario and robot path
(shown in Figure 5-14) will be used as a baseline sample to achieve this.
5.5 PERFORMANCE BENCHMARK SCENARIO DESIGN AND RESULTS
The testing environment in all three selected benchmark studies have been replicated via
the Visual Simulation Environment (VSE). The distances and size of the obstructions
used are approximated to varying degree depending on the availability of data garnered
from the results report from those studies. The three benchmark scenarios are shown in
Figure 5-15. Diagram (a) corresponds to the autonomous navigation system for
telepresence which examines escort functionality with simultaneous obstacle avoidance
(Pang, Seet & Yao 2013). Diagram (b) shows the replicated testing scenario for
examining the robot’s ability to track and follow a person through a crowded area (Harada
et al. 2017). Finally, Diagram (c) recreates the exploration zone for the robot to roam
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similar to the one used for testing the fuzzy-based controller (Montaner & Ramirez-
Serrano 1998).
Figure 5-15: Attempted replication of benchmark testing environments for performance
measurement.
Since the exact dimensions of the entities and the distances between them are not able
to be replicated, a direct route-to-route length comparison is not possible without adoption
of a standard scale. Instead, a motion ratio of robot-to-human travel path is used,
measuring the robot’s conservation of movement against the escorted human total
movement. For most rudimentary follower routines, the robot’s travel path will be almost
identical to the human’s, as what is determined to be the case for Benchmark 3.
Improvements can be made to reduce robot motion by idling while the human lingers
within close distance, approximating an interception heading to coincide with a walking
target and more. Hence, the benchmark tests aim to compare the degree of motion
conservation between the selected studies and this research’s navigation system. As
before, each scenario is run to collect a total of five samples. Since each scenario’s
motion path is elaborate, only one sample will be selected for discussion in this chapter.
The remaining results are situated in Appendix E.
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5.5.1 Benchmark 1 Simulation Results
First, the test results from the Multimodal Navigation System for Telepresence Robot
project (Pang, Seet & Yao 2013) is extracted and applied with plot digitization to
approximate the path lengths of both the Robot and Human. Part of this process is shown
in Figure 5-16, indicating that the study’s Human target estimated travel distance of
10.782m throughout the test. The full travel distances for both Robot and Human can be
found in Table 5-12.
Figure 5-16: Plot digitization of the Human motion path from the Multimodal
Telepresence Robot project. (Pang, Seet & Yao 2013)
Figure 5-17 shows travel paths from CARMI and the Child entities of a selected sample.
The Child entity was manually driven to follow the Human’s route from the benchmark
study. It can be observed that while the Human and Child routes are roughly similar, the
motion between both Robot entities are clearly different. This research’s navigation
system helped produced a more linear path while satisfying the proximity requirement for
escorting the target.
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Figure 5-17: A graphical path result from a selected runtime sample of Benchmark 1
performance tests.
Table 5-11 shows the plot digitized path lengths for both Robot and Human entities
throughout the 5 sample datasets. The average travel lengths are then calculated to show
the human-following performance in Table 5-12.
Due to the differences in system implementation, testing environment characteristics and
experiment conditions between the benchmark studies and this simulated prototype, a
direct comparison between entity travel distances will not yield suitable data to compare
human following performance. Instead, this research proposes using a ratio of Robot
travel distance per unit of Human travel distance. For instance, a direct Robot imitation of
Human travel will result in the baseline performance value of 1. An ideal robot will aim to
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maintain human-following while minimizing its motion when compared to the followed
subject (lower Following-Ratio being better).
The benchmark study’s system achieved a human-following value of 0.786, indicating
that the Robot moves a total of 78.6% of the escorted Human’s motion. This research’s
navigation system managed to complete the challenge with 0.6197, or almost 62% of the
Human motion. This shows that the multi-sensor fusion-based navigation system has
21.52% performance advantage over the benchmark study.
Table 5-11: Plot digitized travel distances for both Robot and Child entities for all samples of Benchmark 1 performance tests. Travel distances are measured in meters
(m).
Sample 1 2 3 4 5 Mean
Robot
Path
18.16 16.124 19.057 18.073 17.05 17.6928
Human
Path
26.553 28.594 30.587 28.321 28.713 28.5496
Table 5-12: Tabulated calculations of Robot-To-Human travel distances for both Benchmark 1 study and simulation results.
Digitized Path Benchmark Path Mean Simulations Path
Robot 8.475 17.6928
Human 10.782 28.5496
Following Ratio 0.786 (78.6%) 0.6197 (61.97%)
5.5.2 Benchmark 2 Simulation Results
The second performance benchmarking test is done using the recreated setup presented
in the study for robot navigation in crowded human gatherings (Harada et al. 2017). This
scenario includes a more elaborate obstacle placement and motion path for the Human
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entity. As before, the travel paths for both the Human target and the Robot was estimated
using plot digitization, as partially shown in Figure 5-18.
Figure 5-19 presents a graphical view of this research’s runtime travel paths from one of
the sample attempts. The Child entity was also manually driven to imitate the motion of
the Human target from the benchmark study. Observation of these results shows that the
manual mimicry was carried out slightly better than Benchmark 1, but the general Robot
travel paths between the benchmark version and CARMI does not present much
difference in routing.
Figure 5-18: Plot digitization of the Robot motion path from the Human-Following in
Crowded Environment project. (Harada et al. 2017)
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Figure 5-19: A graphical path result from a selected runtime sample of Benchmark 2
performance tests.
Table 5-13 lists the travel distances for both CARMI and the Child entities in all five
samples collected from the benchmark scenario simulations. The mean distances are
then used in Table 5-14 to determine the human-following performance difference
between benchmark study and simulation results. The benchmark study’s robot was able
to perform its escort duties at a performance value of 0.6938, or total movement
equivalent to 69.4% of the Human target’s. However, this research’s navigation system
had helped the CARMI entity complete the same challenge at a performance value of
0.4632. This translates to only moving 46.32% of the Child entity’s total motion, bringing
a performance increase of 33.26% over its benchmark study counterpart.
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Table 5-13: Plot digitized travel distances for both Robot and Child entities for all samples of Benchmark 2 performance tests.
Sample 1 2 3 4 5 Mean
Robot
Path
16.141 15.324 13.902 14.723 16.429 15.3038
Human
Path
31.803 32.141 32.453 34.337 34.467 33.0402
Table 5-14: Tabulated calculations of Robot-To-Human travel distances for both Benchmark 2 study and simulation results.
Digitized Path Benchmark Path Mean Simulations Path
Robot 12.936 15.3038
Human 18.645 33.0402
Following Ratio 0.6938 0.4632
5.5.3 Benchmark 3 Simulation Results
Benchmark 3 is intended to be an attempt to gauge the motion conservation performance
between this research’s navigation system and the benchmark study’s fuzzy knowledge-
based controller (Montaner & Ramirez-Serrano 1998) when applied to an exploration
robot armed with rudimentary human-following capabilities. It is assumed that this
assumed system would perfectly imitate the Human target’s heading and speed, resulting
in a human-following performance value of 1.0. Thus, the aim is to prove that the human-
following performance metric is effective in showing differences in terms of direct motion
conservation and pathfinding optimization.
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Figure 5-20 Plot digitization of the Robot motion path from the Fuzzy Knowledge-based
Controller project. (Montaner & Ramirez-Serrano 1998)
Figure 5-20 shows the excerpted test setup results from the benchmark study, along with
plot digitization of its travel path for approximating the total distance travelled. Figure 5-21
shows one of the sample results from running the benchmark simulations using CARMI
equipped with the multi-sensor fusion-based navigation system. As with the previous
benchmarks, the Child entity was piloted manually to roughly emulate the Human travel
route in the study. It can be observed that the route taken between the Robot and CARMI
entities differ in terms of amplitude and shape. The CARMI route seems to be of a
smoother, linear path when compared with the benchmark study version.
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Figure 5-21 A graphical path result from a selected runtime sample of Benchmark 3 performance tests.
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Table 5-15: Plot digitized travel distances for both Robot and Child entities for all samples of Benchmark 3 performance tests.
Sample 1 2 3 4 5 Mean
Robot
Path
15.15 12.893 16.021 16.183 15.685 15.1864
Human
Path
20.352 22.621 22.907 22.495 22.504 22.1758
Table 5-16: Tabulated calculations of Robot-To-Human travel distances for both Benchmark 3 study and simulation results.
Digitized Path Benchmark Path Mean Simulations Path
Robot 23.268 15.1864
Human Full Mimic – 23.268 22.1758
Following Ratio 1.0 0.6848
Table 5-15 lists the travel distances for both CARMI and Child entities in all samples
carried out in the simulation exercise. The mean values are used to populate Table 5-16
and calculate the human-following performance values for both the benchmark study and
this research’s navigation systems. As the benchmark system is assumed to be applied
with a perfectly imitative follower logic, its resultant motion is assumed to be the same as
the Human target, indicated by a value of 1.0 (the robot moves 100% equivalent to the
Human’s travel distance). This research’s system achieved a performance value of
0.6848, moving CARMI only 68.50% of the escorted Child’s total movement. This
improvement of 31.52% over the benchmark study directly corresponds to the graphical
difference in routing shape. Note that the simulation’s CARMI path in Figure 5-21 is less
pronounced and linear than the Robot’s path in Figure 5-20. Hence, this result shows that
the navigation system helps in reducing the travel length and navigation effort by a
magnitude of 31.52% lesser than rudimentary robot tethering or imitative human-following.
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5.6 CHAPTER SUMMARY
This chapter is instrumental in gauging the degree of functionality and performance
capabilities of the multi-sensor fusion-based navigation model with implementation in a
simulated environment. The simulation scenarios were designed to examine the model’s
response when encountering obstructions while performing human following. Thus, a set
of scenarios were created to examine the implementation’s ability to circumvent uniform
and non-uniform obstacles, as well as how its decision-making process is influenced by
the presence of a scattering of obstacles beyond the immediate object. The conclusion
of the functional testing scenario simulations reports that the system was capable of
88.57%. Accuracy in choosing the path directions that result in the least impeded travel
before reaching the proximity threshold for optimal tracking. Furthermore, three external
studies had been chosen for benchmarking human-following capabilities. Scenarios were
made to recreate the testing environments used in those three projects and the simulation
routines are carried out, this time, with the Child entity being manually controlled to imitate
the travel route of the benchmark Human counterparts. The results reveal a pathfinding
performance advantage of 21.52%, 33.26% and 31.52% respectively. This shows that
sensor-fusion of multiple person-tracking and proximity sensors at varying ranges can
beget a clear benefit to autonomous standalone human-following robots, over relying on
compartmentalized methods for separate person tracking and obstacle avoidance.
Publications had been carried out over the results from both function testing scenarios
(Tee, Lau & Siswoyo 2018a) and performance benchmarking simulations (Tsun, Theng
& Jo 2017; Tee, Lau & Siswoyo Jo 2018b).
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Chapter 6: CONCLUSION
6.1 INTRODUCTION
This research has come a long way, from initial literature surveys to overview the state-
of-the-art for companion robots and identifying common challenges in implementing their
human-following capabilities to developing and testing a potential navigational solution
for them. This chapter discusses the accomplishment of the research questions and
objectives and addresses them according to the findings and efforts invested throughout
the project. Following this, a discussion over the limitations of the implemented system
and possible improvements as future work is presented.
6.2 CONTRIBUTIONS
Recapping the inception of this research, the aim was to find out what was the reason
behind companion robotics not being Commercial-Off-the-Shelf (COTS) despite the
prevalence of Assistive Technologies applied to elderly care, aiding the disabled and
accompanying children with cognitive disabilities. This occurrence contrasts with
domestic access to novelty telepresence robots and various autonomous robots
marketed as intelligent household appliances. Early literature survey revealed that
although there are a multitude of research publications related to assistive robotics and
autonomous robot operation, there are rarely any documented build characteristics
shared amongst them. Without knowledge of enough common characteristics in hand, it
is difficult to implement any form of indoor companion robot platform that can cater to the
variety of applications. The contributions are summarized in the following sub-sections.
6.2.1 Identification of Navigational Challenges for Indoor Companion Robots
This research studied how existing research works were using assistive robotics. The
literature review explored the projects that were applying general Assistive Technologies
to prevent injuries in cognitively disabled children. Those diagnosed with Cerebral Palsy
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(CP) and Autism Spectrum Disorder (ASD) had a tendency of experiencing self-injuries
due to inadequate motor control or stereotypy, falls being chief amongst these. The
automation hardware came in the form of smart wearables, embedded environments and
vision-based systems. Smart wearables were good for constant monitoring but risk of
being obtrusive to the user. Embedded environments were useful for overall environment-
wide localization but expensive and often unfeasible to implement. Vision-based
monitoring seemed to offer the best compromise but were plagued by optical hardware
weaknesses and the limited Field-of-View (FOV). These limitations could possibly be
lifted via integration into mobile robots.
Thus, the focus is on robotics as an Assistive Technology. Robotics, or simply automation,
were first commonly employed as technological augmentation to existing therapeutic
practices for the elder, disabled and cognitively impaired. Intelligent machines that
emulate dynamic challenges and adjust the difficulty levels by learning the user’s
performance histories helped revolutionize physiotherapy sessions. Some systems were
developed experimentally to one day allow patients to participate in sessions at home
without the physical need for a physician.
While studying about automated therapies, it was found that robotics gained popular use
in the field of social interaction rehabilitation for children with ASD. These children were
observed to have more affinity towards robotic constructs than with fellow human beings,
leading these machines to being used as surrogate therapists. The Triadic Interactions
model describes how a robot “puppet” can be remotely controlled by a therapist to gain
the child’s trust before gradually introducing a human playmate into the session.
Humanoid robots were also used to elicit responses in active play and gesture mimicking
exercises aimed at improving a cognitively impaired child’s social interaction skill.
Finally, the classification of companion robots arose as indoor autonomous mobile robots
that are intended for long term accompaniment to a user. These robots had to fully
function safely without full-time control by a human operator, able to maneuver around
typical human-populated rooms, and stay within a designated proximity from the primary
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user while carrying out its intended duties. These may include provision of iterative
therapy-reinforcement games, communication, and help in performing Activities of Daily
Living (ADL) or passive monitoring in case of injuries.
To identify the challenges faced during implementation and operation of companion
robots, a robot planning template was proposed. This template championed the inception
of applications built around a multi-purpose mobile robot that consists of COTS
components, making their construction rapid, economical and domestically possible by
the average consumer. However, there are two challenges that cannot be easily
overcome by this template: the need for a robust navigation system and a means for
reliable human tracking. These two problems come together as the main navigational
challenges that conventionally require exclusively developed solutions on project-to-
project basis. Incidentally, both these challenges are combined into what is referred to as
the autonomous human-following problem.
The indoor navigation problem consists of three dilemmas: location-specific, autonomous
wayfinding and hardware. The Location-specific dilemma describes the common indoor
operating environment as cluttered, dynamically shifting and populated by erratically
moving people. The Autonomous wayfinding dilemma encapsulates the difficulties in
applying theoretical pathfinding algorithms to inherently incomplete and rapidly
invalidated maps. Due to the location-specific dilemmas, it is practically impossible to rely
on techniques that work on full a priori knowledge. Utilizing reflex methods can be equally
ineffective because the randomness of shifting obstacles in real-world locations can lead
a robot into dead ends or be unable to maintain consistent proximity to the primary target.
Finally, the hardware dilemma dictates that real-world implementations of sensor and
actuation hardware have device-specific offsets and nuances that skew the input and
output of the robot’s wayfinding system. Some measure of real-time adjustment to both
the environment and robot hardware characteristics are required.
The human tracking challenge is described as a need for a way to consistently perceive
the primary user’s position, relative to the companion robot. When acquired effectively
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and consistently, this allows the robot to engage its navigation system and drive itself so
that it can always remain at a set proximity from its target. Outdoor unmanned drones
capitalize on a satellite-network to perform near-accurate localizations on Earth, called
GPS. Unfortunately, indoor environments lack clear access to the sky and must rely on
alternate means of tracking individual human position. This challenge is largely related to
the availability of less-obtrusive, easily acquirable motion capture devices that work
indoors, along with the inherent hardware limitations that these devices suffer from. The
solution to this problem is to find a way to mitigate said limitations, either via sensor
redundancy or an alternate means of acquiring indoor individual human position.
6.2.2 Design of a Novel Indoor Robot Navigation Model to Perform Real-time Human-following and Obstacle Avoidance
Beginning with the indoor navigation problem, possible navigation systems are usually
operated as either global or local methods. Global methods involve acquiring a complete
map of the operating environment, then utilizing a search or wayfinding algorithm to derive
a shortest path if at least one exists. There are several caveats to using this approach.
First, acquiring a full snapshot of the environment is difficult and requires possibly costly
technologies such as LIDAR. Despite numerous attempts at optimizations, the wayfinding
algorithms have always been heavily mathematical processes that require significant
computation. Finally, processing a complete snapshot only yields a path that stays valid
only while portions of the environment remains unchanged. Afterwards, the entire process
must be repeated. In the real-world, environments shift and change rapidly, thus making
global methods hard to justify. A notable global technique is the Potential Field Method
that represents the goal position as a source of positive force surrounded by obstacles
emanating repulsive forces. A snapshot of the environment translates into a potential field
where a trail of most positive sum of forces represents the best travel path between the
origin and the goal.
Local methods are inspired by biological creatures and how they find their way through
natural biomes while only perceiving their immediate vicinity. It is assumed that each
entity has sensory over a limited sphere around itself and must decide on the next
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direction to move towards based on this information. Multiple techniques range from
random selection to learning algorithms that build upon previous pathfinding history and
alternate interpretations of the Potential Field Method such as Vector Field Histogram
(VFH). This class of techniques incur less computation requirements and basic sensors
but suffer from the limited perception scope.
This research was inspired by the alternate local method adaptation of the Potential Field
Method, where perception of the immediate vicinity is translated into repulsive and
attractive forces. The potential path to take is indicated by the direction corresponding to
highest sum of forces. This technique is sufficiently reactive to the dynamic changes in
human-populated environments while being decoupled from defining any actuator or
sensor specific variables. However, current versions of this method such as VFH is every
sensitive to changing thresholds and still affected by the short-range limitations of its
proximity sensors. Additional layers of sensory scope are needed as input into this
Potential Field adaptation.
Meanwhile, the human tracking problem has been understood to be mostly caused by
hardware limitations. The advantage of modern companion robot projects today is the
commercial availability of vision-based motion capture devices. Often implemented as
monocular, stereoscopic or RGB-D depth cameras, these devices capture video frames
of the world as depth maps – 2D photographs with a distance component in each pixel.
Accompanying proprietary software makes use of the depth maps to identify human
bodies and estimate gesture profiles for a wide variety of human activity tracking. These
offerings provide a huge leap forwards from traditional systems that used to be exclusively
professional equipment that require its subjects to wear bodysuits mounted with active
markers. Unfortunately, these motion capture devices suffer from limited FOV, lens
occlusions and latency that results in frequently losing sight of the primary subject, false
detections and switched body targets.
This research recognizes that a vision-based motion capture device is an integral part of
the navigational solution, but it cannot be considered as reliable on its own. A survey of
alternative human tracking systems was carried out to find possible candidate
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technologies that can augment the motion capture devices. One option was to induce
redundancy by combining multiple sets of the same motion capture product. However,
this method may not present significant improvement because the same inherent
hardware limitation is shared across all redundant sets.
The survey covered three areas of human tracking: self-localization, body tracking and
biomonitoring. Overviewing localization has helped categorize the methods to discern
entity locations as being range-based and range-free. Range-based methods produce
more accurate positions reports but require calibration to estimate distances based on
units of RF signal strength, pixel depth and similar metrics. Dynamically changing
environmental attributes affect the long-term validity of these calibrations so periodic
adjustments are required. Range-free system approximate proximity between nodes
rather than depend on set calibration values. Using the depth component from depth
maps to approximate the heading and position of an object relative to the host is one
example of range-free localization method. This solidifies the choice of using a vision-
based motion capture device as the primary human tracking solution because localization
based relative to rooms is not necessary in purely human-following applications.
Other body tracking and biomonitoring methods involve using wearable devices
embedded with Microelectromechanical systems (MEMS) that help monitor discrete
motions the wearer makes. While these miscellaneous systems do not directly aid in
tracking the spatial position of the wearer, these projects demonstrated that wearables
can be used to mount active markers. Thus, a separate system that combines vision-
based IR tracking and a wearable installed with active IR markers could offer wider FOV,
longer tracking range and better detection threshold against changing lighting conditions
when compared to RGB-D-equipped motion tracking suites.
Thus, this research proposed a solution that consists of combining a COTS RGB-D
motion capture device with a redundant active IR marker tracking system to supersede
the inherent hardware limitations of the former’s human tracking performance. Meanwhile,
the raw depth map from the motion capture device can be adapted to provide the mid to
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long-range depth component for a modified Potential Field pathfinding algorithm.
Alongside the proximity sensor array data that forms the short-range component, and the
body tracking data as the goal component, a possible indoor navigation solution can be
formed.
The solution development began with applying the companion robot planning template
proposed in Chapter 1. The template helped defined a structure to arrange all required
functionalities as a collection of self-contained components, inspired by the Object-
Oriented Approach. The functionality of each component had to be realized using COTS
material so that the robot system can be recreated rapidly and domestically. As a case
study, this template was used to guide the conceptual development of CARMI – a
Companion Avatar Robot for the Mitigation of Injuries. CARMI is intended for use as the
robot entity in a Robot-Based Injury Prevention Strategy that involves a participating
caregiver and the cognitively impaired child as part of the intervention method. Every
component of the robot template was able to be catered for via readily available parts,
except for the robot navigation system.
Following a conducive process of literature survey into existing assistive robot companion
projects, two major challenges were identified as the common source of complications
preventing the possibility of a standard companion robot platform for general purpose
indoor use. Both indoor autonomous navigation and human tracking functionalities could
not be easily portable between applications because each of them requires tailored
solutions based on applied platforms and specific environment characteristics.
Further literature survey into possible technologies that could help formulate a solution,
had yielded enough results. The existing works inspired the inception of a navigation
system adapted from the Potential Field Method. Instead of using a fixed goal position
and map-wide obstacles, the current human position translates into an attraction force
while the combined depth map obstacles and readings from the close-range proximity
sensors array form the repulsive force. The resultant Potential Field array indicates the
best direction to turn to, in a local wayfinding method fashion. This way, the robot will
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select the next best path in a reflex-like method, but the perception data is generated after
considering the goal position (current position of the primary target), the mid to long-range
landscape (depth map) and the immediate surroundings (proximity array readings).
Several studies were carried out to design, develop and experiment on how portions of
this solution can be realized. The first attempt was to gain first-hand experience over how
a selected motion capture suite called Microsoft Kinect could be utilized simultaneously
for tracking both human activity and position while extracting the raw depth map feed. It
was also aimed at proving that COTS solutions can be depended on to provide human
activity tracking, which was done using the proprietary Visual Gestures studio. Four
injurious actions (falls, punches, kicks and jumps) were processed into gesture profiles
via a brief series of recording and neural network training sessions. During runtime, the
system demonstrated above average performance (58.425% accuracy) at identifying the
correct actions as reenacted by test subjects. Higher success rates were estimated if
more vigorous training sessions were to be carried out. However, the study also
confirmed the effects of the limited FOV, detection distance and changing environmental
lighting.
The next study attempted to develop an IR Active Marker Tracking (AIRMT) system as
the redundant system to complement the motion capture device. The system consists of
a worn active marker vest and a repurposed monocular IR camera. The vest was fitted
with IR emitting LED modules across its surface while the camera’s high-pass filter
removes all background images except for visible LED modules. The original intent for
the system was to track a specific pattern alignment so that the robot could steer itself to
an optimal position via visual servoing. This attempt failed to function optimally beyond
the confines of the motion capture device. However, it was found that raw detection of
modules could be carried out at longer distances and wider FOV than the Kinect. Thus,
the system was repurposed to utilize this ability.
The first stage at solving the navigation challenges was to address the human tracking
portion using the proposed sensor fusion technique. Called the ‘Subject Locking’ phase,
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the viewpoint coordinate systems of the AIRMT was transformed to align its view-space
to the Kinect’s. This allows displacement of the Active Marker’s centroid to correspond to
a displacement of the primary subject’s body. Other detected bodies that do not coincide
with that centroid are eliminated from subsequent computations. This algorithm helped
reduce the effects of false detections, decoy bodies and loss of LOS during shifting
environmental lighting. Via compartmentalization, the Subject Locking phase could be
carried out consistently by nesting the Kinect and AIRMT in a rotating “sensor nest” head.
By using the offset between the primary subject’s position and the view-space midpoint
as the error, a PID controller was used to actuate the turntable so that the robot head
always centers on the child. This allows the lower half of CARMI to move around
obstacles without having to account for human tracking during maneuvers.
The second stage of the navigation algorithm is dubbed the ‘Pathfinding’ phase. Here,
the orientation of the turntable is used to indicate the relative position of the primary
subject. This data is transformed into the attractive force component in the algorithm. Next,
the depth map and data from the proximity sensors array are transformed and merged
into the repulsive force component. When the array indexes were appropriately aligned,
all force components were summed to determine whether the highest total “tendency”
was situated on the ‘Left’ or ‘Right’ half of the robot.
When the robot is outside the escort distance between itself and the child (primary target),
it will engage an ‘Approach’ mode and attempt to move towards the target. However,
encountering an obstacle will prompt it to assume ‘Obstacle Avoidance’ mode, which can
be accomplished using any of the wide available wall-following or avoidance strategies.
However, the robot must choose whether to begin maneuvers from the ‘Left’ or ‘Right’
side of the obstacle. Existing projects involve examining the landscape beforehand (a
priori) or randomly decide on a side to begin maneuvers (local method). The robot cannot
have access to complete a priori maps due to the limitations of its implementation but
relying on random number generator could potentially lead it away from the subject or
enter a dead end. This algorithm supplies a “best guess tendency” towards a direction
based on considering short-mid-long range and subject position components.
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6.2.3 Evaluation of the Effectiveness of the Proposed Navigation Model in Indoor Human-following and Obstacle Avoidance
To gauge the performance of this proposed navigation algorithm, a prototype robot
system was developed using Microsoft Robotics Developer Studio (MRDS) and simulated
via the Visual Simulation Environment (VSE). The complete robot software is structured
as two state machines (one for each phase) that run concurrently. The implemented
system consists of a total of 15 developed robot services that interlock and interact with
the other stock subsystem services. The resulting robot system is technically portable
between hardware and simulated entities, but this research’s scope covers just the
proving of concept through simulation.
The design of the simulation scenarios starts with incepting the typical contents of a living
room. It was determined that all possible obstacles can be classified as Low, Medium and
High types based on whether it obstructs visibility of the target and if it is passable by the
robot. The scenarios will be populated by medium obstacles that are impassable but do
not obscure visibility. The entire simulation experiment consists of two types of scenarios:
functional tests and performance benchmarks.
Seven functional tests were created, one being a baseline scenario with only the robot,
primary subject and decoy targets. This baseline is used as the debug test to help develop
and calibrate the ‘Subject Locking’ phase of the navigation algorithm. Scenarios were
made by adding a uniform and non-uniform entity each. These test the functionality of the
immediate decision-making performance upon encountering and obstacle. Subsequent
scenarios examine the effects of clutter in influencing the decisions. The goal of the
functional tests was to ensure that in single instances, the navigation system was able to
pick sides that will most likely lead to a less impeded, shorter path between itself and the
primary subject. Observations of the scenario runtimes report that 3 out of 10 attempts
were disqualified due to MRDS malfunctions causing service load failures. However, the
remaining runtimes resulted in 100% selection of sides that lead to shorter travel before
reaching the optimal proximity between robot and child. Graphical approximation of travel
distances was derived from the performance logs to compare distances from both choices
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in each scenario. In the scenario with non-uniform obstruction and clutter, the inability to
perceive the other end of the obstruction lead to a 20% chance of picking the longer travel
path. Even including cases with these uncertainties, the navigation system has shown
acceptable decision-making reliability under simulated conditions (accuracy of 88.571%
in 31 out of 35 attempts).
While carrying out the functional tests, it was deemed prudent that the extent of local
minima problem faced by other Potential Field-related systems be determined for this
navigation system. A scenario was built, and the experiment was carried out. It was
observed to also affect this system, although furniture in the real world will have to be
arranged in a specific lagoon configuration before the plateau effect can take place. It is
assumed that extent of this problem is negligible following mindful arrangement of
medium and high obstacles in the event of future physical field testing.
The next category of simulations is for benchmarking the performance of the navigation
system when applied to a series of obstacle avoidance instances while following a mobile
human target. Three existing human-following and exploration research projects were
selected so their test environment can be replicated in VSE as individual benchmark
scenarios. Each scenario simulation was carried out by manually controlling the child
entity and following the same route reported in these published works. The goal was to
determine if the navigation system can consistently maintain proximity to the target and
do so under less travel distances compared to the benchmark studies. Because of
implementation differences, the environment scale between the benchmark studies and
VSE are different. Thus, performance is measured using a ratio of Robot to Human travel
distances. A more efficient human following attempt is presented by a robot that moves
markedly less than the human subject while escorting, as opposed to a basic system that
mimics the targets motion in 1:1 ratio.
The first benchmark scenario showed an improvement of 21.52% by the navigation
system over the existing study (62% vs 78.6%). The second scenario reported 33.26%
better human following performance by the navigation system (46.32% vs 69.4%). The
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final scenario was based on an autonomous exploration method, so it was assumed as
applied to a fully mimicking robot. Against a 1:1 benchmark, the results show that this
navigation system travelled 31.52% less distances while following the subject.
The simulation results show that the implemented model presents a viable potential as a
solution to the navigational challenges which could be encapsulated as a component in
the proposed robot planning template. This implementation consists of using COTS
hardware and range-free localization fed into a Potential Field Method inspired algorithm
that provides informed decisions on which direction to begin executing any choice
obstacle avoidance approaches. This research has provided a documented journey from
incepting to examining the performance of a solution to the indoor robot navigational
challenges.
6.3 LIMITATIONS AND FUTURE WORK
Both the functional tests and benchmark scenarios have shown that the implemented
navigation system had achieved its objective of providing a means to perform indoor
autonomous maneuvering and human following using COTS components. This system
itself could be encapsulated as a standalone module within the Companion Robot
Planning Template, making it adaptable to other indoor companion robot projects so long
as the necessary requirements for motion capture devices, proximity sensors, active
marker tracker and computing resources are met. However, there are issues and
drawbacks surrounding the solution implementation that could not be addressed due to
the limits of this research’s scope.
Firstly, the performance metric used in this research is a graphically approximated travel
distance. Runtime logs contain traveled waypoints which were projected in a top-down
map fashion when transformed into a 2D graph. The pathway that the waypoints form
could be traced and digitized to obtain a best-guess distance value. Performance
comparison can also be carried out by measuring elapsed operation time. Elapsed time
is garnered from the recorded sessions by measuring the timestamp difference from the
199
moment all services are initiated to the end of the journey. Unfortunately, elapsed time
varies depending on the platform construction, implementation of actuation, and the
choice of obstacle avoidance algorithm used. In addition, the benchmark studies did not
publish comprehensive elapsed time data for their test runs, so it could not be
incorporated as part of the measurement process.
Another issue related to the design of the test and benchmark scenarios is the use of
medium size obstacles. As the aim was to create a range-free non-exclusive human
following navigation model, the development process requires consistent LOS between
the robot and primary target. Hence, the obstacles used during unit, functional and
benchmark tests were of the type that obstructs movement but not visibility. Low and high
obstacles are also present in real-world environments, so the presence of these varieties
should also be factored into future tests. They will represent additional layers of difficulty
because the system must contend with the loss of LOS and determine which obstacles
could be safely driven over. Consideration of these types of obstacles will be done if the
system can be augmented with an additional layer of sensors.
The navigation system created in this research is still susceptible to the local minima
problem that is faced by almost all Potential Field Method related applications. The
situation exists because a summation of transformed data feed can result in a plateau
effect - a combination of weighed sensors input evening out each other. In this system’s
case, the state machine’s failsafe method of preventing the robot moving farther away
while pathfinding was the leading cause of the local minima. This can be solved with a
longer watchdog timer setting or rearrangement of furniture.
However, a better solution is to implement an adaptable weight-adjustment system for
changing the significance of sensor data sources based on trend. This can be done via
machine learning techniques such as neural networks or fuzzy logic, so that the robot
recognizes it is in a local minimum after several instances of wandering within a plateau.
The navigation system was developed in mind to perform human following and mitigate
any needed deviations from the approach vector. However, it also had to account for
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situations where the primary subject moves too near the robot, placing him outside the
optimal detection zone. In these cases, CARMI was able to reverse itself. However, the
reverse maneuver only has access to physical bump switches as a means for collision
detection. In the event of a collision, the robot stops the actuation and attempts to turn
sideways. This could be further improved in terms of sensory FOV and more degrees of
freedom.
The introduction of the Active IR Marker Tracking (AIRMT) system can potentially be point
of weakness in the system. While it consists of COTS components, it must be built to
provide the type of tracking performance as indicated in this research. Perhaps a better
COTS alternative suite is available or could be adapted to provide similar functionality
with less effort. In addition, the tracking system is still limited by optical sensor hardware.
An appropriate replacement should also be capable of more visible markers and
identifying them at longer range and wider FOV.
The overall purpose of this research is to contribute towards making companion robots a
consumer-accessible mass producible technology. It does this by identifying how one can
be planned and developed using as many standalone products and solutions as possible.
However, homogenous human following, consisting of autonomous wayfinding and
human tracking, could not be easily realized. The research aimed to explore how a
solution to this problem could be made, demonstrated by its development process from
a model to implemented robot control system. The research could only accommodate
functional and benchmark tests using software simulation that came bundled with the
development kit. Although the results were optimistic, there is no substitute for actual
physical tests in validating real-world performance. However, aside from limited project
scope, the reason a physical test was not included was because the optimal configuration
for tuning the navigation system varies from case to case (due to different hardware and
build specifications). Unless there is some form of adaptable recalibration added into the
algorithm, a physical test could only present performance values exclusive to that
machine involved.
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A physical implementation is still an obvious next step to follow up on this research. Doing
so can be useful in studying the extent of obtrusiveness the robot imposes on its user.
Factors such as approach speed and direction, cosmetic design, and possible escalating
levels of interaction could influence the tendencies for its user to react towards a
companion robot in aggression or acceptance. In addition, separate studies with physical
implementations can better gauge the performance of additional navigational features
such as localization between rooms or floor as well as connectivity. This project presents
an ideal leap board into robot networks and interconnected healthcare services that can
be incorporated as part of the Robot-Based Injury Prevention Strategy. Finally, future
works can also explore alternative augmentations to the motion capture system and
AIRMT, such as facial and voice recognition. Additional system components such as
these can also simultaneously provide input for interactive play or direct communications
between robot and user, as opposed to it acting fully as a passive observer.
6.4 RESEARCH SUMMARY
This research has investigated on the reasons why companion robots have not become
widely available as Commercial Off-the-Shelf (COTS) products even though the use of
robotics as an Assistive Technology has made positive breakthroughs over the years.
The literature surveys helped narrow down the factors that lead to this outcome, including
the difficulty in determining similar characteristics amongst projects studying various
forms of robotic application. The research proposed a robot planning template that helps
in organizing indoor companion robot functionalities and implementations in an Object-
Oriented Approach, emphasizing on creating each module as a standalone subsystem
built using COTS components. However, the navigational challenges of companion
robots could not be easily fulfilled in a homogenous way. Thus, the research embarked
on an effort to survey for possible technologies that can help provide a solution to the
identified challenges: a robust indoor pathfinding method and a more reliable human
tracking method. These two challenges combine to form the human-following capability
integral in every companion robot.
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A solution was formed using a fusion of redundant motion capture device, Active IR
Marker tracking, proximity sensors array, and an adapted Potential Field Method. This
algorithm accounts for close, mid and long-range obstacle proximity in addition to the
relative position of the primary subject. The subject’s identification and tracking are
carried out via the ‘Subject Locking’ phase, while the cumulative force calculations are
transformed into a ‘tendency’ array in the ‘Pathfinding’ phase. The ‘tendency’ array
indicates which direction around an obstacle will most likely lead to a less impeded travel
path.
The proposed model was implemented using Microsoft Robotics Developer Studio
(MRDS) and tested in the included Visual Simulations Environment (VSE). The navigation
system was designed as two simultaneously interacting state machines, reflecting the two
phases of the model. A total of 15 custom robot services was developed to realize this
system. 7 base scenarios were created to test and adjust the navigation system’s
functionality for mitigating individually encountered obstacles. An additional scenario was
made to investigate the extent of the local minima problem distinct to projects related to
the Potential Field Method.
Three existing robot navigation studies were selected to adopt their experimentation
results as a performance benchmark. Their test environment was modeled using VSE as
scenarios. Running all functional tests and benchmark scenarios have generated results
that indicate the navigation system being capable of significant improvements in human
following efficiency. The outcome of this experiment shows that the proposed model is a
viable solution to the indoor robot navigation challenges identified during the literature
survey.
Future work includes consideration of more obstacle varieties, reverse motion obstacle
avoidance, machine learning and curbing the local minima problem. Physical tests
involving navigation system implementation in a robot body, is a definite next step, which
opens more avenues for research ranging from further validation of the system’s
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functionality, development of assistive robot networks and alternate redundant systems
that can replace the AIRMT.
204
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<http://www.ncbi.nlm.nih.gov/pubmed/19074687>.
218
219
APPENDICES
APPENDIX A – USE CASE MODELLING
Use Case ID Use Case Name Primary Actor Scope Complexity Priority
1 Begin Monitoring Guardian In Low 3
2 Monitor Activity Robot In High 1
3 Get Activity Report Guardian In Med 1
4 Alert Guardian Robot In Med 1
5 Have Conversation Guardian In High 2
6 Stop Monitoring Guardian In Low 3
Use Case ID 1
Application ANIMA
Name Begin Monitoring
Description The Guardian sets the Robot to Monitoring Mode.
Primary Actor Guardian
Precondition The Robot is not currently monitoring the Child.
Trigger The Guardian requests the Robot to toggle Monitoring Mode.
Basic Flow 1. Guardian toggles the mode setting of the Robot to Monitoring Mode.
2. Robot checks all prerequisites of the tracking system. 3. All prerequisites to begin tracking are met, so Robot
switches to Monitoring Mode. 4. Robot sends Monitoring Mode activation result to
Guardian.
Alternate
Flows
3 - Failed tracking prerequisites.
3. a. Update activation result with list of failed prerequisites.
3. b. Go to step 4.
220
Use Case ID 2
Application ANIMA
Name Monitor Activity
Description The Robot attempts to look for the Child, detect the Child’s current
activity and adds the result to the monitoring log.
Primary Actor Robot
Precondition The Robot is currently in Monitoring Mode.
Trigger Activation of Monitoring Mode.
Basic Flow 1. Robot checks to see if Child is visible. 2. Child is visible, so robot locks camera position. 3. Robot checks to see if front of Child is visible. 4. Child’s front is visible. Robot remains stationary. 5. Robot checks to see if Child matches any recognizable
activity. 6. Child’s activity is recognized. Robot takes a picture and
adds entry to Monitoring Log. 7. Robot checks to see if entry type is dangerous. 8. Entry type is not dangerous, Robot resumes monitoring. 9. Robot checks to see if Monitoring Mode is deactivated. 10. Monitoring Mode is active, Robot repeats step 1 – 9.
Alternate
Flows
1 – Child is not visible.
2. a. Robot adds ‘Visibility Lost’ entry to Monitoring Log.
2. b. Robot begin wall-following room exploration.
2. c. Go to step 9.
4 - Child’s front is not acquired.
4. a. Robot adds ‘Orbiting’ entry to Monitoring Log.
4. b. Robot orbits locked position and avoid obstacles.
4. c. Go to step 9.
6 – Child’s activity is unrecognizable.
6. a. Robot takes a picture and adds ‘Unrecognized Activity’ entry
to Monitoring Log.
6. b. Go to step 9.
221
8 – Entry type is listed as dangerous.
8. a. Robot Alert Guardian.
8. b. Go to step 9.
Use Case ID 3
Application ANIMA
Name Get Activity Report
Description The Guardian requests for a status update and the Robot sends a
summary of the current monitoring log to the Guardian.
Primary Actor Guardian
Precondition The Robot is currently monitoring the Child.
Trigger The Guardian requests the Robot to generate Current Status
Report.
Basic Flow 1. Guardian requests for an Activity Report. 2. Robot receives request and sends the Monitoring Log
report to Guardian. 3. Guardian console displays report results.
Alternate
Flows
2 – Robot does not receive request.
2. a. Guardian console timeout.
2. b. Guardian console reports timeout as result.
2. c. Go to step 3.
Use Case ID 4
Application ANIMA
Name Alert Guardian
Description The Robot sends an Alert message to the Guardian when a
possible injury or danger entry is added into the Monitoring Log.
Primary Actor Robot
222
Precondition The Robot is currently monitoring the Child.
List of Dangerous Report Types is available.
Trigger A Current Report listed as Dangerous, is added into the Monitoring
Log.
Basic Flow 1. Robot sends an Alert notification to the Guardian. 2. Guardian receives alert notification and confirms success. 3. Robot adds “Alert Success” entry to Monitoring Log.
Alternate
Flows
2 – Guardian did not receive alert notification
2. a. Robot alert timeout.
2. b. Go to step 1.
Use Case ID 5
Application ANIMA
Name Have Conversation
Description The Guardian establishes a video call to the Robot to talk to the
Child.
Primary Actor Guardian
Precondition The Robot is currently monitoring the Child.
The Robot is not currently being used for Conversation.
Trigger The Guardian requests the Robot to Establish Telepresence.
Basic Flow 1. Guardian sends request for Video Link. 2. Robot receives Video Link request and video calls
Guardian. 3. Guardian accepts video call and establish communication. 4. Guardian communicates to Child and terminates
communication. 5. Robot adds “Video Link” entry to Monitoring Log.
Alternate
Flows
2 – Robot did not receive Video Link request.
2. a. Guardian console timeout.
2. b. Guardian console reports request failure.
223
3 – Guardian did not accept video call.
3. a. Robot video call timeout.
3. b. Robot adds “Video Link Failure” entry to Monitoring Log.
3. c. Robot sends call failure notification to Guardian.
Use Case ID 6
Application ANIMA
Name Stop Monitoring
Description The Guardian sets the Robot to stop tracking the Child.
Primary Actor Guardian
Precondition The Robot is currently monitoring the Child.
The Robot is not currently being used for Conversation.
Trigger The Guardian requests the Robot to toggle Monitoring Mode.
Basic Flow 1. Guardian toggles the mode setting of the Robot to Idle Mode.
2. Robot sends Monitoring Mode Deactivation result to Guardian.
Alternate
Flows
224
APPENDIX B – ACTIVITY MODEL FLOW CHARTS
[Monitoring Mode Active]
Monitor Activity
[Request for Activity Report] Get Activity Report
[Request to Alert Guardian] Alert Guardian
[Request for Conversation] Have Conversation
[None]
[None]
[None]
[Monitoring Mode Deactivated]
Main Execution Loop
225
[Child Visible]
Lock Camera Position
[Front is Visible]
Set Strategy to Stationary
[Activity Recognizable]
Take Picture and Log "Recognized Activity"
[Activity is not Dangerous]
[Child is not Visible] Log "Visibility Lost" Set Strategy to Roam
[Front is Not Visible] Log "Orbiting"Set Strategy to
Orbiting
[Activity Unrecognizable]
Take a Picture and Log "Unrecognized
Activity"
[Activity is Dangerous]Request Alert
Guardian
Monitor Activity
226
[Request Received]
Send Monitoring Log Summary as Report
[Timeout]Guardian Console Report Timeout
Guardian Console display report result
Get Activity Report
Send Alert Notification to
Guardian Console
[Receipt Confirmed]
Log "Alert Success"
[Timeout]Log "Alert Failure
and Retry"
Alert Guardian
227
[Video Link Request Received]
Begin Video Call to Guardian Console
[Call Picked Up]
Commence Conversation
[Call Ended] Log "Video Link"
[Timeout]Guardian Console
report Timeout
[Timeout]Log "Video Link
Timeout"
[Continue]
Have Conversation
228
APPENDIX C - CUSTOM STRUCTURAL SCHEMATICS
Arduino Mounting Plate
229
Motor Driver Platform
230
Robot Platform Template
231
Turn-Table Bottom Platform
232
Turn-Table Bottom Platform Bearing Spacer
233
Turn-Table Top Platform
234
Ultrasonic Sensor Bracket
235
Ultrasonic Sensor Mounting Plate
236
APPENDIX D – WIRING DIAGRAM
Signal and Power Distribution Wiring.
237
APPENDIX E – FUNCTIONAL TESTING SCENARIOS SIMULATION RESULTS
Single Uniform Obstruction Sample 1
Motion Path Graph - Single Uniform obstruction - Sample 1.
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
22-5-16-24
CARMI
Child
238
Combined logs - Single Uniform obstruction - Sample 1.
X Z X Z L R
4:24:51 PM 6 9.5 6 14.5
4:24:52 PM 6 9.5 6 14.5
4:24:53 PM 6 9.5 6 14.5
4:24:54 PM 6 9.5 6 14.5
4:24:55 PM 5.992651 9.502109 5.999981 14.49985
4:24:56 PM 6.004373 9.558583 5.999981 14.49996
4:24:57 PM -26.9501 -13.718 Right
4:24:58 PM 6.013047 9.573608 5.999981 14.49996 4:24:58 PM -42.6381 -13.214 Right
4:24:59 PM 6.017072 9.574073 5.999981 14.49996 4:24:59 PM -23.2525 -14.9393 Right
4:25:00 PM 6.017901 9.634454 5.999981 14.49985 4:25:00 PM -13.029 -34.673 Left
4:25:01 PM 6.015293 9.633926 5.999981 14.49997 4:25:01 PM -15.0249 -18.7145 Left
4:25:02 PM 6.015908 9.633844 5.999981 14.49996 4:25:02 PM -25.7751 -15.3791 Right
4:25:03 PM 6.017391 9.635232 5.999981 14.49985 4:25:03 PM -13.856 -15.1758 Left
4:25:04 PM 6.015132 9.724289 5.999981 14.49997 4:25:04 PM -15.4861 -19.5559 Left
4:25:05 PM 6.015524 9.79536 5.999981 14.49997 4:25:05 PM -15.4458 -13.8589 Right
4:25:06 PM 6.02018 10.01487 5.999981 14.49985 4:25:06 PM -21.3677 -14.6147 Right
4:25:07 PM 6.019746 10.05604 5.999981 14.49985 4:25:07 PM -16.6169 -16.5297 Right
4:25:08 PM 6.01063 10.31189 5.999981 14.49996 4:25:08 PM -16.0176 -22.0945 Left
4:25:09 PM -17.9192 -18.4521 Left
4:25:10 PM 6.009624 10.32631 5.999981 14.49985 4:25:10 PM -23.0018 -18.736 Right
4:25:11 PM 6.017926 10.66066 5.999981 14.4998
4:25:12 PM 6.029659 10.9949 5.999981 14.49997 4:25:12 PM -32.5512 -29.028 Right
4:25:13 PM 6.042346 11.00828 5.999981 14.49996 4:25:13 PM -41.4539 -42.951 Left
4:25:14 PM 6.061162 11.01945 5.999981 14.49997 4:25:14 PM -136.882 -76.3328 Right
4:25:15 PM 6.065367 11.02473 5.999981 14.49985 4:25:15 PM -131.378 -14.7197 Right
4:25:16 PM 6.05588 11.01888 5.999981 14.49985 4:25:16 PM -138.943 -40.0176 Right
4:25:17 PM 6.050369 11.01087 5.999981 14.4998 4:25:17 PM -127.644 -42.9589 Right
4:25:18 PM 6.042248 11.00822 5.999981 14.4998 4:25:18 PM -114.333 -44.0849 Right
4:25:19 PM 6.040653 10.97397 5.999981 14.49985 4:25:19 PM -37.4561 -59.8465 Left
4:25:20 PM 6.029764 10.94127 5.999981 14.4998 4:25:20 PM -36.9719 -37.0549 Left
4:25:21 PM 6.015032 10.93412 5.999981 14.4998 4:25:21 PM -60.8264 -92.0799 Left
4:25:22 PM -76.7321 -95.0659 Left
4:25:23 PM 6.044283 10.98443 5.999981 14.49996 4:25:23 PM -66.0759 -98.3046 Left
4:25:24 PM 6.072146 11.02569 5.999981 14.4998 4:25:24 PM -76.3523 -128.273 Left
4:25:25 PM 6.103318 11.05598 5.999981 14.49997 4:25:25 PM -95.8822 -167.888 Left
4:25:26 PM 6.155082 11.08659 5.999981 14.49996 4:25:26 PM -110.905 -200.067 Left
4:25:27 PM 6.198971 11.1044 5.999981 14.49997 4:25:27 PM -118.299 -12.7816 Right
4:25:28 PM 6.264235 11.11884 5.999981 14.49985 4:25:28 PM -113.612 -12.9004 Right
4:25:29 PM 6.315813 11.13479 5.999981 14.49997 4:25:29 PM -98.7733 -13.0937 Right
4:25:30 PM 6.368991 11.16461 5.999981 14.49985 4:25:31 PM -86.5439 -12.9257 Right
4:25:31 PM 6.406761 11.18642 5.999981 14.4998 4:25:32 PM -78.1498 -365.212 Left
4:25:32 PM 6.444426 11.22542 5.999981 14.4998 4:25:33 PM -71.7668 -12.8892 Right
4:25:34 PM 6.489717 11.27814 5.999981 14.49985 4:25:34 PM -70.7147 -13.0342 Right
4:25:35 PM 6.521106 11.31754 5.999981 14.4998 4:25:35 PM -61.6177 -13.6078 Right
4:25:36 PM 6.544494 11.38179 5.999981 14.49996 4:25:36 PM -45.608 -13.0661 Right
4:25:37 PM 6.56184 11.42163 5.999981 14.4998 4:25:37 PM -46.6599 -188.117 Left
4:25:38 PM 6.578347 11.46073 5.999981 14.49997 4:25:38 PM -58.5396 -178.498 Left
4:25:39 PM 6.602067 11.51317 5.999981 14.49996 4:25:39 PM -55.8121 -13.2812 Right
4:25:40 PM 6.618358 11.55351 5.999981 14.49997 4:25:40 PM -55.0514 -13.2783 Right
4:25:41 PM 6.639855 11.60311 5.999981 14.49996 4:25:41 PM -54.9736 -103.072 Left
4:25:42 PM 6.67061 11.6648 5.999981 14.49985 4:25:42 PM -54.8665 -13.3548 Right
4:25:43 PM 6.688007 11.72417 5.999981 14.49996 4:25:43 PM -53.9276 -13.4583 Right
4:25:44 PM 6.705129 11.76586 5.999981 14.4998 4:25:44 PM -51.1037 -13.4935 Right
4:25:46 PM 6.705971 11.83268 5.999981 14.49997 4:25:45 PM -36.1457 -15.6595 Right
4:25:47 PM 6.712987 11.87186 5.999981 14.49997 4:25:46 PM -32.4774 -13.6567 Right
4:25:48 PM 6.716211 11.93488 5.999981 14.49985 4:25:47 PM -36.3891 -13.3958 Right
4:25:49 PM 6.709541 11.96891 5.999981 14.49985 4:25:48 PM -25.1876 -15.4558 Right
4:25:50 PM 6.708804 11.96955 5.999981 14.4998 4:25:50 PM -12.6525 -23.4199 Left
4:25:51 PM 6.707758 11.96898 5.999981 14.49996 4:25:51 PM -15.1018 -21.1385 Left
4:25:52 PM 6.70869 11.96882 5.999981 14.4998 4:25:52 PM -20.356 -13.7829 Right
4:25:53 PM 6.711358 11.97085 5.999981 14.49996 4:25:53 PM -17.6539 -15.4597 Right
4:25:54 PM 6.710649 11.97074 5.999981 14.49985 4:25:54 PM -12.9871 -32.4171 Left
4:25:55 PM 6.70836 11.97029 5.999981 14.49985 4:25:55 PM -14.684 -19.5544 Left
4:25:56 PM 6.707629 11.97108 5.999981 14.49997 4:25:56 PM -14.718 -14.8763 Left
4:25:57 PM -17.5134 -13.8212 Right
4:25:58 PM 6.707671 11.97116 5.999981 14.49996 4:25:58 PM -15.9359 -15.4044 Right
4:25:59 PM 6.707596 11.97114 5.999981 14.4998 4:25:59 PM -15.9327 -15.3972 Right
4:26:00 PM 6.707594 11.97114 5.999981 14.49996
Verdict
PathDecider LogReferee Log
CARMI Child
TimeStamp TimeStamp
Tendencies
239
Single Uniform Obstruction Sample 2
Motion Path Graph - Single Uniform obstruction - Sample 2.
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
23-5-13-42
CARMI
Child
240
Combined logs - Single Uniform obstruction - Sample 2.
X Z X Z L R
1:42:16 PM 6 9.5 6 14.5
1:42:17 PM 6 9.5 6 14.5
1:42:18 PM 6 9.5 6 14.5
1:42:19 PM 6 9.5 6 14.5
1:42:20 PM 5.991889 9.50129 5.999981 14.49997
1:42:21 PM 5.990463 9.50124 5.999981 14.49997 1:42:21 PM -28.7972 -13.6605 Right
1:42:22 PM 5.992763 9.501761 5.999981 14.49997 1:42:22 PM -41.7807 -14.7239 Right
1:42:23 PM 5.995845 9.50164 5.999981 14.49985 1:42:23 PM -20.0164 -14.9934 Right
1:42:24 PM 6.017421 9.782832 5.999981 14.4998 1:42:24 PM -15.2665 -14.8591 Right
1:42:25 PM -19.0469 -14.9901 Right
1:42:26 PM 6.043427 10.12263 5.999981 14.49997 1:42:26 PM -24.0836 -15.7712 Right
1:42:27 PM 6.046795 10.13761 5.999981 14.49996 1:42:27 PM -20.8383 -17.1498 Right
1:42:28 PM 6.049041 10.13826 5.999981 14.49985 1:42:28 PM -15.8732 -19.119 Left
1:42:29 PM 6.037561 10.42277 5.999981 14.4998 1:42:29 PM -18.808 -22.2914 Left
1:42:30 PM 6.034553 10.47023 5.999981 14.49996
1:42:31 PM 6.034654 10.5471 5.999981 14.49985 1:42:31 PM -19.3341 -19.8957 Left
1:42:32 PM 6.039503 10.89222 5.999981 14.49997 1:42:32 PM -25.6076 -21.6171 Right
1:42:33 PM 6.053028 11.01005 5.999981 14.49996 1:42:33 PM -43.9809 -41.4203 Right
1:42:34 PM 6.063476 11.0228 5.999981 14.49985 1:42:34 PM -117.705 -51.5432 Right
1:42:35 PM 6.071899 11.03582 5.999981 14.49985 1:42:35 PM -140.651 -81.0693 Right
1:42:36 PM 6.055428 11.061 5.999981 14.49996 1:42:36 PM -159.29 -93.5426 Right
1:42:37 PM -172.461 -86.5653 Right
1:42:38 PM 5.99304 11.10075 5.999981 14.49997 1:42:38 PM -235.424 -98.7037 Right
1:42:39 PM 5.937294 11.1189 5.999981 14.49997 1:42:39 PM -252.907 -108.965 Right
1:42:40 PM 5.896004 11.13508 5.999981 14.49985 1:42:40 PM -13.0811 -121.093 Left
1:42:41 PM 5.845477 11.13525 5.999981 14.49985 1:42:41 PM -12.8851 -128.729 Left
1:42:42 PM 5.790585 11.13493 5.999981 14.49996 1:42:42 PM -12.6299 -116.001 Left
1:42:43 PM 5.741357 11.14796 5.999981 14.49996 1:42:43 PM -12.9799 -98.6647 Left
1:42:44 PM 5.688997 11.16628 5.999981 14.4998 1:42:44 PM -12.973 -96.8418 Left
1:42:45 PM 5.64477 11.18364 5.999981 14.49996 1:42:45 PM -12.6531 -97.3358 Left
1:42:46 PM 5.598882 11.20877 5.999981 14.49997 1:42:46 PM -12.7989 -88.1293 Left
1:42:47 PM 5.570353 11.23466 5.999981 14.49996 1:42:47 PM -12.823 -73.1488 Left
1:42:48 PM 5.529398 11.27607 5.999981 14.49997 1:42:48 PM -12.7637 -61.9158 Left
1:42:49 PM -12.6985 -56.2428 Left
1:42:50 PM 5.500767 11.32429 5.999981 14.49985
1:42:51 PM 5.478791 11.38216 5.999981 14.49996 1:42:51 PM -12.7173 -52.5571 Left
1:42:52 PM 5.457008 11.44357 5.999981 14.49996 1:42:52 PM -315.588 -52.6775 Right
1:42:53 PM 5.437121 11.50699 5.999981 14.49996 1:42:53 PM -12.821 -50.8265 Left
1:42:54 PM 5.421289 11.56236 5.999981 14.4998 1:42:54 PM -162.654 -54.5584 Right
1:42:55 PM 5.39052 11.61724 5.999981 14.49996 1:42:55 PM -145.791 -56.4564 Right
1:42:56 PM 5.367041 11.66942 5.999981 14.49985 1:42:56 PM -114.881 -56.5946 Right
1:42:57 PM 5.353395 11.70844 5.999981 14.49997 1:42:57 PM -12.7932 -56.5943 Left
1:42:58 PM 5.331506 11.75788 5.999981 14.49996 1:42:58 PM -12.9362 -54.8904 Left
1:42:59 PM 5.318673 11.80679 5.999981 14.49985 1:42:59 PM -14.7588 -47.7623 Left
1:43:00 PM 5.310825 11.87943 5.999981 14.4998 1:43:00 PM -15.2694 -29.8043 Left
1:43:01 PM -185.096 -21.6661 Right
1:43:02 PM 5.307692 11.88899 5.999981 14.4998 1:43:02 PM -185.083 -17.9435 Right
1:43:03 PM 5.306461 11.88945 5.999981 14.49996 1:43:03 PM -185.07 -16.0929 Right
1:43:04 PM 5.306663 11.88937 5.999981 14.49985 1:43:04 PM -185.042 -15.1816 Right
1:43:05 PM 5.306663 11.88937 5.999981 14.4998 1:43:05 PM -185.248 -14.967 Right
1:43:06 PM 5.306663 11.88937 5.999981 14.4998 1:43:06 PM -184.686 -15.5179 Right
1:43:07 PM 5.306663 11.88937 5.999981 14.49997 1:43:07 PM -185.248 -14.9656 Right
1:43:08 PM 5.306663 11.88937 5.999981 14.49996
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
241
Single Uniform Obstruction Sample 3
Motion Path Graph - Single Uniform obstruction - Sample 3.
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
23-5-14-7
CARMI
Child
242
Combined logs - Single Uniform obstruction - Sample 3.
X Z X Z L R
2:07:04 PM 6 9.5 6 14.5
2:07:05 PM 6 9.5 6 14.5
2:07:06 PM 6 9.5 6 14.5
2:07:08 PM 6 9.5 6 14.5
2:07:09 PM 5.992098 9.502001 5.999981 14.4998
2:07:10 PM 5.999874 9.553609 5.999981 14.49997 2:07:10 PM -26.9946 -13.7645 Right
2:07:11 PM 6.002772 9.554681 5.999981 14.49985 2:07:11 PM -40.8162 -14.9504 Right
2:07:12 PM 6.005116 9.554918 5.999981 14.49997 2:07:12 PM -19.0335 -15.2253 Right
2:07:13 PM 6.021955 9.785067 5.999981 14.49996 2:07:13 PM -15.2758 -13.8297 Right
2:07:14 PM 6.035243 9.957998 5.999981 14.49985 2:07:14 PM -21.876 -14.5452 Right
2:07:15 PM 6.038996 9.959197 5.999981 14.49985 2:07:15 PM -20.184 -16.2563 Right
2:07:16 PM 6.034621 10.09502 5.999981 14.49997 2:07:16 PM -16.3634 -16.3941 Left
2:07:17 PM 6.021325 10.43603 5.999981 14.49996 2:07:17 PM -15.6022 -18.7412 Left
2:07:18 PM 6.007824 10.78228 5.999981 14.4998 2:07:18 PM -20.1268 -23.5267 Left
2:07:19 PM 5.992911 11.03858 5.999981 14.49997 2:07:19 PM -37.5482 -42.0444 Left
2:07:20 PM -50.0936 -125.638 Left
2:07:21 PM 5.980575 11.04763 5.999981 14.4998 2:07:21 PM -74.8932 -155.861 Left
2:07:22 PM 5.968372 11.05799 5.999981 14.49996 2:07:22 PM -93.6173 -185.655 Left
2:07:23 PM 5.998254 11.09252 5.999981 14.4998 2:07:23 PM -94.0929 -229.112 Left
2:07:24 PM 6.048455 11.12658 5.999981 14.49996 2:07:24 PM -106.298 -283.338 Left
2:07:25 PM 6.09778 11.14987 5.999981 14.49997
2:07:26 PM 6.158193 11.16004 5.999981 14.4998 2:07:26 PM -114.542 -13.569 Right
2:07:27 PM 6.231093 11.16616 5.999981 14.49997 2:07:27 PM -124.802 -12.7983 Right
2:07:28 PM 6.285115 11.18408 5.999981 14.49985 2:07:28 PM -109.864 -12.9048 Right
2:07:29 PM 6.343492 11.20709 5.999981 14.4998 2:07:29 PM -99.6987 -13.0458 Right
2:07:30 PM 6.393831 11.23446 5.999981 14.49996 2:07:30 PM -94.9677 -12.9784 Right
2:07:31 PM -86.5289 -12.9591 Right
2:07:32 PM 6.432129 11.26231 5.999981 14.4998 2:07:32 PM -79.0818 -12.9892 Right
2:07:33 PM 6.473826 11.30286 5.999981 14.49997 2:07:33 PM -72.5969 -13.0913 Right
2:07:34 PM 6.506058 11.34505 5.999981 14.49996 2:07:34 PM -64.0725 -13.0308 Right
2:07:35 PM 6.53012 11.38914 5.999981 14.49985 2:07:35 PM -52.8344 -334.488 Left
2:07:36 PM 6.541178 11.43418 5.999981 14.49985 2:07:36 PM -47.5939 -229.459 Left
2:07:37 PM 6.561899 11.48053 5.999981 14.4998 2:07:37 PM -54.104 -200.719 Left
2:07:38 PM 6.57794 11.54364 5.999981 14.49996 2:07:38 PM -55.0867 -159.123 Left
2:07:39 PM 6.594798 11.59196 5.999981 14.4998 2:07:39 PM -54.8515 -128.873 Left
2:07:40 PM 6.616853 11.63945 5.999981 14.49996 2:07:40 PM -56.7885 -13.2659 Right
2:07:41 PM 6.637027 11.68808 5.999981 14.49996 2:07:41 PM -58.5293 -13.3884 Right
2:07:42 PM 6.658021 11.74481 5.999981 14.49985 2:07:42 PM -49.182 -28.2239 Right
2:07:43 PM 6.670767 11.80093 5.999981 14.4998 2:07:43 PM -39.8064 -13.578 Right
2:07:44 PM -38.0427 -13.5266 Right
2:07:45 PM 6.678015 11.85639 5.999981 14.4998
2:07:46 PM 6.677867 11.91977 5.999981 14.49997 2:07:46 PM -30.6544 -15.3457 Right
2:07:47 PM 6.659755 11.97205 5.999981 14.49997 2:07:47 PM -13.0477 -20.3378 Left
2:07:48 PM 6.656477 11.9768 5.999981 14.49997 2:07:48 PM -15.2798 -21.5138 Left
2:07:49 PM 6.656309 11.97697 5.999981 14.49985 2:07:49 PM -21.2677 -13.8963 Right
2:07:50 PM 6.658786 11.97789 5.999981 14.49997 2:07:50 PM -23.2763 -15.4816 Right
2:07:51 PM 6.659901 11.97934 5.999981 14.49985 2:07:51 PM -12.9914 -16.4808 Left
2:07:52 PM 6.657941 11.98021 5.999981 14.4998 2:07:52 PM -14.7018 -19.6848 Left
2:07:53 PM 6.657022 11.98034 5.999981 14.49985 2:07:53 PM -14.7392 -14.9733 Left
2:07:54 PM 6.65702 11.98034 5.999981 14.49997 2:07:54 PM -17.5463 -13.8637 Right
2:07:55 PM 6.65702 11.98034 5.999981 14.49997 2:07:55 PM -17.1516 -14.2501 Right
2:07:56 PM 6.657019 11.98034 5.999981 14.49996 2:07:56 PM -16.1527 -15.2481 Right
2:07:57 PM -16.1528 #NAME? Left
2:07:58 PM 6.65702 11.98034 5.999981 14.49997 2:07:58 PM -16.153 -15.2493 Right
2:07:59 PM 6.65702 11.98034 5.999981 14.49996 2:07:59 PM -16.1528 -15.2482 Right
2:08:00 PM 6.65702 11.98034 5.999981 14.49997 2:08:00 PM -16.153 -15.2487 Right
2:08:01 PM 6.65702 11.98034 5.999981 14.49997 2:08:01 PM -16.1526 -15.248 Right
2:08:02 PM 6.65702 11.98034 5.999981 14.49997
2:08:03 PM 6.65702 11.98034 5.999981 14.49997 2:08:03 PM -16.1528 -15.2485 Right
2:08:04 PM 6.65702 11.98034 5.999981 14.4998 2:08:04 PM -16.1531 -15.2484 Right
2:08:05 PM 6.65702 11.98034 5.999981 14.49997 2:08:05 PM -16.153 -15.2483 Right
2:08:06 PM 6.657021 11.98034 5.999981 14.4998 2:08:06 PM -16.1529 -15.2484 Right
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
243
Single Uniform Obstruction Sample 4
Motion Path Graph - Single Uniform obstruction - Sample 4.
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.25.45.65.866.26.46.66.87
23-5-14-23
CARMI
Child
244
Combined logs - Single Uniform obstruction - Sample 4.
X Z X Z L R
2:24:00 PM 6 9.5 6 14.5
2:24:01 PM 6 9.5 6 14.5
2:24:02 PM 6 9.5 6 14.5
2:24:03 PM 6 9.5 6 14.5
2:24:04 PM 5.991642 9.503012 5.999981 14.4998
2:24:05 PM 5.989827 9.503398 5.999981 14.49997 2:24:05 PM -30.7473 -13.5811 Right
2:24:06 PM 5.991612 9.503591 5.999981 14.4998
2:24:07 PM -34.0078 -15.0101 Right
2:24:08 PM 5.993536 9.503521 5.999981 14.4998 2:24:08 PM -22.5796 -15.2161 Right
2:24:09 PM 5.994484 9.504256 5.999981 14.49996 2:24:09 PM -23.5277 -13.3261 Right
2:24:10 PM 5.995536 9.50525 5.999981 14.4998 2:24:10 PM -23.5784 -13.2763 Right
2:24:11 PM 5.999249 9.505614 5.999981 14.49997 2:24:11 PM -21.7762 -15.0817 Right
2:24:12 PM 6.000371 9.551789 5.999981 14.4998 2:24:12 PM -15.0524 -15.0388 Right
2:24:13 PM 5.997384 9.887675 5.999981 14.4998 2:24:13 PM -14.2233 -15.9 Left
2:24:14 PM 5.994301 10.23417 5.999981 14.49996 2:24:14 PM -15.5388 -17.3487 Left
2:24:15 PM 5.991281 10.57542 5.999981 14.49985 2:24:15 PM -17.9376 -19.9814 Left
2:24:16 PM 5.988273 10.91667 5.999981 14.4998 2:24:16 PM -26.4472 -29.0557 Left
2:24:17 PM 5.980721 11.0435 5.999981 14.4998 2:24:17 PM -47.255 -47.368 Left
2:24:18 PM 5.964969 11.05122 5.999981 14.49996 2:24:18 PM -60.3566 -146.316 Left
2:24:19 PM -87.7807 -166.652 Left
2:24:20 PM 5.954296 11.06089 5.999981 14.49985 2:24:20 PM -73.5484 -167.724 Left
2:24:21 PM 5.957585 11.07703 5.999981 14.49997 2:24:21 PM -88.5119 -228.897 Left
2:24:22 PM 6.004911 11.11397 5.999981 14.49996 2:24:22 PM -97.8165 -283.308 Left
2:24:23 PM 6.050199 11.14217 5.999981 14.49985 2:24:23 PM -112.701 -334.207 Left
2:24:24 PM 6.094389 11.16249 5.999981 14.49997
2:24:25 PM 6.146997 11.16607 5.999981 14.49997 2:24:25 PM -124.794 -12.8637 Right
2:24:26 PM 6.208316 11.16912 5.999981 14.49985 2:24:26 PM -127.605 -12.9631 Right
2:24:27 PM 6.249608 11.17535 5.999981 14.49997 2:24:27 PM -114.574 -13.0288 Right
2:24:28 PM 6.301901 11.1987 5.999981 14.49985 2:24:28 PM -96.9561 -27.1994 Right
2:24:29 PM 6.357595 11.23395 5.999981 14.49997 2:24:29 PM -92.1243 -12.9502 Right
2:24:30 PM 6.404997 11.26928 5.999981 14.4998 2:24:30 PM -86.5461 -12.9724 Right
2:24:31 PM 6.440885 11.31119 5.999981 14.49996 2:24:31 PM -75.3952 -13.0778 Right
2:24:32 PM -62.2642 -13.079 Right
2:24:33 PM 6.462356 11.34398 5.999981 14.49996 2:24:33 PM -54.7869 -13.0567 Right
2:24:34 PM 6.484216 11.38859 5.999981 14.49985 2:24:34 PM -50.4268 -13.163 Right
2:24:35 PM 6.50139 11.43284 5.999981 14.49985 2:24:35 PM -50.4847 -13.2486 Right
2:24:36 PM 6.522586 11.49151 5.999981 14.49997 2:24:36 PM -52.2708 -283.639 Left
2:24:37 PM 6.547487 11.53818 5.999981 14.49985 2:24:37 PM -55.7413 -229.54 Left
2:24:38 PM 6.566504 11.58569 5.999981 14.4998 2:24:38 PM -54.1337 -13.3853 Right
2:24:39 PM 6.583529 11.63368 5.999981 14.49997 2:24:39 PM -55.1095 -13.2875 Right
2:24:40 PM 6.603369 11.68077 5.999981 14.49985 2:24:40 PM -55.1443 -13.1094 Right
2:24:41 PM 6.627034 11.72534 5.999981 14.49985 2:24:41 PM -54.7697 -13.689 Right
2:24:42 PM 6.64632 11.7841 5.999981 14.4998 2:24:42 PM -42.8075 -161.01 Left
2:24:43 PM 6.656025 11.84265 5.999981 14.49996 2:24:43 PM -34.2168 -19.851 Right
2:24:44 PM -29.707 -15.3036 Right
2:24:45 PM 6.656774 11.89533 5.999981 14.49985
2:24:46 PM 6.655472 11.9461 5.999981 14.4998 2:24:46 PM -22.2871 -15.4173 Right
2:24:47 PM 6.656337 11.94676 5.999981 14.49985 2:24:47 PM -12.9085 -33.1484 Left
2:24:48 PM 6.654506 11.94646 5.999981 14.49996 2:24:48 PM -15.1668 -18.4486 Left
2:24:49 PM 6.654568 11.94666 5.999981 14.49985 2:24:49 PM -21.3737 -13.7049 Right
2:24:50 PM 6.656505 11.94637 5.999981 14.49997 2:24:50 PM -19.519 -15.4722 Right
2:24:51 PM 6.656658 11.94659 5.999981 14.49985 2:24:51 PM -12.9536 -248.776 Left
2:24:52 PM 6.656202 11.94633 5.999981 14.49996 2:24:52 PM -14.6937 -248.976 Left
2:24:53 PM 6.656202 11.94633 5.999981 14.49996 2:24:53 PM -14.6903 -248.979 Left
2:24:54 PM 6.656202 11.94633 5.999981 14.49996 2:24:54 PM -14.7179 -248.952 Left
2:24:55 PM 6.656202 11.94633 5.999981 14.4998 2:24:55 PM -14.7214 -248.953 Left
2:24:56 PM -14.6902 -248.979 Left
2:24:57 PM 6.656202 11.94633 5.999981 14.49997
2:24:58 PM 6.656202 11.94633 5.999981 14.49997 2:24:58 PM -14.6901 -248.979 Left
2:24:59 PM 6.656203 11.94633 5.999981 14.49996 2:24:59 PM -14.6903 -248.98 Left
2:25:00 PM 6.656203 11.94633 5.999981 14.4998 2:25:00 PM -14.685 -248.975 Left
2:25:01 PM 6.656203 11.94633 5.999981 14.4998 2:25:01 PM -14.7179 -248.952 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
245
Single Uniform Obstruction Sample 5
Motion Path Graph - Single Uniform obstruction - Sample 5.
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
23-5-14-36
CARMI
Child
246
Combined logs - Single Uniform obstruction - Sample 5.
X Z X Z L R
2:36:28 PM 6 9.5 6 14.5
2:36:29 PM 6 9.5 6 14.5
2:36:30 PM 6 9.5 6 14.5
2:36:31 PM 6 9.5 6 14.5
2:36:32 PM 5.992746 9.501021 5.999981 14.49985
2:36:34 PM 6.007417 9.582978 5.999981 14.4998 2:36:34 PM -30.0922 -13.6283 Right
2:36:35 PM 6.009819 9.583772 5.999981 14.49997 2:36:35 PM -39.0405 -14.9825 Right
2:36:36 PM 6.01228 9.583688 5.999981 14.49997 2:36:36 PM -23.8771 -15.1842 Right
2:36:37 PM 6.019166 9.657411 5.999981 14.49996 2:36:37 PM -15.3707 -18.3197 Left
2:36:38 PM 6.032834 9.764443 5.999981 14.49997 2:36:38 PM -23.6962 -14.36 Right
2:36:39 PM 6.038038 9.790393 5.999981 14.49996 2:36:39 PM -35.7012 -15.5458 Right
2:36:40 PM 6.039547 9.791645 5.999981 14.4998 2:36:40 PM -21.622 -15.6614 Right
2:36:41 PM 6.043862 9.916765 5.999981 14.49996
2:36:42 PM -15.6847 -23.3744 Left
2:36:43 PM 6.044794 9.945532 5.999981 14.49996 2:36:43 PM -17.5249 -14.823 Right
2:36:44 PM 6.054964 10.01599 5.999981 14.49985 2:36:44 PM -30.8444 -16.3091 Right
2:36:45 PM 6.05662 10.01635 5.999981 14.4998 2:36:45 PM -18.6087 -16.4086 Right
2:36:46 PM 6.052341 10.06703 5.999981 14.49996 2:36:46 PM -16.1921 -23.9914 Left
2:36:47 PM 6.06334 10.18729 5.999981 14.49996 2:36:47 PM -27.2906 -15.7063 Right
2:36:48 PM 6.063964 10.18735 5.999981 14.49985 2:36:48 PM -32.2744 -17.3988 Right
2:36:49 PM 6.066131 10.18779 5.999981 14.49985 2:36:49 PM -20.8783 -17.6071 Right
2:36:50 PM 6.071425 10.46762 5.999981 14.4998 2:36:50 PM -18.1744 -17.8435 Right
2:36:51 PM 6.077846 10.80885 5.999981 14.49997 2:36:51 PM -23.1739 -23.013 Right
2:36:52 PM 6.084408 11.03062 5.999981 14.49997 2:36:52 PM -49.7358 -44.9287 Right
2:36:53 PM 6.087319 11.03093 5.999981 14.49997 2:36:53 PM -46.6126 -47.9759 Left
2:36:54 PM 6.096694 11.03804 5.999981 14.49997 2:36:54 PM -43.8086 -52.6335 Left
2:36:55 PM -145.064 -73.6089 Right
2:36:56 PM 6.105868 11.04598 5.999981 14.49996 2:36:56 PM -173.743 -89.7996 Right
2:36:57 PM 6.11417 11.05821 5.999981 14.4998 2:36:57 PM -172.448 -75.3695 Right
2:36:58 PM 6.103453 11.07753 5.999981 14.49996 2:36:58 PM -219.803 -94.0106 Right
2:36:59 PM 6.045853 11.11095 5.999981 14.49985 2:36:59 PM -235.421 -109.872 Right
2:37:00 PM 5.993153 11.13041 5.999981 14.49996
2:37:01 PM 5.936425 11.13254 5.999981 14.4998 2:37:01 PM -219.867 -117.349 Right
2:37:02 PM 5.887553 11.12776 5.999981 14.49997 2:37:02 PM -13.1128 -131.36 Left
2:37:03 PM 5.835094 11.1264 5.999981 14.49996 2:37:03 PM -12.6219 -136.521 Left
2:37:04 PM 5.778492 11.13452 5.999981 14.49985 2:37:04 PM -290.969 -117.819 Right
2:37:05 PM 5.724764 11.14765 5.999981 14.49997 2:37:05 PM -13.1135 -98.7504 Left
2:37:06 PM 5.649559 11.17921 5.999981 14.49996 2:37:06 PM -12.7039 -99.1119 Left
2:37:07 PM -453.762 -90.8212 Right
2:37:08 PM 5.601518 11.21611 5.999981 14.4998 2:37:08 PM -12.4984 -71.999 Left
2:37:09 PM 5.562678 11.2585 5.999981 14.49997 2:37:09 PM -27.609 -65.6256 Left
2:37:10 PM 5.538737 11.29534 5.999981 14.49997 2:37:10 PM -12.6948 -59.952 Left
2:37:11 PM 5.512889 11.36313 5.999981 14.49996 2:37:11 PM -12.6604 -48.7911 Left
2:37:12 PM 5.490465 11.41259 5.999981 14.49997 2:37:12 PM -375.087 -49.832 Right
2:37:13 PM 5.47027 11.47035 5.999981 14.49996 2:37:13 PM -12.8178 -53.6879 Left
2:37:14 PM 5.450334 11.51438 5.999981 14.49985 2:37:14 PM -12.7879 -54.6147 Left
2:37:15 PM 5.428233 11.56512 5.999981 14.49997 2:37:15 PM -12.7249 -54.5971 Left
2:37:16 PM 5.404184 11.61186 5.999981 14.49985 2:37:16 PM -162.73 -54.7042 Right
2:37:17 PM 5.388108 11.65124 5.999981 14.4998
2:37:18 PM 5.364429 11.71439 5.999981 14.49996 2:37:18 PM -131.737 -54.7618 Right
2:37:19 PM -12.8862 -54.916 Left
2:37:20 PM 5.345765 11.7772 5.999981 14.49997 2:37:20 PM -164.713 -52.2816 Right
2:37:21 PM 5.340353 11.83129 5.999981 14.49985 2:37:21 PM -12.9225 -32.5273 Left
2:37:22 PM 5.330233 11.88993 5.999981 14.4998 2:37:22 PM -13.1033 -34.7145 Left
2:37:23 PM 5.327972 11.93691 5.999981 14.49985 2:37:23 PM -14.9929 -31.5138 Left
2:37:24 PM 5.328048 11.97961 5.999981 14.4998 2:37:24 PM -15.0799 -19.5488 Left
2:37:25 PM 5.328823 11.98095 5.999981 14.49996 2:37:25 PM -24.6793 -14.1889 Right
2:37:26 PM 5.33059 11.9813 5.999981 14.49997 2:37:26 PM -19.7997 -16.2016 Right
2:37:27 PM 5.331334 11.98141 5.999981 14.4998 2:37:27 PM -13.3778 -18.2063 Left
2:37:28 PM 5.331336 11.98141 5.999981 14.4998 2:37:28 PM -13.5521 -18.0721 Left
2:37:29 PM 5.33134 11.98141 5.999981 14.49997 2:37:29 PM -13.7984 -17.8204 Left
2:37:31 PM 5.331341 11.98141 5.999981 14.49985 2:37:31 PM -13.7985 -17.8208 Left
2:37:32 PM 5.331342 11.98141 5.999981 14.49996 2:37:32 PM -13.7986 -17.8205 Left
2:37:33 PM 5.331342 11.98141 5.999981 14.49996 2:37:33 PM -13.8118 -17.8346 Left
2:37:34 PM 5.331343 11.98141 5.999981 14.4998 2:37:34 PM -13.811 -17.8345 Left
2:37:35 PM 5.331323 11.98142 5.999981 14.49997 2:37:35 PM -13.8126 -17.8352 Left
2:37:36 PM 5.331327 11.98141 5.999981 14.4998 2:37:36 PM -13.7998 -17.8234 Left
2:37:37 PM 5.331337 11.98141 5.999981 14.4998 2:37:37 PM -13.7994 -17.8207 Left
2:37:38 PM 5.331334 11.98141 5.999981 14.49996 2:37:38 PM -13.7996 -17.82 Left
2:37:39 PM 5.331337 11.98141 5.999981 14.49997 2:37:39 PM -13.7949 -17.8157 Left
2:37:40 PM 5.331342 11.98141 5.999981 14.49996
2:37:41 PM 5.331341 11.98141 5.999981 14.49996 2:37:41 PM -13.7997 -17.8208 Left
2:37:42 PM 5.331332 11.98142 5.999981 14.4998 2:37:42 PM -13.8 -17.82 Left
2:37:43 PM -15.3493 -16.2713 Left
2:37:44 PM 5.331334 11.98142 5.999981 14.49997 2:37:44 PM -13.7997 -17.821 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
247
Single Uniform Obstruction Sample 6
Motion Path Graph - Single Uniform obstruction - Sample 6.
X Z X Z L R
2:36:28 PM 6 9.5 6 14.5
2:36:29 PM 6 9.5 6 14.5
2:36:30 PM 6 9.5 6 14.5
2:36:31 PM 6 9.5 6 14.5
2:36:32 PM 5.992746 9.501021 5.999981 14.49985
2:36:34 PM 6.007417 9.582978 5.999981 14.4998 2:36:34 PM -30.0922 -13.6283 Right
2:36:35 PM 6.009819 9.583772 5.999981 14.49997 2:36:35 PM -39.0405 -14.9825 Right
2:36:36 PM 6.01228 9.583688 5.999981 14.49997 2:36:36 PM -23.8771 -15.1842 Right
2:36:37 PM 6.019166 9.657411 5.999981 14.49996 2:36:37 PM -15.3707 -18.3197 Left
2:36:38 PM 6.032834 9.764443 5.999981 14.49997 2:36:38 PM -23.6962 -14.36 Right
2:36:39 PM 6.038038 9.790393 5.999981 14.49996 2:36:39 PM -35.7012 -15.5458 Right
2:36:40 PM 6.039547 9.791645 5.999981 14.4998 2:36:40 PM -21.622 -15.6614 Right
2:36:41 PM 6.043862 9.916765 5.999981 14.49996
2:36:42 PM -15.6847 -23.3744 Left
2:36:43 PM 6.044794 9.945532 5.999981 14.49996 2:36:43 PM -17.5249 -14.823 Right
2:36:44 PM 6.054964 10.01599 5.999981 14.49985 2:36:44 PM -30.8444 -16.3091 Right
2:36:45 PM 6.05662 10.01635 5.999981 14.4998 2:36:45 PM -18.6087 -16.4086 Right
2:36:46 PM 6.052341 10.06703 5.999981 14.49996 2:36:46 PM -16.1921 -23.9914 Left
2:36:47 PM 6.06334 10.18729 5.999981 14.49996 2:36:47 PM -27.2906 -15.7063 Right
2:36:48 PM 6.063964 10.18735 5.999981 14.49985 2:36:48 PM -32.2744 -17.3988 Right
2:36:49 PM 6.066131 10.18779 5.999981 14.49985 2:36:49 PM -20.8783 -17.6071 Right
2:36:50 PM 6.071425 10.46762 5.999981 14.4998 2:36:50 PM -18.1744 -17.8435 Right
2:36:51 PM 6.077846 10.80885 5.999981 14.49997 2:36:51 PM -23.1739 -23.013 Right
2:36:52 PM 6.084408 11.03062 5.999981 14.49997 2:36:52 PM -49.7358 -44.9287 Right
2:36:53 PM 6.087319 11.03093 5.999981 14.49997 2:36:53 PM -46.6126 -47.9759 Left
2:36:54 PM 6.096694 11.03804 5.999981 14.49997 2:36:54 PM -43.8086 -52.6335 Left
2:36:55 PM -145.064 -73.6089 Right
2:36:56 PM 6.105868 11.04598 5.999981 14.49996 2:36:56 PM -173.743 -89.7996 Right
2:36:57 PM 6.11417 11.05821 5.999981 14.4998 2:36:57 PM -172.448 -75.3695 Right
2:36:58 PM 6.103453 11.07753 5.999981 14.49996 2:36:58 PM -219.803 -94.0106 Right
2:36:59 PM 6.045853 11.11095 5.999981 14.49985 2:36:59 PM -235.421 -109.872 Right
2:37:00 PM 5.993153 11.13041 5.999981 14.49996
2:37:01 PM 5.936425 11.13254 5.999981 14.4998 2:37:01 PM -219.867 -117.349 Right
2:37:02 PM 5.887553 11.12776 5.999981 14.49997 2:37:02 PM -13.1128 -131.36 Left
2:37:03 PM 5.835094 11.1264 5.999981 14.49996 2:37:03 PM -12.6219 -136.521 Left
2:37:04 PM 5.778492 11.13452 5.999981 14.49985 2:37:04 PM -290.969 -117.819 Right
2:37:05 PM 5.724764 11.14765 5.999981 14.49997 2:37:05 PM -13.1135 -98.7504 Left
2:37:06 PM 5.649559 11.17921 5.999981 14.49996 2:37:06 PM -12.7039 -99.1119 Left
2:37:07 PM -453.762 -90.8212 Right
2:37:08 PM 5.601518 11.21611 5.999981 14.4998 2:37:08 PM -12.4984 -71.999 Left
2:37:09 PM 5.562678 11.2585 5.999981 14.49997 2:37:09 PM -27.609 -65.6256 Left
2:37:10 PM 5.538737 11.29534 5.999981 14.49997 2:37:10 PM -12.6948 -59.952 Left
2:37:11 PM 5.512889 11.36313 5.999981 14.49996 2:37:11 PM -12.6604 -48.7911 Left
2:37:12 PM 5.490465 11.41259 5.999981 14.49997 2:37:12 PM -375.087 -49.832 Right
2:37:13 PM 5.47027 11.47035 5.999981 14.49996 2:37:13 PM -12.8178 -53.6879 Left
2:37:14 PM 5.450334 11.51438 5.999981 14.49985 2:37:14 PM -12.7879 -54.6147 Left
2:37:15 PM 5.428233 11.56512 5.999981 14.49997 2:37:15 PM -12.7249 -54.5971 Left
2:37:16 PM 5.404184 11.61186 5.999981 14.49985 2:37:16 PM -162.73 -54.7042 Right
2:37:17 PM 5.388108 11.65124 5.999981 14.4998
2:37:18 PM 5.364429 11.71439 5.999981 14.49996 2:37:18 PM -131.737 -54.7618 Right
2:37:19 PM -12.8862 -54.916 Left
2:37:20 PM 5.345765 11.7772 5.999981 14.49997 2:37:20 PM -164.713 -52.2816 Right
2:37:21 PM 5.340353 11.83129 5.999981 14.49985 2:37:21 PM -12.9225 -32.5273 Left
2:37:22 PM 5.330233 11.88993 5.999981 14.4998 2:37:22 PM -13.1033 -34.7145 Left
2:37:23 PM 5.327972 11.93691 5.999981 14.49985 2:37:23 PM -14.9929 -31.5138 Left
2:37:24 PM 5.328048 11.97961 5.999981 14.4998 2:37:24 PM -15.0799 -19.5488 Left
2:37:25 PM 5.328823 11.98095 5.999981 14.49996 2:37:25 PM -24.6793 -14.1889 Right
2:37:26 PM 5.33059 11.9813 5.999981 14.49997 2:37:26 PM -19.7997 -16.2016 Right
2:37:27 PM 5.331334 11.98141 5.999981 14.4998 2:37:27 PM -13.3778 -18.2063 Left
2:37:28 PM 5.331336 11.98141 5.999981 14.4998 2:37:28 PM -13.5521 -18.0721 Left
2:37:29 PM 5.33134 11.98141 5.999981 14.49997 2:37:29 PM -13.7984 -17.8204 Left
2:37:31 PM 5.331341 11.98141 5.999981 14.49985 2:37:31 PM -13.7985 -17.8208 Left
2:37:32 PM 5.331342 11.98141 5.999981 14.49996 2:37:32 PM -13.7986 -17.8205 Left
2:37:33 PM 5.331342 11.98141 5.999981 14.49996 2:37:33 PM -13.8118 -17.8346 Left
2:37:34 PM 5.331343 11.98141 5.999981 14.4998 2:37:34 PM -13.811 -17.8345 Left
2:37:35 PM 5.331323 11.98142 5.999981 14.49997 2:37:35 PM -13.8126 -17.8352 Left
2:37:36 PM 5.331327 11.98141 5.999981 14.4998 2:37:36 PM -13.7998 -17.8234 Left
2:37:37 PM 5.331337 11.98141 5.999981 14.4998 2:37:37 PM -13.7994 -17.8207 Left
2:37:38 PM 5.331334 11.98141 5.999981 14.49996 2:37:38 PM -13.7996 -17.82 Left
2:37:39 PM 5.331337 11.98141 5.999981 14.49997 2:37:39 PM -13.7949 -17.8157 Left
2:37:40 PM 5.331342 11.98141 5.999981 14.49996
2:37:41 PM 5.331341 11.98141 5.999981 14.49996 2:37:41 PM -13.7997 -17.8208 Left
2:37:42 PM 5.331332 11.98142 5.999981 14.4998 2:37:42 PM -13.8 -17.82 Left
2:37:43 PM -15.3493 -16.2713 Left
2:37:44 PM 5.331334 11.98142 5.999981 14.49997 2:37:44 PM -13.7997 -17.821 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
23-5-14-53
CARMI
Child
248
Combined logs - Single Uniform obstruction - Sample 6.
X Z X Z L R
2:53:22 PM 6 9.5 6 14.5
2:53:23 PM 6 9.5 6 14.5
2:53:24 PM 6 9.5 6 14.5
2:53:26 PM 6 9.5 6 14.5
2:53:27 PM 5.995378 9.50098 5.999981 14.4998
2:53:28 PM 5.982534 9.504639 5.999981 14.49985
2:53:29 PM 5.972011 9.506713 5.999981 14.4998
2:53:30 PM 5.966234 9.511897 5.999981 14.4998
2:53:31 PM 5.956379 9.517919 5.999981 14.49985
2:53:32 PM 5.948141 9.528584 5.999981 14.4998
2:53:45 PM 6 9.5 6 14.5
2:53:47 PM 6 9.5 6 14.5
2:53:48 PM 6 9.5 6 14.5
2:53:49 PM 6 9.5 6 14.5
2:53:50 PM 5.999637 9.500959 5.999981 14.49985
2:53:51 PM 5.989192 9.501268 5.999981 14.4998
2:53:52 PM 6.009953 9.581203 5.999981 14.4998 2:53:52 PM -24.1744 -13.7619 Right
2:53:53 PM -38.1995 -14.8678 Right
2:53:54 PM 6.011352 9.582403 5.999981 14.49996 2:53:54 PM -23.7347 -15.317 Right
2:53:55 PM 6.013167 9.582652 5.999981 14.49985 2:53:55 PM -17.9856 -13.6688 Right
2:53:56 PM 6.031202 9.803303 5.999981 14.49997 2:53:56 PM -18.9116 -14.8296 Right
2:53:57 PM 6.059042 10.14826 5.999981 14.49985 2:53:57 PM -24.0805 -15.7896 Right
2:53:58 PM 6.068552 10.22809 5.999981 14.49985
2:53:59 PM 6.069862 10.24462 5.999981 14.4998 2:53:59 PM -22.2815 -17.587 Right
2:54:00 PM 6.071493 10.30073 5.999981 14.4998 2:54:00 PM -17.8255 -22.0933 Left
2:54:01 PM 6.081372 10.45183 5.999981 14.49985 2:54:01 PM -20.5162 -16.7644 Right
2:54:02 PM 6.082294 10.45186 5.999981 14.49997 2:54:02 PM -31.3897 -17.7404 Right
2:54:03 PM 6.084512 10.45175 5.999981 14.49996 2:54:03 PM -26.5856 -19.7632 Right
2:54:04 PM 6.090533 10.64822 5.999981 14.49985 2:54:04 PM -17.0844 -19.7062 Left
2:54:05 PM -25.6252 -22.7553 Right
2:54:06 PM 6.105381 10.97767 5.999981 14.4998 2:54:06 PM -45.2909 -49.1814 Left
2:54:07 PM 6.108428 10.98384 5.999981 14.4998 2:54:07 PM -42.9896 -41.1448 Right
2:54:08 PM 6.109766 10.98386 5.999981 14.4998 2:54:08 PM -39.4612 -87.5404 Left
2:54:09 PM 6.097194 10.98714 5.999981 14.49985 2:54:09 PM -46.9477 -104.485 Left
2:54:10 PM 6.087941 10.99595 5.999981 14.4998 2:54:10 PM -82.4227 -121.018 Left
2:54:11 PM 6.08036 11.00706 5.999981 14.49985
2:54:12 PM 6.120484 11.0537 5.999981 14.4998 2:54:12 PM -72.6487 -117.417 Left
2:54:13 PM 6.164454 11.09466 5.999981 14.49997 2:54:13 PM -73.6245 -158.409 Left
2:54:14 PM 6.210436 11.13085 5.999981 14.49985 2:54:14 PM -88.4607 -245.75 Left
2:54:15 PM 6.251112 11.154 5.999981 14.49985 2:54:15 PM -100.657 -307.041 Left
2:54:16 PM 6.311867 11.17426 5.999981 14.49997 2:54:16 PM -112.671 -365.172 Left
2:54:17 PM 6.35881 11.19403 5.999981 14.49985 2:54:17 PM -98.6339 -12.8954 Right
2:54:18 PM -79.259 -12.9476 Right
2:54:19 PM 6.408604 11.22843 5.999981 14.49996 2:54:19 PM -77.2713 -12.9919 Right
2:54:20 PM 6.446264 11.2663 5.999981 14.49996 2:54:20 PM -73.5519 -12.8835 Right
2:54:21 PM 6.47231 11.29582 5.999981 14.49997
2:54:22 PM 6.496563 11.32801 5.999981 14.49985 2:54:22 PM -56.1056 -13.6863 Right
2:54:23 PM 6.517154 11.37788 5.999981 14.4998 2:54:23 PM -46.6246 -13.0017 Right
2:54:24 PM 6.537557 11.42929 5.999981 14.49997 2:54:24 PM -53.2391 -13.1359 Right
2:54:25 PM 6.560007 11.48385 5.999981 14.49997 2:54:25 PM -54.8647 -13.2653 Right
2:54:26 PM 6.571776 11.5283 5.999981 14.49997 2:54:26 PM -48.1041 -214.537 Left
2:54:27 PM 6.589194 11.57422 5.999981 14.49997 2:54:27 PM -45.7202 -12.6899 Right
2:54:28 PM 6.611063 11.62781 5.999981 14.49996 2:54:28 PM -61.3046 -123.48 Left
2:54:29 PM 6.63242 11.69119 5.999981 14.49985 2:54:29 PM -64.3123 -19.2208 Right
2:54:31 PM 6.647907 11.74242 5.999981 14.49997 2:54:31 PM -49.5957 -14.0962 Right
2:54:32 PM 6.667361 11.79609 5.999981 14.49997 2:54:32 PM -38.9248 -13.5605 Right
2:54:33 PM 6.670047 11.84444 5.999981 14.49996 2:54:33 PM -34.3969 -170.425 Left
2:54:34 PM 6.67282 11.89478 5.999981 14.49997 2:54:34 PM -30.5215 -13.6087 Right
2:54:35 PM 6.675263 11.94117 5.999981 14.49996 2:54:35 PM -25.2071 -15.5397 Right
2:54:36 PM 6.678015 11.95598 5.999981 14.49997 2:54:36 PM -12.9488 -21.5554 Left
2:54:37 PM 6.676054 11.95506 5.999981 14.4998 2:54:37 PM -15.2631 -21.6175 Left
2:54:38 PM 6.675122 11.95514 5.999981 14.4998
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
249
Single Uniform Obstruction Sample 7
Motion Path Graph - Single Uniform obstruction - Sample 7.
Combined logs - Single Uniform obstruction - Sample 7.
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
23-5-16-26
CARMI
Child
X Z X Z L R
4:26:07 PM 6 9.5 6 14.5
4:26:08 PM 6 9.5 6 14.5
4:26:09 PM 6 9.5 6 14.5
4:26:10 PM 6 9.5 6 14.5
4:26:12 PM 5.99493 9.500999 5.999981 14.49996
4:26:13 PM 5.985316 9.503292 5.999981 14.49997
4:26:14 PM 5.977532 9.508522 5.999981 14.4998
4:26:15 PM 5.969895 9.521795 5.999981 14.4998
4:26:16 PM 5.962986 9.527066 5.999981 14.49985
4:26:17 PM 5.958001 9.536101 5.999981 14.49996
4:26:57 PM 6 9.5 6 14.5
4:26:58 PM 6 9.5 6 14.5
4:27:00 PM 6 9.5 6 14.5
4:27:01 PM 6 9.5 6 14.5
4:27:02 PM 6.000072 9.500964 5.999981 14.4998
4:27:03 PM 5.990367 9.502106 5.999981 14.49997
4:27:04 PM 6.011034 9.59257 5.999981 14.49985 4:27:04 PM -24.1753 -13.7776 Right
4:27:05 PM 6.012823 9.591856 5.999981 14.49985 4:27:05 PM -35.0501 -15.2392 Right
4:27:06 PM 6.014133 9.593542 5.999981 14.49997 4:27:06 PM -22.8171 -15.3363 Right
4:27:07 PM 6.034587 9.847871 5.999981 14.49985 4:27:07 PM -15.3375 -13.9715 Right
4:27:08 PM 6.054775 10.10355 5.999981 14.49985 4:27:08 PM -21.0661 -15.1408 Right
4:27:09 PM -23.4253 -16.987 Right
4:27:10 PM 6.056666 10.10434 5.999981 14.49997 4:27:10 PM -19.6587 -17.0278 Right
4:27:11 PM 6.056307 10.14029 5.999981 14.49996 4:27:11 PM -15.8541 -21.8172 Left
4:27:12 PM 6.041775 10.39607 5.999981 14.4998 4:27:12 PM -18.5775 -21.0774 Left
4:27:13 PM 6.038926 10.39728 5.999981 14.4998 4:27:13 PM -18.5231 -19.2477 Left
4:27:14 PM 6.039114 10.47541 5.999981 14.49996 4:27:14 PM -25.4049 -19.3215 Right
4:27:15 PM 6.047258 10.69636 5.999981 14.49985
4:27:16 PM 6.048785 10.69682 5.999981 14.49985 4:27:16 PM -26.9319 -23.9316 Right
4:27:17 PM 6.026943 10.95504 5.999981 14.4998 4:27:17 PM -22.2185 -25.896 Left
4:27:18 PM 6.024652 10.95601 5.999981 14.49996 4:27:18 PM -36.181 -41.3379 Left
4:27:19 PM 6.019865 10.95852 5.999981 14.49985 4:27:19 PM -36.2427 -40.3296 Left
4:27:20 PM 6.006553 10.96539 5.999981 14.49985 4:27:20 PM -38.0919 -35.6699 Right
4:27:21 PM -57.6676 -98.3272 Left
4:27:22 PM 5.99994 10.97151 5.999981 14.49997 4:27:22 PM -84.253 -108.996 Left
4:27:23 PM 5.99402 10.98467 5.999981 14.4998 4:27:23 PM -71.6522 -106.932 Left
4:27:24 PM 6.028559 11.02058 5.999981 14.49996 4:27:24 PM -87.5358 -134.778 Left
4:27:25 PM 6.06708 11.05093 5.999981 14.49985 4:27:25 PM -101.506 -158.424 Left
4:27:26 PM 6.128067 11.084 5.999981 14.4998 4:27:26 PM -110.811 -14.7392 Right
4:27:27 PM 6.197443 11.08626 5.999981 14.49997 4:27:27 PM -133.208 -12.8842 Right
4:27:28 PM 6.245965 11.09443 5.999981 14.49997 4:27:28 PM -109.782 -12.8407 Right
4:27:29 PM 6.309918 11.11571 5.999981 14.49985 4:27:29 PM -84.8274 -246.026 Left
4:27:30 PM 6.360104 11.15333 5.999981 14.49997
4:27:31 PM 6.407171 11.1833 5.999981 14.49996 4:27:31 PM -84.931 -13.0222 Right
4:27:32 PM 6.438076 11.22461 5.999981 14.4998 4:27:32 PM -80.9943 -365.304 Left
4:27:33 PM -66.9843 -12.9446 Right
4:27:34 PM 6.470629 11.26311 5.999981 14.49997 4:27:34 PM -64.1645 -13.0147 Right
4:27:35 PM 6.495314 11.30925 5.999981 14.49985 4:27:35 PM -51.7825 -606.21 Left
4:27:36 PM 6.513294 11.36181 5.999981 14.49996 4:27:36 PM -46.6 -365.382 Left
4:27:37 PM 6.534553 11.41395 5.999981 14.49996 4:27:37 PM -50.432 -246.373 Left
4:27:38 PM 6.557011 11.47013 5.999981 14.49997 4:27:38 PM -61.3928 -200.706 Left
4:27:39 PM 6.575093 11.51267 5.999981 14.49996 4:27:39 PM -60.4424 -178.61 Left
4:27:40 PM 6.602152 11.5899 5.999981 14.4998 4:27:40 PM -53.7903 -168.411 Left
4:27:41 PM 6.612155 11.63607 5.999981 14.49996 4:27:41 PM -49.5158 -12.7875 Right
4:27:42 PM 6.639353 11.70051 5.999981 14.49985 4:27:42 PM -63.1739 -13.2962 Right
4:27:43 PM 6.657452 11.74518 5.999981 14.4998 4:27:43 PM -57.7977 -17.5059 Right
4:27:44 PM 6.671871 11.79709 5.999981 14.49996 4:27:44 PM -31.771 -13.7893 Right
4:27:45 PM 6.676064 11.86286 5.999981 14.49985 4:27:45 PM -30.4718 -168.78 Left
4:27:46 PM -30.5825 -13.5749 Right
4:27:47 PM 6.683097 11.91554 5.999981 14.49985
4:27:48 PM 6.670757 11.96261 5.999981 14.49997 4:27:48 PM -19.5505 -15.5052 Right
4:27:49 PM 6.668495 11.97244 5.999981 14.49997 4:27:49 PM -12.5221 -25.5081 Left
4:27:50 PM 6.668192 11.97108 5.999981 14.49985 4:27:50 PM -15.2574 -18.2345 Left
4:27:51 PM 6.668219 11.97125 5.999981 14.49985 4:27:51 PM -22.3541 -15.4667 Right
4:27:52 PM 6.670714 11.97234 5.999981 14.49985 4:27:52 PM -12.9883 -15.4975 Left
4:27:53 PM 6.670653 11.97348 5.999981 14.49985
4:27:54 PM 6.668746 11.97258 5.999981 14.49997 4:27:54 PM -14.6944 -22.5089 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
250
X Z X Z L R
4:26:07 PM 6 9.5 6 14.5
4:26:08 PM 6 9.5 6 14.5
4:26:09 PM 6 9.5 6 14.5
4:26:10 PM 6 9.5 6 14.5
4:26:12 PM 5.99493 9.500999 5.999981 14.49996
4:26:13 PM 5.985316 9.503292 5.999981 14.49997
4:26:14 PM 5.977532 9.508522 5.999981 14.4998
4:26:15 PM 5.969895 9.521795 5.999981 14.4998
4:26:16 PM 5.962986 9.527066 5.999981 14.49985
4:26:17 PM 5.958001 9.536101 5.999981 14.49996
4:26:57 PM 6 9.5 6 14.5
4:26:58 PM 6 9.5 6 14.5
4:27:00 PM 6 9.5 6 14.5
4:27:01 PM 6 9.5 6 14.5
4:27:02 PM 6.000072 9.500964 5.999981 14.4998
4:27:03 PM 5.990367 9.502106 5.999981 14.49997
4:27:04 PM 6.011034 9.59257 5.999981 14.49985 4:27:04 PM -24.1753 -13.7776 Right
4:27:05 PM 6.012823 9.591856 5.999981 14.49985 4:27:05 PM -35.0501 -15.2392 Right
4:27:06 PM 6.014133 9.593542 5.999981 14.49997 4:27:06 PM -22.8171 -15.3363 Right
4:27:07 PM 6.034587 9.847871 5.999981 14.49985 4:27:07 PM -15.3375 -13.9715 Right
4:27:08 PM 6.054775 10.10355 5.999981 14.49985 4:27:08 PM -21.0661 -15.1408 Right
4:27:09 PM -23.4253 -16.987 Right
4:27:10 PM 6.056666 10.10434 5.999981 14.49997 4:27:10 PM -19.6587 -17.0278 Right
4:27:11 PM 6.056307 10.14029 5.999981 14.49996 4:27:11 PM -15.8541 -21.8172 Left
4:27:12 PM 6.041775 10.39607 5.999981 14.4998 4:27:12 PM -18.5775 -21.0774 Left
4:27:13 PM 6.038926 10.39728 5.999981 14.4998 4:27:13 PM -18.5231 -19.2477 Left
4:27:14 PM 6.039114 10.47541 5.999981 14.49996 4:27:14 PM -25.4049 -19.3215 Right
4:27:15 PM 6.047258 10.69636 5.999981 14.49985
4:27:16 PM 6.048785 10.69682 5.999981 14.49985 4:27:16 PM -26.9319 -23.9316 Right
4:27:17 PM 6.026943 10.95504 5.999981 14.4998 4:27:17 PM -22.2185 -25.896 Left
4:27:18 PM 6.024652 10.95601 5.999981 14.49996 4:27:18 PM -36.181 -41.3379 Left
4:27:19 PM 6.019865 10.95852 5.999981 14.49985 4:27:19 PM -36.2427 -40.3296 Left
4:27:20 PM 6.006553 10.96539 5.999981 14.49985 4:27:20 PM -38.0919 -35.6699 Right
4:27:21 PM -57.6676 -98.3272 Left
4:27:22 PM 5.99994 10.97151 5.999981 14.49997 4:27:22 PM -84.253 -108.996 Left
4:27:23 PM 5.99402 10.98467 5.999981 14.4998 4:27:23 PM -71.6522 -106.932 Left
4:27:24 PM 6.028559 11.02058 5.999981 14.49996 4:27:24 PM -87.5358 -134.778 Left
4:27:25 PM 6.06708 11.05093 5.999981 14.49985 4:27:25 PM -101.506 -158.424 Left
4:27:26 PM 6.128067 11.084 5.999981 14.4998 4:27:26 PM -110.811 -14.7392 Right
4:27:27 PM 6.197443 11.08626 5.999981 14.49997 4:27:27 PM -133.208 -12.8842 Right
4:27:28 PM 6.245965 11.09443 5.999981 14.49997 4:27:28 PM -109.782 -12.8407 Right
4:27:29 PM 6.309918 11.11571 5.999981 14.49985 4:27:29 PM -84.8274 -246.026 Left
4:27:30 PM 6.360104 11.15333 5.999981 14.49997
4:27:31 PM 6.407171 11.1833 5.999981 14.49996 4:27:31 PM -84.931 -13.0222 Right
4:27:32 PM 6.438076 11.22461 5.999981 14.4998 4:27:32 PM -80.9943 -365.304 Left
4:27:33 PM -66.9843 -12.9446 Right
4:27:34 PM 6.470629 11.26311 5.999981 14.49997 4:27:34 PM -64.1645 -13.0147 Right
4:27:35 PM 6.495314 11.30925 5.999981 14.49985 4:27:35 PM -51.7825 -606.21 Left
4:27:36 PM 6.513294 11.36181 5.999981 14.49996 4:27:36 PM -46.6 -365.382 Left
4:27:37 PM 6.534553 11.41395 5.999981 14.49996 4:27:37 PM -50.432 -246.373 Left
4:27:38 PM 6.557011 11.47013 5.999981 14.49997 4:27:38 PM -61.3928 -200.706 Left
4:27:39 PM 6.575093 11.51267 5.999981 14.49996 4:27:39 PM -60.4424 -178.61 Left
4:27:40 PM 6.602152 11.5899 5.999981 14.4998 4:27:40 PM -53.7903 -168.411 Left
4:27:41 PM 6.612155 11.63607 5.999981 14.49996 4:27:41 PM -49.5158 -12.7875 Right
4:27:42 PM 6.639353 11.70051 5.999981 14.49985 4:27:42 PM -63.1739 -13.2962 Right
4:27:43 PM 6.657452 11.74518 5.999981 14.4998 4:27:43 PM -57.7977 -17.5059 Right
4:27:44 PM 6.671871 11.79709 5.999981 14.49996 4:27:44 PM -31.771 -13.7893 Right
4:27:45 PM 6.676064 11.86286 5.999981 14.49985 4:27:45 PM -30.4718 -168.78 Left
4:27:46 PM -30.5825 -13.5749 Right
4:27:47 PM 6.683097 11.91554 5.999981 14.49985
4:27:48 PM 6.670757 11.96261 5.999981 14.49997 4:27:48 PM -19.5505 -15.5052 Right
4:27:49 PM 6.668495 11.97244 5.999981 14.49997 4:27:49 PM -12.5221 -25.5081 Left
4:27:50 PM 6.668192 11.97108 5.999981 14.49985 4:27:50 PM -15.2574 -18.2345 Left
4:27:51 PM 6.668219 11.97125 5.999981 14.49985 4:27:51 PM -22.3541 -15.4667 Right
4:27:52 PM 6.670714 11.97234 5.999981 14.49985 4:27:52 PM -12.9883 -15.4975 Left
4:27:53 PM 6.670653 11.97348 5.999981 14.49985
4:27:54 PM 6.668746 11.97258 5.999981 14.49997 4:27:54 PM -14.6944 -22.5089 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
251
Uniform Obstruction with Left Scatter Sample 1
Motion Path Graph - Uniform obstruction with Left Scatter – Sample 1.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
29-6-9-47
CARMI Child
252
Combined logs - Uniform obstruction with Left Scatter - Sample 1.
X Z X Z L R
9:47:12 AM 6 9.5 6 14.5
9:47:13 AM 6 9.5 6 14.5
9:47:14 AM 6 9.5 6 14.5
9:47:15 AM 6 9.5 6 14.5
9:47:16 AM 5.987337 9.501452 5.999981 14.49985
9:47:18 AM 5.988928 9.500646 5.999981 14.49997
9:47:19 AM 5.991797 9.500566 5.999981 14.49996
9:47:20 AM 5.992589 9.501349 5.999981 14.49985
9:47:21 AM 5.993534 9.501087 5.999981 14.49996
9:47:22 AM 5.995075 9.501016 5.999981 14.49997
9:47:23 AM 5.995943 9.500745 5.999981 14.49996
9:47:24 AM 5.995262 9.483018 5.999981 14.49996
9:47:25 AM 6.003499 9.420257 5.999981 14.49997
9:47:26 AM 6.023458 9.38566 5.999981 14.49996
9:47:27 AM 6.054671 9.353238 5.999981 14.49985
9:47:28 AM 6.090059 9.319435 5.999981 14.4998
9:47:30 AM 6.121851 9.301648 5.999981 14.4998
9:47:31 AM 6.17718 9.271253 5.999981 14.4998
9:47:32 AM 6.153265 9.288312 5.999981 14.49996 9:47:32 AM -115.903 -136.007 Left
9:47:33 AM 6.093799 9.311602 5.999981 14.49997 9:47:33 AM -113.891 -140.081 Left
9:47:34 AM 6.049867 9.339276 5.999981 14.49985 9:47:34 AM -115.026 -130.319 Left
9:47:35 AM 6.008853 9.37044 5.999981 14.4998 9:47:35 AM -117.518 -126.369 Left
9:47:36 AM 5.963525 9.421406 5.999981 14.4998 9:47:36 AM -114.813 -126.778 Left
9:47:37 AM 5.929592 9.469126 5.999981 14.49997 9:47:37 AM -115.132 -121.537 Left
9:47:38 AM 5.906971 9.505761 5.999981 14.4998
9:47:39 AM 5.885407 9.568006 5.999981 14.49997 9:47:39 AM -115.167 -119.052 Left
9:47:40 AM -114.555 -119.626 Left
9:47:41 AM 5.870071 9.63638 5.999981 14.4998 9:47:41 AM -115.13 -117.879 Left
9:47:42 AM 5.865932 9.661238 5.999981 14.49996 9:47:42 AM -117.715 -111.898 Right
9:47:43 AM 5.863786 9.890822 5.999981 14.49985 9:47:43 AM -122.866 -109.72 Right
9:47:44 AM 5.860491 10.24262 5.999981 14.4998 9:47:44 AM -126.129 -111.446 Right
9:47:45 AM 5.857299 10.58391 5.999981 14.49985 9:47:45 AM -128.219 -112.273 Right
9:47:46 AM 5.854057 10.93046 5.999981 14.49997 9:47:46 AM -126.979 -111.443 Right
9:47:47 AM 5.838756 11.05261 5.999981 14.49997 9:47:47 AM -125.024 -116.554 Right
9:47:48 AM 5.830475 11.05593 5.999981 14.49997 9:47:48 AM -124.047 -117.462 Right
9:47:49 AM 5.822028 11.06103 5.999981 14.4998 9:47:49 AM -142.527 -113.971 Right
9:47:51 AM 5.816976 11.06668 5.999981 14.49996 9:47:51 AM -142.48 -115.723 Right
9:47:52 AM 5.811285 11.07549 5.999981 14.49985 9:47:52 AM -134.459 -123.817 Right
9:47:53 AM 5.831675 11.09174 5.999981 14.49996 9:47:53 AM -144.642 -123.288 Right
9:47:54 AM 5.87175 11.10656 5.999981 14.49996 9:47:54 AM -149.996 -121.005 Right
9:47:55 AM 5.93866 11.12216 5.999981 14.4998 9:47:55 AM -148.146 -125.495 Right
9:47:56 AM 5.990708 11.12964 5.999981 14.49996 9:47:56 AM -150.659 -129.23 Right
9:47:57 AM 6.028641 11.12573 5.999981 14.4998 9:47:57 AM -154.512 -125.42 Right
9:47:58 AM 6.069986 11.1176 5.999981 14.49985 9:47:58 AM -154.828 -118.768 Right
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
253
Uniform Obstruction with Left Scatter Sample 2
Motion Path Graph - Uniform obstruction with Left Scatter – Sample 2.
Combined logs - Uniform obstruction with Left Scatter - Sample 2.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
29-6-9-48
CARMI Child
X Z X Z L R
9:48:03 AM 6 9.5 6 14.5
9:48:04 AM 6 9.5 6 14.5
9:48:05 AM 6 9.5 6 14.5
9:48:07 AM 5.989539 9.500969 5.999981 14.4998
9:48:08 AM 5.984222 9.48856 5.999981 14.4998
9:48:09 AM 5.960499 9.443511 5.999981 14.4998
9:48:10 AM 5.921671 9.415379 5.999981 14.49985
9:48:11 AM 5.913706 9.40899 5.999981 14.49996
9:48:12 AM 5.955582 9.448895 5.999981 14.49985 9:48:12 AM -135.086 -115.03 Right
9:48:13 AM 5.991875 9.495641 5.999981 14.4998 9:48:13 AM -137.806 -107.979 Right
9:48:14 AM 6.020525 9.53996 5.999981 14.49985 9:48:14 AM -119.496 -116.305 Right
9:48:15 AM -124.849 -116.264 Right
9:48:16 AM 6.04394 9.594741 5.999981 14.4998 9:48:16 AM -132.185 -112.092 Right
9:48:17 AM 6.064877 9.645852 5.999981 14.49985 9:48:17 AM -122.547 -111.827 Right
9:48:18 AM 6.074235 9.685211 5.999981 14.4998 9:48:18 AM -116.574 -114.748 Right
9:48:19 AM 6.076669 9.685261 5.999981 14.4998 9:48:19 AM -119.221 -112.346 Right
9:48:20 AM 6.078529 9.685247 5.999981 14.49985 9:48:20 AM -118.702 -112.932 Right
9:48:21 AM 6.079652 9.690499 5.999981 14.49996 9:48:21 AM -115.761 -115.03 Right
9:48:22 AM 6.07695 10.02347 5.999981 14.4998
9:48:23 AM 6.07415 10.3699 5.999981 14.49996 9:48:23 AM -120.879 -114.956 Right
9:48:24 AM 6.071434 10.70584 5.999981 14.49996 9:48:24 AM -124.651 -114.623 Right
9:48:25 AM 6.068777 11.02084 5.999981 14.4998 9:48:25 AM -128.483 -111.964 Right
9:48:26 AM -132.286 -111.381 Right
9:48:27 AM 6.081495 11.02713 5.999981 14.4998 9:48:27 AM -116.973 -110.039 Right
9:48:28 AM 6.092523 11.03154 5.999981 14.49997 9:48:28 AM -116.035 -119.467 Left
9:48:29 AM 6.101803 11.03939 5.999981 14.4998 9:48:29 AM -118.244 -130.927 Left
9:48:30 AM 6.108337 11.04859 5.999981 14.49996 9:48:30 AM -114.67 -130.079 Left
9:48:31 AM 6.075421 11.07258 5.999981 14.49996 9:48:31 AM -114.446 -133.945 Left
9:48:32 AM 6.020397 11.10131 5.999981 14.49997 9:48:32 AM -115.426 -138.165 Left
9:48:33 AM 5.969902 11.12522 5.999981 14.49985 9:48:33 AM -113.648 -139.498 Left
9:48:34 AM 5.929872 11.13881 5.999981 14.49997
9:48:35 AM -111.132 -141.565 Left
9:48:36 AM 5.871634 11.14168 5.999981 14.49985 9:48:36 AM -110.162 -147.574 Left
9:48:37 AM 5.82583 11.13651 5.999981 14.4998 9:48:37 AM -116.455 -149.18 Left
9:48:38 AM 5.772073 11.13676 5.999981 14.49997 9:48:38 AM -115.975 -144.742 Left
9:48:39 AM 5.730596 11.14291 5.999981 14.49985 9:48:39 AM -115.315 -139.473 Left
9:48:40 AM 5.672811 11.15937 5.999981 14.49985 9:48:40 AM -116.231 -141.171 Left
9:48:41 AM 5.62881 11.17478 5.999981 14.4998 9:48:41 AM #NAME? #NAME? Mid
9:48:42 AM 5.590019 11.19199 5.999981 14.49996 9:48:42 AM -117.939 -138.012 Left
9:48:43 AM 5.550181 11.22446 5.999981 14.49985 9:48:43 AM -115.684 -133.22 Left
9:48:44 AM 5.511716 11.26664 5.999981 14.4998 9:48:44 AM -117.732 -133.865 Left
9:48:45 AM 5.466749 11.32162 5.999981 14.4998 9:48:45 AM -125.388 -132.517 Left
9:48:46 AM 5.450275 11.36241 5.999981 14.4998
9:48:47 AM 5.42876 11.40299 5.999981 14.49997 9:48:47 AM -119.432 -123.327 Left
9:48:48 AM -118.308 -116.1 Right
9:48:49 AM 5.412917 11.45558 5.999981 14.49985 9:48:49 AM -116.318 -128.303 Left
9:48:50 AM 5.381623 11.52646 5.999981 14.49997 9:48:50 AM -119.223 -130.755 Left
9:48:51 AM 5.365721 11.56622 5.999981 14.49985 9:48:51 AM -120.13 -129.811 Left
9:48:52 AM 5.340682 11.63942 5.999981 14.4998 9:48:52 AM -115.879 -127.178 Left
9:48:53 AM 5.318073 11.68324 5.999981 14.49996 9:48:53 AM -119.028 -132.238 Left
9:48:54 AM 5.300752 11.72077 5.999981 14.49996 9:48:54 AM -124.315 -133.235 Left
9:48:55 AM 5.288986 11.77509 5.999981 14.49985 9:48:55 AM -125.666 -124.846 Right
9:48:56 AM 5.277324 11.82756 5.999981 14.49996 9:48:56 AM -125.324 -127.783 Left
9:48:57 AM 5.279469 11.88951 5.999981 14.4998 9:48:57 AM -124.506 -128.075 Left
9:48:59 AM 5.281568 11.9309 5.999981 14.49985 9:48:59 AM -125.409 -124.389 Right
9:49:00 AM 5.285477 11.94316 5.999981 14.49996 9:49:00 AM -120.673 -119.517 Right
9:49:01 AM 5.285779 11.94318 5.999981 14.49985 9:49:01 AM -123.937 -120.516 Right
9:49:02 AM 5.284322 11.94364 5.999981 14.49997 9:49:02 AM -125.261 -122.851 Right
9:49:04 AM 5.282274 11.94449 5.999981 14.49985 9:49:04 AM -126.032 -122.59 Right
9:49:05 AM 5.282291 11.94449 5.999981 14.49997 9:49:05 AM -126.888 -120.201 Right
9:49:06 AM 5.28231 11.94448 5.999981 14.49996 9:49:06 AM -125.039 -122.037 Right
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
254
X Z X Z L R
9:48:03 AM 6 9.5 6 14.5
9:48:04 AM 6 9.5 6 14.5
9:48:05 AM 6 9.5 6 14.5
9:48:07 AM 5.989539 9.500969 5.999981 14.4998
9:48:08 AM 5.984222 9.48856 5.999981 14.4998
9:48:09 AM 5.960499 9.443511 5.999981 14.4998
9:48:10 AM 5.921671 9.415379 5.999981 14.49985
9:48:11 AM 5.913706 9.40899 5.999981 14.49996
9:48:12 AM 5.955582 9.448895 5.999981 14.49985 9:48:12 AM -135.086 -115.03 Right
9:48:13 AM 5.991875 9.495641 5.999981 14.4998 9:48:13 AM -137.806 -107.979 Right
9:48:14 AM 6.020525 9.53996 5.999981 14.49985 9:48:14 AM -119.496 -116.305 Right
9:48:15 AM -124.849 -116.264 Right
9:48:16 AM 6.04394 9.594741 5.999981 14.4998 9:48:16 AM -132.185 -112.092 Right
9:48:17 AM 6.064877 9.645852 5.999981 14.49985 9:48:17 AM -122.547 -111.827 Right
9:48:18 AM 6.074235 9.685211 5.999981 14.4998 9:48:18 AM -116.574 -114.748 Right
9:48:19 AM 6.076669 9.685261 5.999981 14.4998 9:48:19 AM -119.221 -112.346 Right
9:48:20 AM 6.078529 9.685247 5.999981 14.49985 9:48:20 AM -118.702 -112.932 Right
9:48:21 AM 6.079652 9.690499 5.999981 14.49996 9:48:21 AM -115.761 -115.03 Right
9:48:22 AM 6.07695 10.02347 5.999981 14.4998
9:48:23 AM 6.07415 10.3699 5.999981 14.49996 9:48:23 AM -120.879 -114.956 Right
9:48:24 AM 6.071434 10.70584 5.999981 14.49996 9:48:24 AM -124.651 -114.623 Right
9:48:25 AM 6.068777 11.02084 5.999981 14.4998 9:48:25 AM -128.483 -111.964 Right
9:48:26 AM -132.286 -111.381 Right
9:48:27 AM 6.081495 11.02713 5.999981 14.4998 9:48:27 AM -116.973 -110.039 Right
9:48:28 AM 6.092523 11.03154 5.999981 14.49997 9:48:28 AM -116.035 -119.467 Left
9:48:29 AM 6.101803 11.03939 5.999981 14.4998 9:48:29 AM -118.244 -130.927 Left
9:48:30 AM 6.108337 11.04859 5.999981 14.49996 9:48:30 AM -114.67 -130.079 Left
9:48:31 AM 6.075421 11.07258 5.999981 14.49996 9:48:31 AM -114.446 -133.945 Left
9:48:32 AM 6.020397 11.10131 5.999981 14.49997 9:48:32 AM -115.426 -138.165 Left
9:48:33 AM 5.969902 11.12522 5.999981 14.49985 9:48:33 AM -113.648 -139.498 Left
9:48:34 AM 5.929872 11.13881 5.999981 14.49997
9:48:35 AM -111.132 -141.565 Left
9:48:36 AM 5.871634 11.14168 5.999981 14.49985 9:48:36 AM -110.162 -147.574 Left
9:48:37 AM 5.82583 11.13651 5.999981 14.4998 9:48:37 AM -116.455 -149.18 Left
9:48:38 AM 5.772073 11.13676 5.999981 14.49997 9:48:38 AM -115.975 -144.742 Left
9:48:39 AM 5.730596 11.14291 5.999981 14.49985 9:48:39 AM -115.315 -139.473 Left
9:48:40 AM 5.672811 11.15937 5.999981 14.49985 9:48:40 AM -116.231 -141.171 Left
9:48:41 AM 5.62881 11.17478 5.999981 14.4998 9:48:41 AM #NAME? #NAME? Mid
9:48:42 AM 5.590019 11.19199 5.999981 14.49996 9:48:42 AM -117.939 -138.012 Left
9:48:43 AM 5.550181 11.22446 5.999981 14.49985 9:48:43 AM -115.684 -133.22 Left
9:48:44 AM 5.511716 11.26664 5.999981 14.4998 9:48:44 AM -117.732 -133.865 Left
9:48:45 AM 5.466749 11.32162 5.999981 14.4998 9:48:45 AM -125.388 -132.517 Left
9:48:46 AM 5.450275 11.36241 5.999981 14.4998
9:48:47 AM 5.42876 11.40299 5.999981 14.49997 9:48:47 AM -119.432 -123.327 Left
9:48:48 AM -118.308 -116.1 Right
9:48:49 AM 5.412917 11.45558 5.999981 14.49985 9:48:49 AM -116.318 -128.303 Left
9:48:50 AM 5.381623 11.52646 5.999981 14.49997 9:48:50 AM -119.223 -130.755 Left
9:48:51 AM 5.365721 11.56622 5.999981 14.49985 9:48:51 AM -120.13 -129.811 Left
9:48:52 AM 5.340682 11.63942 5.999981 14.4998 9:48:52 AM -115.879 -127.178 Left
9:48:53 AM 5.318073 11.68324 5.999981 14.49996 9:48:53 AM -119.028 -132.238 Left
9:48:54 AM 5.300752 11.72077 5.999981 14.49996 9:48:54 AM -124.315 -133.235 Left
9:48:55 AM 5.288986 11.77509 5.999981 14.49985 9:48:55 AM -125.666 -124.846 Right
9:48:56 AM 5.277324 11.82756 5.999981 14.49996 9:48:56 AM -125.324 -127.783 Left
9:48:57 AM 5.279469 11.88951 5.999981 14.4998 9:48:57 AM -124.506 -128.075 Left
9:48:59 AM 5.281568 11.9309 5.999981 14.49985 9:48:59 AM -125.409 -124.389 Right
9:49:00 AM 5.285477 11.94316 5.999981 14.49996 9:49:00 AM -120.673 -119.517 Right
9:49:01 AM 5.285779 11.94318 5.999981 14.49985 9:49:01 AM -123.937 -120.516 Right
9:49:02 AM 5.284322 11.94364 5.999981 14.49997 9:49:02 AM -125.261 -122.851 Right
9:49:04 AM 5.282274 11.94449 5.999981 14.49985 9:49:04 AM -126.032 -122.59 Right
9:49:05 AM 5.282291 11.94449 5.999981 14.49997 9:49:05 AM -126.888 -120.201 Right
9:49:06 AM 5.28231 11.94448 5.999981 14.49996 9:49:06 AM -125.039 -122.037 Right
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
255
Uniform Obstruction with Left Scatter Sample 3
Motion Path Graph - Uniform obstruction with Left Scatter – Sample 3.
Combined logs - Uniform obstruction with Left Scatter - Sample 3.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.25.45.65.866.26.46.66.87
29-6-9-49
CARMI Child
X Z X Z L R
9:49:23 AM 6 9.5 6 14.5
9:49:24 AM 6 9.5 6 14.5
9:49:27 AM 5.990247 9.501098 5.999981 14.49985
9:49:28 AM 6.006161 9.585439 5.999981 14.49985 9:49:28 AM -112.068 -117.462 Left
9:49:29 AM -121.448 -111.362 Right
9:49:30 AM 6.007299 9.586416 5.999981 14.49997 9:49:30 AM -120.412 -111.959 Right
9:49:31 AM 6.009 9.587243 5.999981 14.49985 9:49:31 AM -119.405 -111.379 Right
9:49:32 AM 6.010558 9.587162 5.999981 14.49996
9:49:33 AM 6.012883 9.587519 5.999981 14.49985 9:49:33 AM -118.443 -110.829 Right
9:49:34 AM 6.01392 9.858509 5.999981 14.49985 9:49:34 AM -117.534 -113.545 Right
9:49:35 AM 6.015444 10.21553 5.999981 14.49985 9:49:35 AM -121.3 -114.512 Right
9:49:36 AM 6.017012 10.58303 5.999981 14.49997 9:49:36 AM -125.439 -114.659 Right
9:49:37 AM 6.018445 10.91909 5.999981 14.4998 9:49:37 AM -129.452 -111.18 Right
9:49:38 AM 6.021758 11.08074 5.999981 14.49997 9:49:38 AM -126.899 -110.434 Right
9:49:39 AM -117.661 -112.276 Right
9:49:40 AM 6.031547 11.08267 5.999981 14.4998 9:49:40 AM -118.313 -117.977 Right
9:49:41 AM 6.047211 11.07371 5.999981 14.49996
9:49:42 AM 6.052978 11.07791 5.999981 14.49997 9:49:42 AM -119.363 -125.76 Left
9:49:43 AM 6.059465 11.08437 5.999981 14.49996 9:49:43 AM -117.24 -123.76 Left
9:49:44 AM 6.063909 11.0908 5.999981 14.49985 9:49:44 AM -112.777 -129.06 Left
9:49:45 AM 6.0028 11.13235 5.999981 14.49985 9:49:45 AM -115.587 -135.043 Left
9:49:46 AM 5.96297 11.15077 5.999981 14.49996 9:49:46 AM -116.001 -137.089 Left
9:49:47 AM 5.921112 11.16725 5.999981 14.4998 9:49:47 AM -114.439 -138.936 Left
9:49:48 AM 5.859396 11.18007 5.999981 14.49996 9:49:48 AM -114.199 -142.834 Left
9:49:49 AM -116.969 -146.289 Left
9:49:50 AM 5.80068 11.18311 5.999981 14.4998 9:49:50 AM -116.233 -148.387 Left
9:49:51 AM 5.730271 11.1985 5.999981 14.49996 9:49:51 AM -115.684 -142.432 Left
9:49:52 AM 5.680724 11.21501 5.999981 14.4998 9:49:52 AM -119.419 -137.763 Left
9:49:53 AM 5.648708 11.23523 5.999981 14.49997 9:49:53 AM -116.15 -135.482 Left
9:49:54 AM 5.61039 11.26261 5.999981 14.49985
9:49:55 AM 5.573277 11.30005 5.999981 14.49985 9:49:55 AM -120.8 -135.158 Left
9:49:56 AM 5.534227 11.34663 5.999981 14.49997 9:49:56 AM -121.587 -130.395 Left
9:49:57 AM 5.51938 11.37982 5.999981 14.49985 9:49:57 AM -133.881 -125.947 Right
9:49:58 AM 5.504006 11.42471 5.999981 14.49996 9:49:58 AM -122.529 -118.344 Right
9:49:59 AM 5.484873 11.47762 5.999981 14.4998 9:49:59 AM -124.391 -128.29 Left
9:50:00 AM 5.468014 11.51742 5.999981 14.49997 9:50:00 AM -120.77 -129.273 Left
9:50:01 AM -124.157 -128.181 Left
9:50:02 AM 5.454309 11.56304 5.999981 14.49997 9:50:02 AM -121.817 -129.561 Left
9:50:03 AM 5.433574 11.61482 5.999981 14.49997 9:50:03 AM -120.675 -129.265 Left
9:50:04 AM 5.413979 11.66395 5.999981 14.4998 9:50:04 AM -122.72 -131.32 Left
9:50:05 AM 5.400045 11.70569 5.999981 14.49996 9:50:05 AM -120.805 -131.515 Left
9:50:06 AM 5.378882 11.7506 5.999981 14.49996
9:50:07 AM 5.368788 11.79686 5.999981 14.49985 9:50:07 AM -125.437 -132.457 Left
9:50:08 AM 5.353352 11.84335 5.999981 14.49985 9:50:08 AM -118.325 -125.026 Left
9:50:09 AM 5.344953 11.89445 5.999981 14.49996 9:50:09 AM -123.876 -127.563 Left
9:50:10 AM 5.341011 11.95598 5.999981 14.4998 9:50:10 AM -125.192 -130.355 Left
9:50:11 AM 5.341657 11.98195 5.999981 14.4998 9:50:11 AM -127.904 -128.598 Left
9:50:12 AM -124.013 -120.393 Right
9:50:13 AM 5.343054 11.98208 5.999981 14.49997 9:50:13 AM -120.808 -121.112 Left
9:50:14 AM 5.342436 11.98178 5.999981 14.49985 9:50:14 AM -124.526 -127.061 Left
9:50:15 AM 5.340897 11.98321 5.999981 14.49996 9:50:15 AM -126.05 -126.105 Left
9:50:16 AM 5.340421 11.98296 5.999981 14.49996 9:50:16 AM -126.808 -122.442 Right
9:50:17 AM 5.340442 11.98295 5.999981 14.49985 9:50:17 AM -126.582 -122.563 Right
9:50:18 AM 5.340468 11.98294 5.999981 14.4998
9:50:19 AM 5.340486 11.98294 5.999981 14.49985 9:50:19 AM -126.589 -122.571 Right
9:50:20 AM -126.626 -122.595 Right
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
256
X Z X Z L R
9:49:23 AM 6 9.5 6 14.5
9:49:24 AM 6 9.5 6 14.5
9:49:27 AM 5.990247 9.501098 5.999981 14.49985
9:49:28 AM 6.006161 9.585439 5.999981 14.49985 9:49:28 AM -112.068 -117.462 Left
9:49:29 AM -121.448 -111.362 Right
9:49:30 AM 6.007299 9.586416 5.999981 14.49997 9:49:30 AM -120.412 -111.959 Right
9:49:31 AM 6.009 9.587243 5.999981 14.49985 9:49:31 AM -119.405 -111.379 Right
9:49:32 AM 6.010558 9.587162 5.999981 14.49996
9:49:33 AM 6.012883 9.587519 5.999981 14.49985 9:49:33 AM -118.443 -110.829 Right
9:49:34 AM 6.01392 9.858509 5.999981 14.49985 9:49:34 AM -117.534 -113.545 Right
9:49:35 AM 6.015444 10.21553 5.999981 14.49985 9:49:35 AM -121.3 -114.512 Right
9:49:36 AM 6.017012 10.58303 5.999981 14.49997 9:49:36 AM -125.439 -114.659 Right
9:49:37 AM 6.018445 10.91909 5.999981 14.4998 9:49:37 AM -129.452 -111.18 Right
9:49:38 AM 6.021758 11.08074 5.999981 14.49997 9:49:38 AM -126.899 -110.434 Right
9:49:39 AM -117.661 -112.276 Right
9:49:40 AM 6.031547 11.08267 5.999981 14.4998 9:49:40 AM -118.313 -117.977 Right
9:49:41 AM 6.047211 11.07371 5.999981 14.49996
9:49:42 AM 6.052978 11.07791 5.999981 14.49997 9:49:42 AM -119.363 -125.76 Left
9:49:43 AM 6.059465 11.08437 5.999981 14.49996 9:49:43 AM -117.24 -123.76 Left
9:49:44 AM 6.063909 11.0908 5.999981 14.49985 9:49:44 AM -112.777 -129.06 Left
9:49:45 AM 6.0028 11.13235 5.999981 14.49985 9:49:45 AM -115.587 -135.043 Left
9:49:46 AM 5.96297 11.15077 5.999981 14.49996 9:49:46 AM -116.001 -137.089 Left
9:49:47 AM 5.921112 11.16725 5.999981 14.4998 9:49:47 AM -114.439 -138.936 Left
9:49:48 AM 5.859396 11.18007 5.999981 14.49996 9:49:48 AM -114.199 -142.834 Left
9:49:49 AM -116.969 -146.289 Left
9:49:50 AM 5.80068 11.18311 5.999981 14.4998 9:49:50 AM -116.233 -148.387 Left
9:49:51 AM 5.730271 11.1985 5.999981 14.49996 9:49:51 AM -115.684 -142.432 Left
9:49:52 AM 5.680724 11.21501 5.999981 14.4998 9:49:52 AM -119.419 -137.763 Left
9:49:53 AM 5.648708 11.23523 5.999981 14.49997 9:49:53 AM -116.15 -135.482 Left
9:49:54 AM 5.61039 11.26261 5.999981 14.49985
9:49:55 AM 5.573277 11.30005 5.999981 14.49985 9:49:55 AM -120.8 -135.158 Left
9:49:56 AM 5.534227 11.34663 5.999981 14.49997 9:49:56 AM -121.587 -130.395 Left
9:49:57 AM 5.51938 11.37982 5.999981 14.49985 9:49:57 AM -133.881 -125.947 Right
9:49:58 AM 5.504006 11.42471 5.999981 14.49996 9:49:58 AM -122.529 -118.344 Right
9:49:59 AM 5.484873 11.47762 5.999981 14.4998 9:49:59 AM -124.391 -128.29 Left
9:50:00 AM 5.468014 11.51742 5.999981 14.49997 9:50:00 AM -120.77 -129.273 Left
9:50:01 AM -124.157 -128.181 Left
9:50:02 AM 5.454309 11.56304 5.999981 14.49997 9:50:02 AM -121.817 -129.561 Left
9:50:03 AM 5.433574 11.61482 5.999981 14.49997 9:50:03 AM -120.675 -129.265 Left
9:50:04 AM 5.413979 11.66395 5.999981 14.4998 9:50:04 AM -122.72 -131.32 Left
9:50:05 AM 5.400045 11.70569 5.999981 14.49996 9:50:05 AM -120.805 -131.515 Left
9:50:06 AM 5.378882 11.7506 5.999981 14.49996
9:50:07 AM 5.368788 11.79686 5.999981 14.49985 9:50:07 AM -125.437 -132.457 Left
9:50:08 AM 5.353352 11.84335 5.999981 14.49985 9:50:08 AM -118.325 -125.026 Left
9:50:09 AM 5.344953 11.89445 5.999981 14.49996 9:50:09 AM -123.876 -127.563 Left
9:50:10 AM 5.341011 11.95598 5.999981 14.4998 9:50:10 AM -125.192 -130.355 Left
9:50:11 AM 5.341657 11.98195 5.999981 14.4998 9:50:11 AM -127.904 -128.598 Left
9:50:12 AM -124.013 -120.393 Right
9:50:13 AM 5.343054 11.98208 5.999981 14.49997 9:50:13 AM -120.808 -121.112 Left
9:50:14 AM 5.342436 11.98178 5.999981 14.49985 9:50:14 AM -124.526 -127.061 Left
9:50:15 AM 5.340897 11.98321 5.999981 14.49996 9:50:15 AM -126.05 -126.105 Left
9:50:16 AM 5.340421 11.98296 5.999981 14.49996 9:50:16 AM -126.808 -122.442 Right
9:50:17 AM 5.340442 11.98295 5.999981 14.49985 9:50:17 AM -126.582 -122.563 Right
9:50:18 AM 5.340468 11.98294 5.999981 14.4998
9:50:19 AM 5.340486 11.98294 5.999981 14.49985 9:50:19 AM -126.589 -122.571 Right
9:50:20 AM -126.626 -122.595 Right
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
257
Uniform Obstruction with Left Scatter Sample 4
Motion Path Graph - Uniform obstruction with Left Scatter – Sample 4.
Combined logs - Uniform with Left Scatter - Sample 4.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
29-6-9-50
CARMI Child
X Z X Z L R
9:50:35 AM 6 9.5 6 14.5
9:50:36 AM 6 9.5 6 14.5
9:50:38 AM 6 9.5 6 14.5
9:50:39 AM 5.992033 9.500932 5.999981 14.49996
9:50:40 AM 5.982918 9.495651 5.999981 14.49996
9:50:41 AM 5.965286 9.452658 5.999981 14.49996
9:50:43 AM 5.935833 9.396638 5.999981 14.49997
9:50:44 AM 5.900529 9.345466 5.999981 14.49996
9:50:45 AM 5.862112 9.30288 5.999981 14.49996
9:50:46 AM 5.835354 9.306749 5.999981 14.49985
9:50:47 AM 5.797246 9.296778 5.999981 14.49997
9:50:48 AM 5.751979 9.290651 5.999981 14.4998
9:50:49 AM 5.805103 9.300448 5.999981 14.49997 9:50:49 AM -141.224 -114.622 Right
9:50:50 AM 5.847622 9.310148 5.999981 14.49996 9:50:50 AM -140.256 -114.785 Right
9:50:51 AM 5.891947 9.33126 5.999981 14.4998 9:50:51 AM -137.189 -114.589 Right
9:50:52 AM -135.441 -112.594 Right
9:50:53 AM 5.927948 9.347473 5.999981 14.49996 9:50:53 AM -129.679 -112.388 Right
9:50:54 AM 5.972642 9.385774 5.999981 14.49985
9:50:55 AM 6.01306 9.436497 5.999981 14.49997 9:50:55 AM -128.985 -114.427 Right
9:50:56 AM 6.036311 9.466467 5.999981 14.49985 9:50:56 AM -128.68 -112.12 Right
9:50:57 AM 6.058016 9.510811 5.999981 14.49996 9:50:57 AM -123.546 -112.662 Right
9:50:58 AM 6.075562 9.555101 5.999981 14.49997 9:50:58 AM -121.452 -113.771 Right
9:50:59 AM 6.092412 9.600558 5.999981 14.49985 9:50:59 AM -123.72 -112.473 Right
9:51:00 AM 6.09944 9.633196 5.999981 14.49997 9:51:00 AM -122.155 -111.153 Right
9:51:01 AM 6.096567 9.749199 5.999981 14.49996 9:51:01 AM -113.865 -117.882 Left
9:51:02 AM 6.094957 9.750555 5.999981 14.49996 9:51:02 AM -111.771 -119.903 Left
9:51:03 AM -118.113 -115.477 Right
9:51:04 AM 6.100585 9.870809 5.999981 14.49996 9:51:04 AM -121.892 -113.187 Right
9:51:05 AM 6.10114 9.870079 5.999981 14.4998 9:51:05 AM -118.546 -114.644 Right
9:51:06 AM 6.095837 10.05738 5.999981 14.49985
9:51:07 AM 6.084424 10.39807 5.999981 14.4998 9:51:07 AM -121.816 -116.319 Right
9:51:08 AM 6.073164 10.73291 5.999981 14.4998 9:51:08 AM -126.438 -114.119 Right
9:51:09 AM 6.065636 11.0088 5.999981 14.4998 9:51:09 AM -129.306 -110.574 Right
9:51:10 AM 6.072446 11.0112 5.999981 14.49997 9:51:10 AM -120.818 -111.02 Right
9:51:11 AM 6.084439 11.0161 5.999981 14.49996 9:51:11 AM -116.894 -112.894 Right
9:51:12 AM 6.091426 11.02125 5.999981 14.49997 9:51:12 AM -117.755 -122.583 Left
9:51:13 AM -118.115 -128.913 Left
9:51:14 AM 6.097644 11.02645 5.999981 14.49997 9:51:14 AM -110.282 -127.571 Left
9:51:15 AM 6.075973 11.04914 5.999981 14.49985 9:51:15 AM -112.153 -138.25 Left
9:51:16 AM 6.030676 11.07591 5.999981 14.49985
9:51:17 AM 5.988955 11.09675 5.999981 14.4998 9:51:17 AM -117.982 -141.948 Left
9:51:18 AM 5.947705 11.10923 5.999981 14.49997 9:51:18 AM -113.099 -140.849 Left
9:51:19 AM 5.894887 11.11926 5.999981 14.4998 9:51:19 AM -111.674 -143.529 Left
9:51:20 AM 5.859352 11.11634 5.999981 14.49997 9:51:20 AM -113.138 -147.49 Left
9:51:21 AM 5.814218 11.11827 5.999981 14.49985 9:51:21 AM -115.187 -150.509 Left
9:51:22 AM 5.765913 11.1298 5.999981 14.49996 9:51:22 AM -119.04 -140.767 Left
9:51:23 AM -114.366 -137.759 Left
9:51:24 AM 5.719851 11.14345 5.999981 14.49985 9:51:24 AM -116.066 -139.195 Left
9:51:25 AM 5.674833 11.16323 5.999981 14.4998 9:51:25 AM -118.379 -141.961 Left
9:51:26 AM 5.636488 11.18308 5.999981 14.49997 9:51:26 AM -116.041 -137.193 Left
9:51:27 AM 5.593248 11.21056 5.999981 14.49985
9:51:28 AM 5.555815 11.2404 5.999981 14.49996 9:51:28 AM -116.584 -134.762 Left
9:51:29 AM 5.521909 11.27086 5.999981 14.4998 9:51:29 AM -117.846 -133.757 Left
9:51:30 AM 5.489244 11.31269 5.999981 14.49996 9:51:30 AM -121.019 -132.14 Left
9:51:31 AM 5.464964 11.36073 5.999981 14.49985 9:51:31 AM -125.493 -128.432 Left
9:51:32 AM 5.44524 11.40433 5.999981 14.49996 9:51:32 AM -117.556 -124.887 Left
9:51:33 AM 5.42282 11.45891 5.999981 14.49985 9:51:33 AM -115.972 -125.244 Left
9:51:34 AM -116.677 -127.455 Left
9:51:35 AM 5.39315 11.52384 5.999981 14.49996 9:51:35 AM -122.047 -129.672 Left
9:51:36 AM 5.38341 11.56829 5.999981 14.49997 9:51:36 AM -115.94 -125.697 Left
9:51:37 AM 5.357516 11.61091 5.999981 14.49996 9:51:37 AM -119.221 -130.562 Left
9:51:38 AM 5.34116 11.66051 5.999981 14.49985 9:51:38 AM -123.953 -132.117 Left
9:51:39 AM 5.322598 11.7019 5.999981 14.49996 9:51:39 AM -123.509 -129.53 Left
9:51:40 AM 5.314125 11.74172 5.999981 14.49996
9:51:41 AM 5.304253 11.7815 5.999981 14.49997 9:51:41 AM -121.5 -126.523 Left
9:51:42 AM 5.296102 11.83753 5.999981 14.49996 9:51:42 AM -124.387 -129.128 Left
9:51:43 AM 5.300335 11.88195 5.999981 14.49985 9:51:43 AM -128.3 -129.23 Left
9:51:44 AM 5.299342 11.88287 5.999981 14.49996 9:51:44 AM -124.344 -123.061 Right
9:51:45 AM -124.703 -123.206 Right
9:51:46 AM 5.297827 11.88361 5.999981 14.49985 9:51:46 AM -125.292 -127.356 Left
9:51:47 AM 5.295514 11.88386 5.999981 14.49996 9:51:47 AM -127.382 -123.893 Right
9:51:48 AM 5.29448 11.88433 5.999981 14.49985 9:51:48 AM -127.344 -122.725 Right
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
258
X Z X Z L R
9:50:35 AM 6 9.5 6 14.5
9:50:36 AM 6 9.5 6 14.5
9:50:38 AM 6 9.5 6 14.5
9:50:39 AM 5.992033 9.500932 5.999981 14.49996
9:50:40 AM 5.982918 9.495651 5.999981 14.49996
9:50:41 AM 5.965286 9.452658 5.999981 14.49996
9:50:43 AM 5.935833 9.396638 5.999981 14.49997
9:50:44 AM 5.900529 9.345466 5.999981 14.49996
9:50:45 AM 5.862112 9.30288 5.999981 14.49996
9:50:46 AM 5.835354 9.306749 5.999981 14.49985
9:50:47 AM 5.797246 9.296778 5.999981 14.49997
9:50:48 AM 5.751979 9.290651 5.999981 14.4998
9:50:49 AM 5.805103 9.300448 5.999981 14.49997 9:50:49 AM -141.224 -114.622 Right
9:50:50 AM 5.847622 9.310148 5.999981 14.49996 9:50:50 AM -140.256 -114.785 Right
9:50:51 AM 5.891947 9.33126 5.999981 14.4998 9:50:51 AM -137.189 -114.589 Right
9:50:52 AM -135.441 -112.594 Right
9:50:53 AM 5.927948 9.347473 5.999981 14.49996 9:50:53 AM -129.679 -112.388 Right
9:50:54 AM 5.972642 9.385774 5.999981 14.49985
9:50:55 AM 6.01306 9.436497 5.999981 14.49997 9:50:55 AM -128.985 -114.427 Right
9:50:56 AM 6.036311 9.466467 5.999981 14.49985 9:50:56 AM -128.68 -112.12 Right
9:50:57 AM 6.058016 9.510811 5.999981 14.49996 9:50:57 AM -123.546 -112.662 Right
9:50:58 AM 6.075562 9.555101 5.999981 14.49997 9:50:58 AM -121.452 -113.771 Right
9:50:59 AM 6.092412 9.600558 5.999981 14.49985 9:50:59 AM -123.72 -112.473 Right
9:51:00 AM 6.09944 9.633196 5.999981 14.49997 9:51:00 AM -122.155 -111.153 Right
9:51:01 AM 6.096567 9.749199 5.999981 14.49996 9:51:01 AM -113.865 -117.882 Left
9:51:02 AM 6.094957 9.750555 5.999981 14.49996 9:51:02 AM -111.771 -119.903 Left
9:51:03 AM -118.113 -115.477 Right
9:51:04 AM 6.100585 9.870809 5.999981 14.49996 9:51:04 AM -121.892 -113.187 Right
9:51:05 AM 6.10114 9.870079 5.999981 14.4998 9:51:05 AM -118.546 -114.644 Right
9:51:06 AM 6.095837 10.05738 5.999981 14.49985
9:51:07 AM 6.084424 10.39807 5.999981 14.4998 9:51:07 AM -121.816 -116.319 Right
9:51:08 AM 6.073164 10.73291 5.999981 14.4998 9:51:08 AM -126.438 -114.119 Right
9:51:09 AM 6.065636 11.0088 5.999981 14.4998 9:51:09 AM -129.306 -110.574 Right
9:51:10 AM 6.072446 11.0112 5.999981 14.49997 9:51:10 AM -120.818 -111.02 Right
9:51:11 AM 6.084439 11.0161 5.999981 14.49996 9:51:11 AM -116.894 -112.894 Right
9:51:12 AM 6.091426 11.02125 5.999981 14.49997 9:51:12 AM -117.755 -122.583 Left
9:51:13 AM -118.115 -128.913 Left
9:51:14 AM 6.097644 11.02645 5.999981 14.49997 9:51:14 AM -110.282 -127.571 Left
9:51:15 AM 6.075973 11.04914 5.999981 14.49985 9:51:15 AM -112.153 -138.25 Left
9:51:16 AM 6.030676 11.07591 5.999981 14.49985
9:51:17 AM 5.988955 11.09675 5.999981 14.4998 9:51:17 AM -117.982 -141.948 Left
9:51:18 AM 5.947705 11.10923 5.999981 14.49997 9:51:18 AM -113.099 -140.849 Left
9:51:19 AM 5.894887 11.11926 5.999981 14.4998 9:51:19 AM -111.674 -143.529 Left
9:51:20 AM 5.859352 11.11634 5.999981 14.49997 9:51:20 AM -113.138 -147.49 Left
9:51:21 AM 5.814218 11.11827 5.999981 14.49985 9:51:21 AM -115.187 -150.509 Left
9:51:22 AM 5.765913 11.1298 5.999981 14.49996 9:51:22 AM -119.04 -140.767 Left
9:51:23 AM -114.366 -137.759 Left
9:51:24 AM 5.719851 11.14345 5.999981 14.49985 9:51:24 AM -116.066 -139.195 Left
9:51:25 AM 5.674833 11.16323 5.999981 14.4998 9:51:25 AM -118.379 -141.961 Left
9:51:26 AM 5.636488 11.18308 5.999981 14.49997 9:51:26 AM -116.041 -137.193 Left
9:51:27 AM 5.593248 11.21056 5.999981 14.49985
9:51:28 AM 5.555815 11.2404 5.999981 14.49996 9:51:28 AM -116.584 -134.762 Left
9:51:29 AM 5.521909 11.27086 5.999981 14.4998 9:51:29 AM -117.846 -133.757 Left
9:51:30 AM 5.489244 11.31269 5.999981 14.49996 9:51:30 AM -121.019 -132.14 Left
9:51:31 AM 5.464964 11.36073 5.999981 14.49985 9:51:31 AM -125.493 -128.432 Left
9:51:32 AM 5.44524 11.40433 5.999981 14.49996 9:51:32 AM -117.556 -124.887 Left
9:51:33 AM 5.42282 11.45891 5.999981 14.49985 9:51:33 AM -115.972 -125.244 Left
9:51:34 AM -116.677 -127.455 Left
9:51:35 AM 5.39315 11.52384 5.999981 14.49996 9:51:35 AM -122.047 -129.672 Left
9:51:36 AM 5.38341 11.56829 5.999981 14.49997 9:51:36 AM -115.94 -125.697 Left
9:51:37 AM 5.357516 11.61091 5.999981 14.49996 9:51:37 AM -119.221 -130.562 Left
9:51:38 AM 5.34116 11.66051 5.999981 14.49985 9:51:38 AM -123.953 -132.117 Left
9:51:39 AM 5.322598 11.7019 5.999981 14.49996 9:51:39 AM -123.509 -129.53 Left
9:51:40 AM 5.314125 11.74172 5.999981 14.49996
9:51:41 AM 5.304253 11.7815 5.999981 14.49997 9:51:41 AM -121.5 -126.523 Left
9:51:42 AM 5.296102 11.83753 5.999981 14.49996 9:51:42 AM -124.387 -129.128 Left
9:51:43 AM 5.300335 11.88195 5.999981 14.49985 9:51:43 AM -128.3 -129.23 Left
9:51:44 AM 5.299342 11.88287 5.999981 14.49996 9:51:44 AM -124.344 -123.061 Right
9:51:45 AM -124.703 -123.206 Right
9:51:46 AM 5.297827 11.88361 5.999981 14.49985 9:51:46 AM -125.292 -127.356 Left
9:51:47 AM 5.295514 11.88386 5.999981 14.49996 9:51:47 AM -127.382 -123.893 Right
9:51:48 AM 5.29448 11.88433 5.999981 14.49985 9:51:48 AM -127.344 -122.725 Right
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
259
Uniform Obstruction with Left Scatter Sample 5
Motion Path Graph - Uniform obstruction with Left Scatter – Sample 5.
Combined logs - Uniform with Left Scatter - Sample 5.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
29-6-9-52
CARMI Child
X Z X Z L R
9:52:03 AM 6 9.5 6 14.5
9:52:04 AM 6 9.5 6 14.5
9:52:05 AM 6 9.5 6 14.5
9:52:06 AM 5.986923 9.501168 5.999981 14.49996
9:52:07 AM -114.247 -116.774 Left
9:52:08 AM 5.985776 9.500831 5.999981 14.49997
9:52:09 AM 5.986289 9.500549 5.999981 14.49996 9:52:09 AM -126.844 -109.334 Right
9:52:10 AM 5.987936 9.50121 5.999981 14.49985 9:52:10 AM -119.296 -112.33 Right
9:52:11 AM 5.989825 9.501816 5.999981 14.4998 9:52:11 AM -119.035 -111.647 Right
9:52:12 AM 5.991438 9.501507 5.999981 14.49996 9:52:12 AM -119.947 -110.943 Right
9:52:13 AM 5.994099 9.501693 5.999981 14.49997 9:52:13 AM -120.233 -110.404 Right
9:52:14 AM 5.996954 9.501394 5.999981 14.49985 9:52:14 AM -117.913 -109.889 Right
9:52:15 AM 5.992976 9.814143 5.999981 14.49985 9:52:15 AM -116.118 -113.455 Right
9:52:16 AM 5.9884 10.17606 5.999981 14.4998 9:52:16 AM -121.528 -113.426 Right
9:52:17 AM -125.733 -113.562 Right
9:52:18 AM 5.983995 10.52225 5.999981 14.4998 9:52:18 AM -129.233 -111.506 Right
9:52:19 AM 5.979523 10.87368 5.999981 14.49997
9:52:20 AM 5.981913 11.05711 5.999981 14.49985 9:52:20 AM -127.362 -111.066 Right
9:52:21 AM 5.991177 11.059 5.999981 14.49997 9:52:21 AM -118.371 -113.215 Right
9:52:22 AM 5.998302 11.07402 5.999981 14.49996 9:52:22 AM -111.838 -115.564 Left
9:52:23 AM 6.005992 11.08325 5.999981 14.4998 9:52:23 AM -111.101 -110.24 Right
9:52:24 AM 5.992832 11.08154 5.999981 14.49985 9:52:24 AM -106.847 -107.324 Left
9:52:25 AM 5.985376 11.07502 5.999981 14.49997 9:52:25 AM -111.303 -107.679 Right
9:52:26 AM 5.976749 11.07048 5.999981 14.49985 9:52:26 AM -112.173 -107.702 Right
9:52:27 AM -117.917 -111.765 Right
9:52:28 AM 5.978275 11.05606 5.999981 14.49997 9:52:28 AM -122.525 -119.825 Right
9:52:29 AM 5.999763 11.00662 5.999981 14.49985 9:52:29 AM -119.697 -120.466 Left
9:52:30 AM 6.024755 10.9677 5.999981 14.4998 9:52:30 AM -118.504 -121.348 Left
9:52:31 AM 6.032842 10.96397 5.999981 14.49985
9:52:32 AM 6.014199 10.9949 5.999981 14.49996 9:52:32 AM -117.925 -125.723 Left
9:52:33 AM 5.987284 11.02559 5.999981 14.4998 9:52:33 AM -116.407 -126.901 Left
9:52:34 AM 5.953606 11.05565 5.999981 14.49985 9:52:34 AM -115.08 -129 Left
9:52:35 AM 5.898875 11.09244 5.999981 14.4998 9:52:35 AM -115.281 -133.179 Left
9:52:36 AM 5.862541 11.11813 5.999981 14.49997 9:52:36 AM -111.91 -135.706 Left
9:52:37 AM 5.80668 11.13608 5.999981 14.49996 9:52:37 AM -113.123 -140.932 Left
9:52:38 AM 5.75741 11.14399 5.999981 14.49997 9:52:38 AM -115.412 -143.464 Left
9:52:39 AM -115.86 -147.343 Left
9:52:40 AM 5.71046 11.15507 5.999981 14.49996 9:52:40 AM -115.989 -140.025 Left
9:52:41 AM 5.665432 11.17138 5.999981 14.4998 9:52:41 AM -118.114 -140.827 Left
9:52:42 AM 5.616353 11.19842 5.999981 14.49985 9:52:42 AM -121.249 -135.504 Left
9:52:43 AM 5.583124 11.22611 5.999981 14.49997 9:52:43 AM -117.91 -131.563 Left
9:52:44 AM 5.5418 11.26998 5.999981 14.49985
9:52:45 AM 5.508549 11.32217 5.999981 14.49996 9:52:45 AM -122.432 -131.554 Left
9:52:46 AM 5.48676 11.37243 5.999981 14.49985 9:52:46 AM -125.706 -126.489 Left
9:52:47 AM 5.468561 11.41863 5.999981 14.4998 9:52:47 AM -118.398 -124.538 Left
9:52:48 AM 5.454777 11.45083 5.999981 14.49997 9:52:48 AM -116.756 -125.79 Left
9:52:49 AM 5.433987 11.49951 5.999981 14.49985 9:52:49 AM -117.402 -127.23 Left
9:52:50 AM -119.579 -127.874 Left
9:52:51 AM 5.419274 11.54935 5.999981 14.49996 9:52:51 AM -119.162 -129.584 Left
9:52:52 AM 5.392208 11.60023 5.999981 14.4998 9:52:52 AM -118.521 -130.245 Left
9:52:53 AM 5.376558 11.64423 5.999981 14.49997 9:52:53 AM -117.961 -130.065 Left
9:52:54 AM 5.357267 11.68128 5.999981 14.49996 9:52:54 AM -121.425 -131.033 Left
9:52:55 AM 5.341269 11.72774 5.999981 14.49985 9:52:55 AM -125.744 -131.34 Left
9:52:56 AM 5.324807 11.78022 5.999981 14.49997 9:52:56 AM -125.978 -126.602 Left
9:52:57 AM 5.318489 11.83174 5.999981 14.49985
9:52:58 AM 5.312061 11.87229 5.999981 14.49996 9:52:58 AM -123.377 -125.145 Left
9:52:59 AM 5.315662 11.94361 5.999981 14.49985 9:52:59 AM -125.563 -129.555 Left
9:53:00 AM 5.316074 11.95011 5.999981 14.49997 9:53:00 AM -126.156 -127.504 Left
9:53:01 AM -124.699 -119.457 Right
9:53:02 AM 5.316076 11.95011 5.999981 14.49985 9:53:02 AM -122.596 -119.9 Right
9:53:03 AM 5.316077 11.95011 5.999981 14.49996 9:53:03 AM -125.269 -123.511 Right
9:53:04 AM 5.31516 11.95057 5.999981 14.4998 9:53:04 AM -125.74 -124.766 Right
9:53:05 AM 5.314207 11.94992 5.999981 14.49985 9:53:05 AM -126.238 -122.88 Right
9:53:06 AM 5.31402 11.94994 5.999981 14.49996 9:53:06 AM -126.052 -121.72 Right
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
260
X Z X Z L R
9:52:03 AM 6 9.5 6 14.5
9:52:04 AM 6 9.5 6 14.5
9:52:05 AM 6 9.5 6 14.5
9:52:06 AM 5.986923 9.501168 5.999981 14.49996
9:52:07 AM -114.247 -116.774 Left
9:52:08 AM 5.985776 9.500831 5.999981 14.49997
9:52:09 AM 5.986289 9.500549 5.999981 14.49996 9:52:09 AM -126.844 -109.334 Right
9:52:10 AM 5.987936 9.50121 5.999981 14.49985 9:52:10 AM -119.296 -112.33 Right
9:52:11 AM 5.989825 9.501816 5.999981 14.4998 9:52:11 AM -119.035 -111.647 Right
9:52:12 AM 5.991438 9.501507 5.999981 14.49996 9:52:12 AM -119.947 -110.943 Right
9:52:13 AM 5.994099 9.501693 5.999981 14.49997 9:52:13 AM -120.233 -110.404 Right
9:52:14 AM 5.996954 9.501394 5.999981 14.49985 9:52:14 AM -117.913 -109.889 Right
9:52:15 AM 5.992976 9.814143 5.999981 14.49985 9:52:15 AM -116.118 -113.455 Right
9:52:16 AM 5.9884 10.17606 5.999981 14.4998 9:52:16 AM -121.528 -113.426 Right
9:52:17 AM -125.733 -113.562 Right
9:52:18 AM 5.983995 10.52225 5.999981 14.4998 9:52:18 AM -129.233 -111.506 Right
9:52:19 AM 5.979523 10.87368 5.999981 14.49997
9:52:20 AM 5.981913 11.05711 5.999981 14.49985 9:52:20 AM -127.362 -111.066 Right
9:52:21 AM 5.991177 11.059 5.999981 14.49997 9:52:21 AM -118.371 -113.215 Right
9:52:22 AM 5.998302 11.07402 5.999981 14.49996 9:52:22 AM -111.838 -115.564 Left
9:52:23 AM 6.005992 11.08325 5.999981 14.4998 9:52:23 AM -111.101 -110.24 Right
9:52:24 AM 5.992832 11.08154 5.999981 14.49985 9:52:24 AM -106.847 -107.324 Left
9:52:25 AM 5.985376 11.07502 5.999981 14.49997 9:52:25 AM -111.303 -107.679 Right
9:52:26 AM 5.976749 11.07048 5.999981 14.49985 9:52:26 AM -112.173 -107.702 Right
9:52:27 AM -117.917 -111.765 Right
9:52:28 AM 5.978275 11.05606 5.999981 14.49997 9:52:28 AM -122.525 -119.825 Right
9:52:29 AM 5.999763 11.00662 5.999981 14.49985 9:52:29 AM -119.697 -120.466 Left
9:52:30 AM 6.024755 10.9677 5.999981 14.4998 9:52:30 AM -118.504 -121.348 Left
9:52:31 AM 6.032842 10.96397 5.999981 14.49985
9:52:32 AM 6.014199 10.9949 5.999981 14.49996 9:52:32 AM -117.925 -125.723 Left
9:52:33 AM 5.987284 11.02559 5.999981 14.4998 9:52:33 AM -116.407 -126.901 Left
9:52:34 AM 5.953606 11.05565 5.999981 14.49985 9:52:34 AM -115.08 -129 Left
9:52:35 AM 5.898875 11.09244 5.999981 14.4998 9:52:35 AM -115.281 -133.179 Left
9:52:36 AM 5.862541 11.11813 5.999981 14.49997 9:52:36 AM -111.91 -135.706 Left
9:52:37 AM 5.80668 11.13608 5.999981 14.49996 9:52:37 AM -113.123 -140.932 Left
9:52:38 AM 5.75741 11.14399 5.999981 14.49997 9:52:38 AM -115.412 -143.464 Left
9:52:39 AM -115.86 -147.343 Left
9:52:40 AM 5.71046 11.15507 5.999981 14.49996 9:52:40 AM -115.989 -140.025 Left
9:52:41 AM 5.665432 11.17138 5.999981 14.4998 9:52:41 AM -118.114 -140.827 Left
9:52:42 AM 5.616353 11.19842 5.999981 14.49985 9:52:42 AM -121.249 -135.504 Left
9:52:43 AM 5.583124 11.22611 5.999981 14.49997 9:52:43 AM -117.91 -131.563 Left
9:52:44 AM 5.5418 11.26998 5.999981 14.49985
9:52:45 AM 5.508549 11.32217 5.999981 14.49996 9:52:45 AM -122.432 -131.554 Left
9:52:46 AM 5.48676 11.37243 5.999981 14.49985 9:52:46 AM -125.706 -126.489 Left
9:52:47 AM 5.468561 11.41863 5.999981 14.4998 9:52:47 AM -118.398 -124.538 Left
9:52:48 AM 5.454777 11.45083 5.999981 14.49997 9:52:48 AM -116.756 -125.79 Left
9:52:49 AM 5.433987 11.49951 5.999981 14.49985 9:52:49 AM -117.402 -127.23 Left
9:52:50 AM -119.579 -127.874 Left
9:52:51 AM 5.419274 11.54935 5.999981 14.49996 9:52:51 AM -119.162 -129.584 Left
9:52:52 AM 5.392208 11.60023 5.999981 14.4998 9:52:52 AM -118.521 -130.245 Left
9:52:53 AM 5.376558 11.64423 5.999981 14.49997 9:52:53 AM -117.961 -130.065 Left
9:52:54 AM 5.357267 11.68128 5.999981 14.49996 9:52:54 AM -121.425 -131.033 Left
9:52:55 AM 5.341269 11.72774 5.999981 14.49985 9:52:55 AM -125.744 -131.34 Left
9:52:56 AM 5.324807 11.78022 5.999981 14.49997 9:52:56 AM -125.978 -126.602 Left
9:52:57 AM 5.318489 11.83174 5.999981 14.49985
9:52:58 AM 5.312061 11.87229 5.999981 14.49996 9:52:58 AM -123.377 -125.145 Left
9:52:59 AM 5.315662 11.94361 5.999981 14.49985 9:52:59 AM -125.563 -129.555 Left
9:53:00 AM 5.316074 11.95011 5.999981 14.49997 9:53:00 AM -126.156 -127.504 Left
9:53:01 AM -124.699 -119.457 Right
9:53:02 AM 5.316076 11.95011 5.999981 14.49985 9:53:02 AM -122.596 -119.9 Right
9:53:03 AM 5.316077 11.95011 5.999981 14.49996 9:53:03 AM -125.269 -123.511 Right
9:53:04 AM 5.31516 11.95057 5.999981 14.4998 9:53:04 AM -125.74 -124.766 Right
9:53:05 AM 5.314207 11.94992 5.999981 14.49985 9:53:05 AM -126.238 -122.88 Right
9:53:06 AM 5.31402 11.94994 5.999981 14.49996 9:53:06 AM -126.052 -121.72 Right
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
261
Uniform Obstruction with Right Scatter Sample 1
Motion Path Graph - Uniform obstruction with Right Scatter – Sample 1.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
29-6-9-54
CARMI Child
262
Combined logs - Uniform obstruction with Right Scatter - Sample 1.
X Z X Z L R
9:54:20 AM 6 9.5 6 14.5
9:54:21 AM 6 9.5 6 14.5
9:54:22 AM 6 9.5 6 14.5
9:54:24 AM 5.992743 9.501327 5.999981 14.49996
9:54:25 AM 6.014415 9.652831 5.999981 14.49985 9:54:25 AM -109.355 -121.732 Left
9:54:26 AM 6.016137 9.652969 5.999981 14.49997 9:54:26 AM -120.416 -115.859 Right
9:54:27 AM 6.018673 9.652266 5.999981 14.49997 9:54:27 AM -124.603 -112.088 Right
9:54:28 AM 6.022757 9.734281 5.999981 14.49996 9:54:28 AM -114.683 -115.343 Left
9:54:29 AM 6.035034 10.07532 5.999981 14.49997 9:54:29 AM -111.127 -122.824 Left
9:54:30 AM 6.047583 10.42157 5.999981 14.49985 9:54:30 AM -112.634 -126.391 Left
9:54:31 AM 6.050531 10.42703 5.999981 14.49997 9:54:31 AM -112.889 -127.154 Left
9:54:32 AM -110.945 -125.513 Left
9:54:33 AM 6.045438 10.59026 5.999981 14.49985 9:54:33 AM -110.314 -126.542 Left
9:54:34 AM 6.035212 10.93034 5.999981 14.4998 9:54:34 AM -107.805 -122.514 Left
9:54:35 AM 6.029407 11.02936 5.999981 14.49985 9:54:35 AM -108.739 -114.683 Left
9:54:36 AM 6.018277 11.03932 5.999981 14.49997 9:54:36 AM -115.581 -107.849 Right
9:54:37 AM 6.017547 11.05508 5.999981 14.4998
9:54:38 AM 6.012517 11.05829 5.999981 14.49997 9:54:38 AM -126.3 -107.902 Right
9:54:39 AM 6.028029 11.08198 5.999981 14.49996 9:54:39 AM -130.589 -113.127 Right
9:54:40 AM 6.065265 11.11689 5.999981 14.4998 9:54:40 AM -129.694 -108.345 Right
9:54:41 AM 6.111082 11.14374 5.999981 14.4998 9:54:41 AM -135.096 -109.961 Right
9:54:42 AM -142.776 -109.965 Right
9:54:43 AM 6.158471 11.16322 5.999981 14.49997 9:54:43 AM -143.954 -106.512 Right
9:54:44 AM 6.205731 11.16906 5.999981 14.49996 9:54:44 AM -143.341 -107.78 Right
9:54:45 AM 6.242528 11.17795 5.999981 14.49985 9:54:45 AM -139.898 -109.889 Right
9:54:46 AM 6.29443 11.19541 5.999981 14.49996 9:54:46 AM -137.761 -110.354 Right
9:54:47 AM 6.337522 11.21556 5.999981 14.49997 9:54:47 AM -134.745 -110.4 Right
9:54:48 AM 6.385379 11.24562 5.999981 14.49996
9:54:49 AM 6.422552 11.27703 5.999981 14.4998 9:54:49 AM -132.099 -111.216 Right
9:54:50 AM 6.450261 11.30324 5.999981 14.49996 9:54:50 AM -129.517 -112.148 Right
9:54:51 AM 6.484265 11.34326 5.999981 14.49985 9:54:51 AM #NAME? #NAME? Mid
9:54:52 AM 6.518316 11.39919 5.999981 14.49985 9:54:52 AM -125.377 -116.424 Right
9:54:53 AM 6.531538 11.44027 5.999981 14.4998 9:54:53 AM -119.71 -117.881 Right
9:54:54 AM -122.98 -108.096 Right
9:54:55 AM 6.549782 11.48136 5.999981 14.4998 9:54:55 AM -123.539 -114.581 Right
9:54:56 AM 6.573968 11.53471 5.999981 14.4998 9:54:56 AM -121.92 -114.437 Right
9:54:57 AM 6.586883 11.57396 5.999981 14.49996 9:54:57 AM -122.01 -115.686 Right
9:54:58 AM 6.599334 11.61252 5.999981 14.49985 9:54:58 AM -124.641 -112.713 Right
9:54:59 AM 6.614729 11.64415 5.999981 14.4998 9:54:59 AM -123.006 -113.922 Right
9:55:00 AM 6.635639 11.69306 5.999981 14.49996 9:55:00 AM -122.358 -116.383 Right
9:55:01 AM 6.658867 11.74774 5.999981 14.4998
9:55:02 AM 6.669861 11.78621 5.999981 14.49985 9:55:02 AM -120.054 -118.531 Right
9:55:03 AM 6.673469 11.83668 5.999981 14.4998 9:55:03 AM -116.947 -119.705 Left
9:55:04 AM 6.6792 11.87639 5.999981 14.49996 9:55:04 AM -113.345 -118.26 Left
9:55:05 AM -116.976 -117.922 Left
9:55:06 AM 6.680484 11.92096 5.999981 14.49997 9:55:06 AM -115.768 -123.232 Left
9:55:07 AM 6.674488 11.96908 5.999981 14.4998 9:55:07 AM -110.095 -123.105 Left
9:55:08 AM 6.673965 11.96892 5.999981 14.49985 9:55:08 AM -110.483 -120.978 Left
9:55:09 AM 6.67272 11.96874 5.999981 14.49996 9:55:09 AM -107.896 -122.012 Left
9:55:10 AM 6.672424 11.9693 5.999981 14.4998 9:55:10 AM -110.864 -121.478 Left
9:55:11 AM 6.672506 11.96932 5.999981 14.49996 9:55:11 AM -110.537 -121.547 Left
9:55:12 AM 6.672506 11.96932 5.999981 14.49985 9:55:12 AM -108.211 -122.777 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
263
Uniform Obstruction with Right Scatter Sample 2
Motion Path Graph - Uniform obstruction with Right Scatter – Sample 2.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
29-6-9-55
CARMI Child
264
Combined logs - Uniform obstruction with Right Scatter - Sample 2.
X Z X Z L R
9:55:25 AM 6 9.5 6 14.5
9:55:26 AM 6 9.5 6 14.5
9:55:27 AM 6 9.5 6 14.5
9:55:29 AM 5.997623 9.500815 5.999981 14.49985
9:55:30 AM 5.988283 9.501217 5.999981 14.49997 9:55:30 AM -111.921 -120.332 Left
9:55:31 AM 5.99008 9.501486 5.999981 14.4998 9:55:31 AM -129.2 -110.807 Right
9:55:32 AM 5.990736 9.50109 5.999981 14.49997 9:55:32 AM -128.366 -109.722 Right
9:55:33 AM 5.992559 9.507976 5.999981 14.49985 9:55:33 AM -107.885 -120.328 Left
9:55:34 AM 6.015729 9.66961 5.999981 14.49985 9:55:34 AM -109.284 -121.959 Left
9:55:36 AM 6.02519 9.704388 5.999981 14.49996 9:55:36 AM -123.774 -113.495 Right
9:55:37 AM 6.026832 9.70393 5.999981 14.4998 9:55:37 AM -120.867 -113.435 Right
9:55:38 AM 6.038556 9.898973 5.999981 14.49996 9:55:38 AM -109.734 -123.514 Left
9:55:39 AM 6.059045 10.24453 5.999981 14.4998 9:55:39 AM -111.636 -127.029 Left
9:55:40 AM 6.064431 10.27414 5.999981 14.49985 9:55:40 AM -114.657 -124.947 Left
9:55:41 AM 6.065457 10.27537 5.999981 14.49997 9:55:41 AM -114.294 -124.039 Left
9:55:42 AM 6.057249 10.58068 5.999981 14.4998 9:55:42 AM -110.138 -129.492 Left
9:55:43 AM 6.047837 10.93047 5.999981 14.49996 9:55:43 AM -109.817 -125.938 Left
9:55:44 AM -108.363 -119.862 Left
9:55:45 AM 6.036555 11.04493 5.999981 14.49985 9:55:45 AM -109.689 -108.021 Right
9:55:46 AM 6.029049 11.0474 5.999981 14.49997 9:55:46 AM -118.704 -107.957 Right
9:55:47 AM 6.024122 11.06406 5.999981 14.49997
9:55:48 AM 6.018104 11.06892 5.999981 14.49997 9:55:48 AM -125.172 -110.427 Right
9:55:49 AM 6.039644 11.10099 5.999981 14.49996 9:55:49 AM -128.314 -108.153 Right
9:55:50 AM 6.081065 11.13148 5.999981 14.4998 9:55:50 AM -132.476 -109.466 Right
9:55:51 AM 6.128121 11.15453 5.999981 14.49997 9:55:51 AM -139.565 -109.474 Right
9:55:52 AM 6.170222 11.17085 5.999981 14.49996 9:55:52 AM -141.764 -110.459 Right
9:55:53 AM 6.215927 11.17769 5.999981 14.49985 9:55:53 AM -144.497 -106.811 Right
9:55:54 AM -143.525 -109.554 Right
9:55:55 AM 6.274438 11.18949 5.999981 14.49996 9:55:55 AM -138.297 -109.77 Right
9:55:56 AM 6.323219 11.20669 5.999981 14.49997 9:55:56 AM -136.206 -110.422 Right
9:55:57 AM 6.378795 11.23668 5.999981 14.4998 9:55:57 AM -133.81 -112.324 Right
9:55:58 AM 6.424065 11.27384 5.999981 14.49985 9:55:58 AM -130.528 -110.918 Right
9:55:59 AM 6.463225 11.30717 5.999981 14.4998
9:56:00 AM 6.509186 11.35582 5.999981 14.49997 9:56:00 AM -129.205 -112.825 Right
9:56:01 AM 6.537572 11.39879 5.999981 14.49997 9:56:01 AM -124.932 -117.751 Right
9:56:02 AM 6.557451 11.44825 5.999981 14.49996 9:56:02 AM -120.974 -115.47 Right
9:56:03 AM 6.577597 11.48911 5.999981 14.4998 9:56:03 AM -123.077 -109.536 Right
9:56:04 AM 6.590925 11.52767 5.999981 14.49996 9:56:04 AM -123.449 -115.81 Right
9:56:05 AM -122.369 -114.732 Right
9:56:06 AM 6.608106 11.56886 5.999981 14.49997 9:56:06 AM -121.47 -116.422 Right
9:56:07 AM 6.623736 11.61112 5.999981 14.4998 9:56:07 AM -126.571 -110.798 Right
9:56:08 AM 6.65342 11.67208 5.999981 14.49996 9:56:08 AM -122.908 -115.925 Right
9:56:09 AM 6.682631 11.74033 5.999981 14.49997 9:56:09 AM -121.75 -117.973 Right
9:56:10 AM 6.695972 11.78157 5.999981 14.49985 9:56:10 AM -117.141 -118.419 Left
9:56:11 AM 6.704181 11.83208 5.999981 14.49997 9:56:11 AM -115.505 -119.768 Left
9:56:12 AM 6.710687 11.87988 5.999981 14.4998
9:56:13 AM 6.706898 11.93766 5.999981 14.49996 9:56:13 AM -115.857 -120.153 Left
9:56:14 AM 6.707311 11.93733 5.999981 14.49985 9:56:14 AM -111.201 -121.07 Left
9:56:15 AM 6.707311 11.93733 5.999981 14.49985 9:56:15 AM -108.14 -121.309 Left
9:56:16 AM -108.108 -121.252 Left
9:56:17 AM 6.707311 11.93733 5.999981 14.49997 9:56:17 AM -108.109 -121.257 Left
9:56:18 AM 6.707311 11.93733 5.999981 14.49996 9:56:18 AM -108.11 -121.256 Left
9:56:19 AM 6.707311 11.93733 5.999981 14.49985 9:56:19 AM -108.111 -121.274 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
265
Uniform Obstruction with Right Scatter Sample 3
Motion Path Graph - Uniform obstruction with Right Scatter – Sample 3.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
29-6-9-56
CARMI Child
266
Combined logs - Uniform obstruction with Right Scatter - Sample 3.
X Z X Z L R
9:56:33 AM 6 9.5 6 14.5
9:56:34 AM 6 9.5 6 14.5
9:56:35 AM 6 9.5 6 14.5
9:56:36 AM 5.995082 9.501253 5.999981 14.49985
9:56:37 AM -108.99 -121.201 Left
9:56:38 AM 6.006018 9.59443 5.999981 14.49996
9:56:39 AM 6.018891 9.647563 5.999981 14.4998 9:56:39 AM -118.808 -115.094 Right
9:56:40 AM 6.020895 9.646997 5.999981 14.49997 9:56:40 AM -119.743 -113.432 Right
9:56:41 AM 6.022592 9.687309 5.999981 14.49996 9:56:41 AM -110.94 -117.35 Left
9:56:42 AM 6.024922 10.03388 5.999981 14.49997 9:56:42 AM -113.492 -121.404 Left
9:56:43 AM 6.027252 10.38044 5.999981 14.49985 9:56:43 AM -113.169 -125.448 Left
9:56:44 AM 6.029546 10.72174 5.999981 14.4998 9:56:44 AM -111.101 -128.459 Left
9:56:45 AM 6.031813 11.05793 5.999981 14.4998 9:56:45 AM -108.403 -125.918 Left
9:56:46 AM 6.026261 11.06274 5.999981 14.49996 9:56:46 AM -109.4 -118.314 Left
9:56:47 AM 6.017118 11.06535 5.999981 14.49997 9:56:47 AM -114.102 -109.689 Right
9:56:48 AM -120.613 -113.018 Right
9:56:49 AM 6.008781 11.06935 5.999981 14.49985
9:56:50 AM 6.002533 11.07401 5.999981 14.49997 9:56:50 AM -122.447 -109.911 Right
9:56:51 AM 5.99748 11.07964 5.999981 14.4998 9:56:51 AM -125.98 -108.241 Right
9:56:52 AM 6.026755 11.1105 5.999981 14.49985 9:56:52 AM -133.218 -108.974 Right
9:56:53 AM 6.069405 11.14307 5.999981 14.49985 9:56:53 AM -134.032 -109.163 Right
9:56:54 AM 6.106963 11.16498 5.999981 14.49996 9:56:54 AM -136.311 -110.887 Right
9:56:55 AM 6.144246 11.1833 5.999981 14.49997 9:56:55 AM -141.633 -111.348 Right
9:56:56 AM 6.183669 11.19402 5.999981 14.49996 9:56:56 AM -144.741 -106.667 Right
9:56:57 AM 6.221784 11.196 5.999981 14.4998 9:56:57 AM -144.266 -108.355 Right
9:56:58 AM 6.275648 11.21893 5.999981 14.49985
9:56:59 AM -138.604 -110.43 Right
9:57:00 AM 6.32091 11.23946 5.999981 14.49997 9:57:00 AM -136.629 -108.489 Right
9:57:01 AM 6.374705 11.26873 5.999981 14.49985 9:57:01 AM -133.432 -111.818 Right
9:57:02 AM 6.409009 11.29554 5.999981 14.49997 9:57:02 AM -129.942 -111.85 Right
9:57:03 AM 6.4429 11.32918 5.999981 14.49985 9:57:03 AM -127.518 -112.007 Right
9:57:04 AM 6.468696 11.35887 5.999981 14.49985 9:57:04 AM -124.39 -113.133 Right
9:57:05 AM 6.503281 11.42072 5.999981 14.49996 9:57:05 AM -122.957 -120.454 Right
9:57:06 AM 6.518909 11.46089 5.999981 14.4998 9:57:06 AM -121.716 -113.31 Right
9:57:07 AM 6.535764 11.50683 5.999981 14.49985 9:57:07 AM -123.608 -113.982 Right
9:57:08 AM 6.554189 11.55095 5.999981 14.49996 9:57:08 AM -123.459 -113.277 Right
9:57:09 AM 6.566879 11.58492 5.999981 14.49996 9:57:09 AM -123.731 -108.69 Right
9:57:10 AM -124.006 -113.585 Right
9:57:11 AM 6.591447 11.63612 5.999981 14.49997
9:57:12 AM 6.607576 11.68182 5.999981 14.49997 9:57:12 AM -122.801 -114.706 Right
9:57:13 AM 6.629003 11.73351 5.999981 14.49997 9:57:13 AM -122.17 -117.626 Right
9:57:14 AM 6.638561 11.77993 5.999981 14.49996 9:57:14 AM -119.621 -118.539 Right
9:57:15 AM 6.650188 11.84339 5.999981 14.49985 9:57:15 AM -118.487 -120.691 Left
9:57:16 AM 6.659105 11.90425 5.999981 14.49996 9:57:16 AM -115.089 -120.502 Left
9:57:17 AM 6.658787 11.94016 5.999981 14.49985 9:57:17 AM -115.377 -120.652 Left
9:57:18 AM 6.659345 11.94735 5.999981 14.49996 9:57:18 AM -110.968 -121.346 Left
9:57:19 AM -107.701 -122.393 Left
9:57:20 AM 6.659346 11.94735 5.999981 14.49996 9:57:20 AM -108.151 -121.729 Left
9:57:21 AM 6.659349 11.94735 5.999981 14.49997 9:57:21 AM -109.076 -121.405 Left
9:57:22 AM 6.65935 11.94735 5.999981 14.49985 9:57:22 AM -109.107 -121.409 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
267
Uniform Obstruction with Right Scatter Sample 4
Motion Path Graph - Uniform obstruction with Right Scatter – Sample 4.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
29-6-9-57
CARMI Child
268
Combined logs - Uniform obstruction with Right Scatter - Sample 4.
X Z X Z L R
9:57:32 AM 6 9.5 6 14.5
9:57:33 AM 6 9.5 6 14.5
9:57:34 AM 6 9.5 6 14.5
9:57:35 AM 5.994548 9.500986 5.999981 14.49996
9:57:36 AM -109.011 -120.832 Left
9:57:37 AM 6.006451 9.595366 5.999981 14.49985 9:57:37 AM -118.064 -115.032 Right
9:57:38 AM 6.011172 9.61203 5.999981 14.4998
9:57:39 AM 6.012843 9.612447 5.999981 14.49996 9:57:39 AM -115.645 -115.769 Left
9:57:40 AM 6.014937 9.613797 5.999981 14.4998 9:57:40 AM -112.795 -117.482 Left
9:57:41 AM 6.017207 9.614681 5.999981 14.49997 9:57:41 AM -114.784 -115.365 Left
9:57:42 AM 6.009337 9.84693 5.999981 14.49996 9:57:42 AM -112.888 -118.412 Left
9:57:43 AM 5.996538 10.1879 5.999981 14.49985 9:57:43 AM -112.895 -123.559 Left
9:57:44 AM 5.983344 10.53899 5.999981 14.49996 9:57:44 AM -112.299 -127.131 Left
9:57:45 AM 5.97054 10.87854 5.999981 14.49985 9:57:45 AM -109.298 -128.267 Left
9:57:46 AM 5.960775 11.06625 5.999981 14.49996 9:57:46 AM -106.639 -126.349 Left
9:57:47 AM 5.952388 11.0663 5.999981 14.4998 9:57:47 AM -108.208 -115.141 Left
9:57:48 AM -114.495 -112.658 Right
9:57:49 AM 5.943923 11.06854 5.999981 14.49997
9:57:50 AM 5.943664 11.07973 5.999981 14.4998 9:57:50 AM -119.886 -113.928 Right
9:57:51 AM 5.936495 11.08825 5.999981 14.49997 9:57:51 AM -123.583 -108.658 Right
9:57:52 AM 5.931809 11.09559 5.999981 14.49996 9:57:52 AM -131.65 -108.474 Right
9:57:53 AM 5.969792 11.12473 5.999981 14.49985 9:57:53 AM -134.739 -108.887 Right
9:57:54 AM 6.010285 11.14679 5.999981 14.4998 9:57:54 AM -137.277 -110.01 Right
9:57:55 AM 6.047515 11.16355 5.999981 14.49996 9:57:55 AM -141.899 -110.14 Right
9:57:56 AM 6.113077 11.17416 5.999981 14.4998 9:57:56 AM -143.16 -110.918 Right
9:57:57 AM 6.162657 11.1731 5.999981 14.49996 9:57:57 AM -148.983 -106.421 Right
9:57:58 AM -146.597 -109.746 Right
9:57:59 AM 6.198093 11.17299 5.999981 14.49997 9:57:59 AM -142.169 -109.001 Right
9:58:00 AM 6.246221 11.182 5.999981 14.49985
9:58:01 AM 6.287482 11.19607 5.999981 14.49996 9:58:01 AM -139.258 -108.498 Right
9:58:02 AM 6.330459 11.21676 5.999981 14.49997 9:58:02 AM -138.008 -110.519 Right
9:58:03 AM 6.363225 11.23319 5.999981 14.49985 9:58:03 AM -135.52 -110.245 Right
9:58:04 AM 6.399527 11.26743 5.999981 14.4998 9:58:04 AM -131.53 -113.533 Right
9:58:05 AM 6.439667 11.31122 5.999981 14.4998 9:58:05 AM -128.758 -111.366 Right
9:58:06 AM 6.470792 11.35169 5.999981 14.49997 9:58:06 AM -126.635 -114.149 Right
9:58:07 AM 6.501385 11.40107 5.999981 14.49997 9:58:07 AM -122.3 -114.156 Right
9:58:08 AM 6.515336 11.43874 5.999981 14.49996 9:58:08 AM -122.708 -114.139 Right
9:58:09 AM -123.045 -116.371 Right
9:58:10 AM 6.532215 11.47868 5.999981 14.4998 9:58:10 AM -121.162 -119.805 Right
9:58:11 AM 6.55264 11.53074 5.999981 14.49985 9:58:11 AM -123.572 -113.19 Right
9:58:12 AM 6.570231 11.58547 5.999981 14.49996
9:58:13 AM 6.587126 11.63303 5.999981 14.49985 9:58:13 AM -122.133 -117.49 Right
9:58:14 AM 6.599205 11.66648 5.999981 14.49996 9:58:14 AM -121.686 -114.865 Right
9:58:15 AM 6.617399 11.70682 5.999981 14.49985 9:58:15 AM -125.86 -110.926 Right
9:58:16 AM 6.636156 11.74861 5.999981 14.49997 9:58:16 AM -122.107 -117.28 Right
9:58:17 AM 6.6454 11.78609 5.999981 14.49985 9:58:17 AM -119.438 -119.395 Right
9:58:18 AM 6.661684 11.85751 5.999981 14.49997 9:58:18 AM -115.43 -118.168 Left
9:58:19 AM -116.605 -122.597 Left
9:58:20 AM 6.65705 11.90391 5.999981 14.49997 9:58:20 AM -107.669 -123.22 Left
9:58:21 AM 6.658482 11.92179 5.999981 14.4998 9:58:21 AM -109.358 -121.231 Left
9:58:22 AM 6.660076 11.92215 5.999981 14.49997 9:58:22 AM -110.572 -120.749 Left
9:58:23 AM 6.661672 11.92333 5.999981 14.49985 9:58:23 AM -110.661 -123.473 Left
9:58:24 AM 6.663239 11.92305 5.999981 14.4998
9:58:25 AM 6.664297 11.92356 5.999981 14.4998 9:58:25 AM -111.16 -123.086 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
269
Uniform Obstruction with Right Scatter Sample 5
Motion Path Graph - Uniform obstruction with Right Scatter – Sample 5.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
29-6-9-58
CARMI Child
270
Combined logs - Uniform obstruction with Right Scatter – Sample 5.
X Z X Z L R
9:58:45 AM 6 9.5 6 14.5
9:58:46 AM 6 9.5 6 14.5
9:58:47 AM 6 9.5 6 14.5
9:58:48 AM 6 9.5 6 14.5
9:58:50 AM 5.993189 9.501262 5.999981 14.49997
9:58:51 AM 6.008582 9.61451 5.999981 14.49996 9:58:51 AM -110.171 -121.082 Left
9:58:52 AM 6.010566 9.615026 5.999981 14.49985 9:58:52 AM -116.409 -116.397 Right
9:58:53 AM 6.012881 9.615509 5.999981 14.49996 9:58:53 AM -115.724 -115.42 Right
9:58:54 AM 6.015862 9.718835 5.999981 14.49996 9:58:54 AM -112.066 -116.652 Left
9:58:55 AM 6.019601 10.07033 5.999981 14.49997 9:58:55 AM -114.08 -120.006 Left
9:58:56 AM 6.02334 10.42181 5.999981 14.4998 9:58:56 AM -114.112 -124.132 Left
9:58:57 AM 6.026967 10.7628 5.999981 14.49997 9:58:57 AM -112.058 -126.733 Left
9:58:58 AM 6.025583 11.06142 5.999981 14.49997 9:58:58 AM -109.408 -123.925 Left
9:58:59 AM 6.012806 11.06401 5.999981 14.49997 9:58:59 AM -109.165 -112.242 Left
9:59:00 AM -120.305 -108.132 Right
9:59:01 AM 6.00105 11.06982 5.999981 14.49997 9:59:01 AM -128.477 -110.022 Right
9:59:02 AM 5.994045 11.07644 5.999981 14.49997
9:59:03 AM 6.012623 11.09851 5.999981 14.49985 9:59:03 AM -130.794 -108.339 Right
9:59:04 AM 6.043941 11.12405 5.999981 14.49996 9:59:04 AM -133.159 -108.999 Right
9:59:05 AM 6.10221 11.1601 5.999981 14.49996 9:59:05 AM -136.71 -109.699 Right
9:59:06 AM 6.140649 11.17582 5.999981 14.4998 9:59:06 AM -140.868 -111.454 Right
9:59:07 AM 6.173244 11.18021 5.999981 14.49985 9:59:07 AM -145.563 -106.88 Right
9:59:08 AM 6.207083 11.18391 5.999981 14.49997 9:59:08 AM -145.895 -110.107 Right
9:59:09 AM 6.255499 11.19372 5.999981 14.49996 9:59:09 AM -139.945 -109.159 Right
9:59:10 AM -139.337 -108.066 Right
9:59:11 AM 6.298922 11.2069 5.999981 14.49996 9:59:11 AM -136.126 -111.213 Right
9:59:12 AM 6.352117 11.23507 5.999981 14.49985 9:59:12 AM -132.905 -111.341 Right
9:59:13 AM 6.388076 11.25852 5.999981 14.49996 9:59:13 AM -130.994 -111.339 Right
9:59:14 AM 6.436852 11.30429 5.999981 14.4998 9:59:14 AM -128.745 -113.657 Right
9:59:15 AM 6.474067 11.34313 5.999981 14.49997
9:59:16 AM 6.505203 11.38421 5.999981 14.49996 9:59:16 AM -124.746 -113.107 Right
9:59:17 AM 6.532163 11.43241 5.999981 14.49985 9:59:17 AM -122.59 -113.534 Right
9:59:18 AM 6.545402 11.47203 5.999981 14.49996 9:59:18 AM -122.432 -115.954 Right
9:59:19 AM 6.561539 11.50831 5.999981 14.4998 9:59:19 AM -123.261 -109.104 Right
9:59:20 AM 6.587187 11.5717 5.999981 14.49996 9:59:20 AM -122.661 -115.484 Right
9:59:21 AM -121.97 -115.392 Right
9:59:22 AM 6.610212 11.62936 5.999981 14.4998 9:59:22 AM -121.599 -115.747 Right
9:59:23 AM 6.628486 11.67712 5.999981 14.49985 9:59:23 AM -121.599 -115.948 Right
9:59:24 AM 6.641671 11.71919 5.999981 14.4998 9:59:24 AM -121.04 -116.998 Right
9:59:25 AM 6.65768 11.77047 5.999981 14.49985 9:59:25 AM -119.608 -117.796 Right
9:59:26 AM 6.670365 11.82412 5.999981 14.49996 9:59:26 AM -117.651 -118.717 Left
9:59:27 AM 6.676343 11.86964 5.999981 14.49985 9:59:27 AM -115.078 -119.698 Left
9:59:28 AM 6.677368 11.91808 5.999981 14.49997 9:59:28 AM -114.894 -122.213 Left
9:59:29 AM 6.67787 11.93672 5.999981 14.49996 9:59:29 AM -110.996 -121.218 Left
9:59:30 AM 6.678367 11.93714 5.999981 14.49997
9:59:31 AM -109.572 -120.981 Left
9:59:32 AM 6.678356 11.93713 5.999981 14.49997 9:59:32 AM -109.391 -121.091 Left
9:59:33 AM 6.678356 11.93713 5.999981 14.49996 9:59:33 AM -109.391 -121.107 Left
9:59:34 AM 6.678356 11.93714 5.999981 14.4998 9:59:34 AM -109.392 -121.097 Left
9:59:35 AM 6.678356 11.93713 5.999981 14.49996 9:59:35 AM -109.391 -121.112 Left
9:59:36 AM 6.678357 11.93713 5.999981 14.4998 9:59:36 AM -109.391 -121.095 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
271
Single Non-Uniform Obstruction Sample 1
Motion Path Graph - Single Non-Uniform obstruction – Sample 1.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.25.45.65.866.26.46.66.87
29-6-10-13
CARMI Child
272
Combined logs – Single Non-Uniform obstruction – Sample 1.
X Z X Z L R
10:13:45 AM 6 9.5 6 14.5
10:13:46 AM 6 9.5 6 14.5
10:13:47 AM 6 9.5 6 14.5
10:13:48 AM 5.991876 9.501328 5.999981 14.49997
10:13:49 AM 5.986232 9.492682 5.999981 14.49996
10:13:51 AM 5.963475 9.437613 5.999981 14.4998
10:13:52 AM 5.934978 9.393324 5.999981 14.49985
10:13:53 AM 5.910014 9.365203 5.999981 14.49996
10:13:54 AM 5.873061 9.333778 5.999981 14.4998
10:13:55 AM 5.839613 9.307987 5.999981 14.4998
10:13:56 AM 5.879478 9.32711 5.999981 14.49996 10:13:56 AM -143.018 -116.469 Right
10:13:57 AM 5.919246 9.351189 5.999981 14.4998 10:13:57 AM -142.429 -109.693 Right
10:13:58 AM 5.958128 9.391424 5.999981 14.49997 10:13:58 AM -140.122 -114.848 Right
10:13:59 AM 5.992304 9.430294 5.999981 14.49985 10:13:59 AM -138.026 -113.818 Right
10:14:00 AM 6.016098 9.468188 5.999981 14.49996 10:14:00 AM -135.606 -112.005 Right
10:14:01 AM 6.042299 9.515686 5.999981 14.4998 10:14:01 AM -135.022 -112.19 Right
10:14:02 AM -134.784 -113.094 Right
10:14:03 AM 6.059335 9.562017 5.999981 14.49997 10:14:03 AM -131.914 -111.386 Right
10:14:04 AM 6.069165 9.594148 5.999981 14.49985 10:14:04 AM -130.484 -113.099 Right
10:14:05 AM 6.070604 9.603067 5.999981 14.49996
10:14:06 AM 6.078282 9.952428 5.999981 14.49996 10:14:06 AM -132.67 -113.611 Right
10:14:07 AM 6.085866 10.29835 5.999981 14.49985 10:14:07 AM -131.297 -115.268 Right
10:14:08 AM 6.093336 10.63891 5.999981 14.49997 10:14:08 AM -125.151 -115.122 Right
10:14:09 AM 6.10093 10.98456 5.999981 14.49985 10:14:09 AM -114.693 -111.836 Right
10:14:10 AM 6.128466 11.00007 5.999981 14.49997 10:14:10 AM -111.018 -108.294 Right
10:14:11 AM 6.137124 11.0077 5.999981 14.49996 10:14:11 AM -109.46 -110.532 Left
10:14:12 AM -110.378 -107.551 Right
10:14:13 AM 6.143859 11.01498 5.999981 14.49985 10:14:13 AM -106.25 -107.319 Left
10:14:14 AM 6.145187 11.01672 5.999981 14.49985 10:14:14 AM -106.427 -107.547 Left
10:14:15 AM 6.140853 11.00891 5.999981 14.49997 10:14:15 AM -110.11 -107.514 Right
10:14:16 AM 6.134578 11.00257 5.999981 14.49985 10:14:16 AM -111.484 -107.63 Right
10:14:17 AM 6.126868 10.99637 5.999981 14.49985 10:14:17 AM -109.464 -108.303 Right
10:14:18 AM 6.142395 10.95863 5.999981 14.49997 10:14:18 AM -109.082 -120.846 Left
10:14:19 AM 6.148995 10.95172 5.999981 14.49997 10:14:19 AM -107.394 -121.711 Left
10:14:20 AM 6.14954 10.96553 5.999981 14.4998
10:14:21 AM 6.120224 11.00731 5.999981 14.49997 10:14:21 AM -110.192 -121.456 Left
10:14:22 AM 6.11064 11.02822 5.999981 14.49985 10:14:22 AM -110.177 -127.288 Left
10:14:23 AM 6.07086 11.05933 5.999981 14.49997 10:14:23 AM #NAME? #NAME? Mid
10:14:24 AM -110.714 -132.566 Left
10:14:25 AM 6.027144 11.08917 5.999981 14.49996 10:14:25 AM -111.257 -136.161 Left
10:14:26 AM 5.976007 11.11179 5.999981 14.4998 10:14:26 AM -112.081 -140.23 Left
10:14:27 AM 5.94027 11.12349 5.999981 14.4998 10:14:27 AM -112.447 -142.929 Left
10:14:28 AM 5.896355 11.13249 5.999981 14.49997 10:14:28 AM -110.031 -144.912 Left
10:14:29 AM 5.856781 11.12952 5.999981 14.49996 10:14:29 AM -110.321 -146.758 Left
10:14:30 AM 5.80122 11.13281 5.999981 14.4998 10:14:30 AM -106.377 -149.661 Left
10:14:31 AM 5.749688 11.14359 5.999981 14.49997 10:14:31 AM -107.998 -142.594 Left
10:14:32 AM 5.702169 11.15886 5.999981 14.49985 10:14:32 AM #NAME? #NAME? Mid
10:14:33 AM 5.642862 11.19348 5.999981 14.49996 10:14:33 AM -107.457 -138.866 Left
10:14:34 AM 5.609009 11.21532 5.999981 14.49996 10:14:34 AM -106.3 -137.481 Left
10:14:35 AM 5.56469 11.24916 5.999981 14.49997 10:14:35 AM -106.688 -135.484 Left
10:14:36 AM -107.583 -133.49 Left
10:14:37 AM 5.529091 11.28092 5.999981 14.49996 10:14:37 AM -108.618 -132.668 Left
10:14:38 AM 5.495217 11.3248 5.999981 14.49985 10:14:38 AM #NAME? #NAME? Mid
10:14:39 AM 5.475418 11.36049 5.999981 14.49997
10:14:40 AM 5.461507 11.40364 5.999981 14.49985 10:14:40 AM -109.518 -124.934 Left
10:14:41 AM 5.440051 11.45829 5.999981 14.49997 10:14:41 AM -109.664 -126.322 Left
10:14:42 AM 5.421883 11.50379 5.999981 14.49996 10:14:42 AM -108.333 -127.681 Left
10:14:43 AM 5.401868 11.55611 5.999981 14.49997 10:14:43 AM -108.116 -128.118 Left
10:14:44 AM 5.380318 11.60897 5.999981 14.49985 10:14:44 AM -109.132 -130.318 Left
10:14:45 AM 5.364387 11.65057 5.999981 14.4998 10:14:45 AM -109.022 -130.6 Left
10:14:46 AM -110.706 -132.62 Left
10:14:47 AM 5.341948 11.69557 5.999981 14.49996 10:14:47 AM -109.805 -129.706 Left
10:14:48 AM 5.329875 11.75236 5.999981 14.49996 10:14:48 AM -110.187 -131.28 Left
10:14:49 AM 5.314241 11.80533 5.999981 14.49985 10:14:49 AM -108.399 -129.789 Left
10:14:50 AM 5.305806 11.85946 5.999981 14.4998
10:14:51 AM 5.306743 11.89437 5.999981 14.49996 10:14:51 AM -112.841 -119.404 Left
10:14:52 AM 5.305887 11.89471 5.999981 14.4998 10:14:52 AM -111.333 -121.329 Left
10:14:53 AM 5.30481 11.89482 5.999981 14.49996 10:14:53 AM -112.74 -120.444 Left
10:14:54 AM 5.304423 11.89456 5.999981 14.49996 10:14:54 AM -111.456 -123.375 Left
10:14:55 AM 5.30336 11.895 5.999981 14.49985 10:14:55 AM -111.91 -123.153 Left
10:14:56 AM 5.30139 11.89341 5.999981 14.49985 10:14:56 AM -111.031 -123.401 Left
10:14:57 AM -110.793 -123.839 Left
10:14:58 AM 5.300543 11.89305 5.999981 14.49985 10:14:58 AM -111.952 -121.689 Left
10:14:59 AM 5.300544 11.89305 5.999981 14.49997 10:14:59 AM -113.44 -120.002 Left
10:15:00 AM 5.300546 11.89305 5.999981 14.49996 10:15:00 AM -113.355 -119.982 Left
10:15:01 AM 5.300547 11.89305 5.999981 14.49997 10:15:01 AM -113.349 -119.971 Left
10:15:02 AM 5.300554 11.89305 5.999981 14.49985 10:15:02 AM -113.457 -119.98 Left
10:15:03 AM 5.300559 11.89304 5.999981 14.49996 10:15:03 AM -113.344 -119.975 Left
10:15:04 AM 5.30056 11.89304 5.999981 14.49996 10:15:04 AM -113.351 -119.983 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
273
X Z X Z L R
10:13:45 AM 6 9.5 6 14.5
10:13:46 AM 6 9.5 6 14.5
10:13:47 AM 6 9.5 6 14.5
10:13:48 AM 5.991876 9.501328 5.999981 14.49997
10:13:49 AM 5.986232 9.492682 5.999981 14.49996
10:13:51 AM 5.963475 9.437613 5.999981 14.4998
10:13:52 AM 5.934978 9.393324 5.999981 14.49985
10:13:53 AM 5.910014 9.365203 5.999981 14.49996
10:13:54 AM 5.873061 9.333778 5.999981 14.4998
10:13:55 AM 5.839613 9.307987 5.999981 14.4998
10:13:56 AM 5.879478 9.32711 5.999981 14.49996 10:13:56 AM -143.018 -116.469 Right
10:13:57 AM 5.919246 9.351189 5.999981 14.4998 10:13:57 AM -142.429 -109.693 Right
10:13:58 AM 5.958128 9.391424 5.999981 14.49997 10:13:58 AM -140.122 -114.848 Right
10:13:59 AM 5.992304 9.430294 5.999981 14.49985 10:13:59 AM -138.026 -113.818 Right
10:14:00 AM 6.016098 9.468188 5.999981 14.49996 10:14:00 AM -135.606 -112.005 Right
10:14:01 AM 6.042299 9.515686 5.999981 14.4998 10:14:01 AM -135.022 -112.19 Right
10:14:02 AM -134.784 -113.094 Right
10:14:03 AM 6.059335 9.562017 5.999981 14.49997 10:14:03 AM -131.914 -111.386 Right
10:14:04 AM 6.069165 9.594148 5.999981 14.49985 10:14:04 AM -130.484 -113.099 Right
10:14:05 AM 6.070604 9.603067 5.999981 14.49996
10:14:06 AM 6.078282 9.952428 5.999981 14.49996 10:14:06 AM -132.67 -113.611 Right
10:14:07 AM 6.085866 10.29835 5.999981 14.49985 10:14:07 AM -131.297 -115.268 Right
10:14:08 AM 6.093336 10.63891 5.999981 14.49997 10:14:08 AM -125.151 -115.122 Right
10:14:09 AM 6.10093 10.98456 5.999981 14.49985 10:14:09 AM -114.693 -111.836 Right
10:14:10 AM 6.128466 11.00007 5.999981 14.49997 10:14:10 AM -111.018 -108.294 Right
10:14:11 AM 6.137124 11.0077 5.999981 14.49996 10:14:11 AM -109.46 -110.532 Left
10:14:12 AM -110.378 -107.551 Right
10:14:13 AM 6.143859 11.01498 5.999981 14.49985 10:14:13 AM -106.25 -107.319 Left
10:14:14 AM 6.145187 11.01672 5.999981 14.49985 10:14:14 AM -106.427 -107.547 Left
10:14:15 AM 6.140853 11.00891 5.999981 14.49997 10:14:15 AM -110.11 -107.514 Right
10:14:16 AM 6.134578 11.00257 5.999981 14.49985 10:14:16 AM -111.484 -107.63 Right
10:14:17 AM 6.126868 10.99637 5.999981 14.49985 10:14:17 AM -109.464 -108.303 Right
10:14:18 AM 6.142395 10.95863 5.999981 14.49997 10:14:18 AM -109.082 -120.846 Left
10:14:19 AM 6.148995 10.95172 5.999981 14.49997 10:14:19 AM -107.394 -121.711 Left
10:14:20 AM 6.14954 10.96553 5.999981 14.4998
10:14:21 AM 6.120224 11.00731 5.999981 14.49997 10:14:21 AM -110.192 -121.456 Left
10:14:22 AM 6.11064 11.02822 5.999981 14.49985 10:14:22 AM -110.177 -127.288 Left
10:14:23 AM 6.07086 11.05933 5.999981 14.49997 10:14:23 AM #NAME? #NAME? Mid
10:14:24 AM -110.714 -132.566 Left
10:14:25 AM 6.027144 11.08917 5.999981 14.49996 10:14:25 AM -111.257 -136.161 Left
10:14:26 AM 5.976007 11.11179 5.999981 14.4998 10:14:26 AM -112.081 -140.23 Left
10:14:27 AM 5.94027 11.12349 5.999981 14.4998 10:14:27 AM -112.447 -142.929 Left
10:14:28 AM 5.896355 11.13249 5.999981 14.49997 10:14:28 AM -110.031 -144.912 Left
10:14:29 AM 5.856781 11.12952 5.999981 14.49996 10:14:29 AM -110.321 -146.758 Left
10:14:30 AM 5.80122 11.13281 5.999981 14.4998 10:14:30 AM -106.377 -149.661 Left
10:14:31 AM 5.749688 11.14359 5.999981 14.49997 10:14:31 AM -107.998 -142.594 Left
10:14:32 AM 5.702169 11.15886 5.999981 14.49985 10:14:32 AM #NAME? #NAME? Mid
10:14:33 AM 5.642862 11.19348 5.999981 14.49996 10:14:33 AM -107.457 -138.866 Left
10:14:34 AM 5.609009 11.21532 5.999981 14.49996 10:14:34 AM -106.3 -137.481 Left
10:14:35 AM 5.56469 11.24916 5.999981 14.49997 10:14:35 AM -106.688 -135.484 Left
10:14:36 AM -107.583 -133.49 Left
10:14:37 AM 5.529091 11.28092 5.999981 14.49996 10:14:37 AM -108.618 -132.668 Left
10:14:38 AM 5.495217 11.3248 5.999981 14.49985 10:14:38 AM #NAME? #NAME? Mid
10:14:39 AM 5.475418 11.36049 5.999981 14.49997
10:14:40 AM 5.461507 11.40364 5.999981 14.49985 10:14:40 AM -109.518 -124.934 Left
10:14:41 AM 5.440051 11.45829 5.999981 14.49997 10:14:41 AM -109.664 -126.322 Left
10:14:42 AM 5.421883 11.50379 5.999981 14.49996 10:14:42 AM -108.333 -127.681 Left
10:14:43 AM 5.401868 11.55611 5.999981 14.49997 10:14:43 AM -108.116 -128.118 Left
10:14:44 AM 5.380318 11.60897 5.999981 14.49985 10:14:44 AM -109.132 -130.318 Left
10:14:45 AM 5.364387 11.65057 5.999981 14.4998 10:14:45 AM -109.022 -130.6 Left
10:14:46 AM -110.706 -132.62 Left
10:14:47 AM 5.341948 11.69557 5.999981 14.49996 10:14:47 AM -109.805 -129.706 Left
10:14:48 AM 5.329875 11.75236 5.999981 14.49996 10:14:48 AM -110.187 -131.28 Left
10:14:49 AM 5.314241 11.80533 5.999981 14.49985 10:14:49 AM -108.399 -129.789 Left
10:14:50 AM 5.305806 11.85946 5.999981 14.4998
10:14:51 AM 5.306743 11.89437 5.999981 14.49996 10:14:51 AM -112.841 -119.404 Left
10:14:52 AM 5.305887 11.89471 5.999981 14.4998 10:14:52 AM -111.333 -121.329 Left
10:14:53 AM 5.30481 11.89482 5.999981 14.49996 10:14:53 AM -112.74 -120.444 Left
10:14:54 AM 5.304423 11.89456 5.999981 14.49996 10:14:54 AM -111.456 -123.375 Left
10:14:55 AM 5.30336 11.895 5.999981 14.49985 10:14:55 AM -111.91 -123.153 Left
10:14:56 AM 5.30139 11.89341 5.999981 14.49985 10:14:56 AM -111.031 -123.401 Left
10:14:57 AM -110.793 -123.839 Left
10:14:58 AM 5.300543 11.89305 5.999981 14.49985 10:14:58 AM -111.952 -121.689 Left
10:14:59 AM 5.300544 11.89305 5.999981 14.49997 10:14:59 AM -113.44 -120.002 Left
10:15:00 AM 5.300546 11.89305 5.999981 14.49996 10:15:00 AM -113.355 -119.982 Left
10:15:01 AM 5.300547 11.89305 5.999981 14.49997 10:15:01 AM -113.349 -119.971 Left
10:15:02 AM 5.300554 11.89305 5.999981 14.49985 10:15:02 AM -113.457 -119.98 Left
10:15:03 AM 5.300559 11.89304 5.999981 14.49996 10:15:03 AM -113.344 -119.975 Left
10:15:04 AM 5.30056 11.89304 5.999981 14.49996 10:15:04 AM -113.351 -119.983 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
274
Single Non-Uniform Obstruction Sample 2
Motion Path Graph - Single Non-Uniform obstruction – Sample 2.
Combined logs – Single Non-Uniform obstruction – Sample 2.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.25.45.65.866.26.46.66.87
29-6-10-15
CARMI Child
X Z X Z L R
10:15:11 AM 6 9.5 6 14.5
10:15:13 AM 6 9.5 6 14.5
10:15:14 AM 6 9.5 6 14.5
10:15:15 AM 5.994308 9.500896 5.999981 14.49985
10:15:16 AM 6.011705 9.688725 5.999981 14.49997 10:15:16 AM -133.196 -114.818 Right
10:15:17 AM 6.012587 9.688594 5.999981 14.49997 10:15:17 AM -133.468 -111.689 Right
10:15:18 AM 6.012868 9.68847 5.999981 14.49985 10:15:18 AM -131.599 -112.553 Right
10:15:19 AM 6.013505 9.688615 5.999981 14.4998 10:15:19 AM -131.134 -112.334 Right
10:15:20 AM 6.014375 9.689281 5.999981 14.49997 10:15:20 AM -131.074 -112.977 Right
10:15:21 AM -131.058 -112.709 Right
10:15:22 AM 6.014764 9.689977 5.999981 14.49997 10:15:22 AM -130.746 -112.007 Right
10:15:23 AM 6.015401 9.689497 5.999981 14.49985 10:15:23 AM -130.371 -111.646 Right
10:15:24 AM 6.014698 9.76862 5.999981 14.49997
10:15:25 AM 6.008034 10.11512 5.999981 14.49985 10:15:25 AM -129.743 -113.956 Right
10:15:26 AM 6.001469 10.45637 5.999981 14.4998 10:15:26 AM -125.08 -114.417 Right
10:15:27 AM 5.994901 10.79762 5.999981 14.49997 10:15:27 AM -117.042 -113.094 Right
10:15:28 AM 5.986585 11.05514 5.999981 14.4998 10:15:28 AM #NAME? #NAME? Mid
10:15:29 AM 5.977194 11.05714 5.999981 14.49985 10:15:29 AM -110.042 -111.487 Left
10:15:30 AM 5.968297 11.06085 5.999981 14.4998 10:15:30 AM -116.185 -110.696 Right
10:15:31 AM 5.959096 11.06872 5.999981 14.49997 10:15:31 AM -121.786 -110.306 Right
10:15:32 AM -128.323 -108.181 Right
10:15:33 AM 5.952369 11.07753 5.999981 14.49996 10:15:33 AM -134.193 -108.237 Right
10:15:34 AM 5.94794 11.08485 5.999981 14.49996 10:15:34 AM -137.615 -107.843 Right
10:15:35 AM 5.984583 11.10779 5.999981 14.49985 10:15:35 AM -139.084 -109.197 Right
10:15:36 AM 6.03102 11.12298 5.999981 14.4998 10:15:36 AM -143.162 -109.015 Right
10:15:37 AM 6.08515 11.13496 5.999981 14.49997
10:15:38 AM 6.131732 11.14088 5.999981 14.4998 10:15:38 AM -147.214 -109.419 Right
10:15:39 AM 6.180035 11.14217 5.999981 14.49997 10:15:39 AM -149.494 -110.027 Right
10:15:40 AM 6.241859 11.12912 5.999981 14.49996 10:15:40 AM -155.086 -109.518 Right
10:15:41 AM 6.287606 11.11476 5.999981 14.4998 10:15:41 AM -157.074 -108.382 Right
10:15:42 AM 6.328498 11.09644 5.999981 14.49997 10:15:42 AM -159.059 -108.906 Right
10:15:43 AM -155.406 -105.672 Right
10:15:44 AM 6.377422 11.07776 5.999981 14.49985 10:15:44 AM -158.41 -108.969 Right
10:15:45 AM 6.427683 11.05554 5.999981 14.4998 10:15:45 AM 124 Recenter-Right
10:15:46 AM 6.448325 11.04522 5.999981 14.49997 10:15:46 AM -158.062 -106.754 Right
10:15:47 AM 6.447668 11.04526 5.999981 14.49996 10:15:47 AM -155.091 -107.359 Right
10:15:48 AM 6.447338 11.04475 5.999981 14.49997 10:15:48 AM -155.756 -109.431 Right
10:15:49 AM 6.447227 11.04415 5.999981 14.49985 10:15:49 AM -156.493 -107.918 Right
10:15:50 AM 6.446936 11.04281 5.999981 14.49996 10:15:50 AM -157.333 -107.337 Right
10:15:51 AM 6.447393 11.0419 5.999981 14.49985
10:15:52 AM 6.448669 11.04137 5.999981 14.49997 10:15:52 AM -157.18 -106.901 Right
10:15:53 AM 6.448472 11.04022 5.999981 14.49997 10:15:53 AM -154.86 -107.584 Right
10:15:54 AM 6.448925 11.03927 5.999981 14.49996 10:15:54 AM -155.106 -107.471 Right
10:15:55 AM -155.361 -106.97 Right
10:15:56 AM 6.449743 11.03851 5.999981 14.4998 10:15:56 AM -154.355 -106.969 Right
10:15:57 AM 6.450462 11.03801 5.999981 14.49997 10:15:57 AM -153.511 -107.182 Right
10:15:58 AM 6.452027 11.03657 5.999981 14.49985
10:15:59 AM 6.452987 11.03602 5.999981 14.49997 10:15:59 AM -153.908 -107.077 Right
10:16:00 AM 6.453202 11.03454 5.999981 14.49996 10:16:00 AM -153.938 -107.089 Right
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
275
X Z X Z L R
10:15:11 AM 6 9.5 6 14.5
10:15:13 AM 6 9.5 6 14.5
10:15:14 AM 6 9.5 6 14.5
10:15:15 AM 5.994308 9.500896 5.999981 14.49985
10:15:16 AM 6.011705 9.688725 5.999981 14.49997 10:15:16 AM -133.196 -114.818 Right
10:15:17 AM 6.012587 9.688594 5.999981 14.49997 10:15:17 AM -133.468 -111.689 Right
10:15:18 AM 6.012868 9.68847 5.999981 14.49985 10:15:18 AM -131.599 -112.553 Right
10:15:19 AM 6.013505 9.688615 5.999981 14.4998 10:15:19 AM -131.134 -112.334 Right
10:15:20 AM 6.014375 9.689281 5.999981 14.49997 10:15:20 AM -131.074 -112.977 Right
10:15:21 AM -131.058 -112.709 Right
10:15:22 AM 6.014764 9.689977 5.999981 14.49997 10:15:22 AM -130.746 -112.007 Right
10:15:23 AM 6.015401 9.689497 5.999981 14.49985 10:15:23 AM -130.371 -111.646 Right
10:15:24 AM 6.014698 9.76862 5.999981 14.49997
10:15:25 AM 6.008034 10.11512 5.999981 14.49985 10:15:25 AM -129.743 -113.956 Right
10:15:26 AM 6.001469 10.45637 5.999981 14.4998 10:15:26 AM -125.08 -114.417 Right
10:15:27 AM 5.994901 10.79762 5.999981 14.49997 10:15:27 AM -117.042 -113.094 Right
10:15:28 AM 5.986585 11.05514 5.999981 14.4998 10:15:28 AM #NAME? #NAME? Mid
10:15:29 AM 5.977194 11.05714 5.999981 14.49985 10:15:29 AM -110.042 -111.487 Left
10:15:30 AM 5.968297 11.06085 5.999981 14.4998 10:15:30 AM -116.185 -110.696 Right
10:15:31 AM 5.959096 11.06872 5.999981 14.49997 10:15:31 AM -121.786 -110.306 Right
10:15:32 AM -128.323 -108.181 Right
10:15:33 AM 5.952369 11.07753 5.999981 14.49996 10:15:33 AM -134.193 -108.237 Right
10:15:34 AM 5.94794 11.08485 5.999981 14.49996 10:15:34 AM -137.615 -107.843 Right
10:15:35 AM 5.984583 11.10779 5.999981 14.49985 10:15:35 AM -139.084 -109.197 Right
10:15:36 AM 6.03102 11.12298 5.999981 14.4998 10:15:36 AM -143.162 -109.015 Right
10:15:37 AM 6.08515 11.13496 5.999981 14.49997
10:15:38 AM 6.131732 11.14088 5.999981 14.4998 10:15:38 AM -147.214 -109.419 Right
10:15:39 AM 6.180035 11.14217 5.999981 14.49997 10:15:39 AM -149.494 -110.027 Right
10:15:40 AM 6.241859 11.12912 5.999981 14.49996 10:15:40 AM -155.086 -109.518 Right
10:15:41 AM 6.287606 11.11476 5.999981 14.4998 10:15:41 AM -157.074 -108.382 Right
10:15:42 AM 6.328498 11.09644 5.999981 14.49997 10:15:42 AM -159.059 -108.906 Right
10:15:43 AM -155.406 -105.672 Right
10:15:44 AM 6.377422 11.07776 5.999981 14.49985 10:15:44 AM -158.41 -108.969 Right
10:15:45 AM 6.427683 11.05554 5.999981 14.4998 10:15:45 AM 124 Recenter-Right
10:15:46 AM 6.448325 11.04522 5.999981 14.49997 10:15:46 AM -158.062 -106.754 Right
10:15:47 AM 6.447668 11.04526 5.999981 14.49996 10:15:47 AM -155.091 -107.359 Right
10:15:48 AM 6.447338 11.04475 5.999981 14.49997 10:15:48 AM -155.756 -109.431 Right
10:15:49 AM 6.447227 11.04415 5.999981 14.49985 10:15:49 AM -156.493 -107.918 Right
10:15:50 AM 6.446936 11.04281 5.999981 14.49996 10:15:50 AM -157.333 -107.337 Right
10:15:51 AM 6.447393 11.0419 5.999981 14.49985
10:15:52 AM 6.448669 11.04137 5.999981 14.49997 10:15:52 AM -157.18 -106.901 Right
10:15:53 AM 6.448472 11.04022 5.999981 14.49997 10:15:53 AM -154.86 -107.584 Right
10:15:54 AM 6.448925 11.03927 5.999981 14.49996 10:15:54 AM -155.106 -107.471 Right
10:15:55 AM -155.361 -106.97 Right
10:15:56 AM 6.449743 11.03851 5.999981 14.4998 10:15:56 AM -154.355 -106.969 Right
10:15:57 AM 6.450462 11.03801 5.999981 14.49997 10:15:57 AM -153.511 -107.182 Right
10:15:58 AM 6.452027 11.03657 5.999981 14.49985
10:15:59 AM 6.452987 11.03602 5.999981 14.49997 10:15:59 AM -153.908 -107.077 Right
10:16:00 AM 6.453202 11.03454 5.999981 14.49996 10:16:00 AM -153.938 -107.089 Right
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
276
Single Non-Uniform Obstruction Sample 3
Motion Path Graph - Single Non-Uniform obstruction – Sample 3.
Combined logs – Single Non-Uniform obstruction – Sample 3.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.25.45.65.866.26.46.66.87
29-6-10-16
CARMI Child
X Z X Z L R
10:16:53 AM 6 9.5 6 14.5
10:16:54 AM 6 9.5 6 14.5
10:16:55 AM 6 9.5 6 14.5
10:16:56 AM 5.993452 9.500679 5.999981 14.49996
10:16:57 AM 5.999599 9.552241 5.999981 14.49996 10:16:57 AM -132.124 -116.025 Right
10:16:58 AM 6.000326 9.552691 5.999981 14.49985 10:16:58 AM -134.157 -110.572 Right
10:16:59 AM -131.811 -112.343 Right
10:17:00 AM 6.000171 9.553128 5.999981 14.49996 10:17:00 AM -132.424 -112.206 Right
10:17:01 AM 6.000528 9.553026 5.999981 14.49996 10:17:01 AM -132.63 -111.586 Right
10:17:02 AM 6.000697 9.554759 5.999981 14.4998 10:17:02 AM -132.506 -110.914 Right
10:17:03 AM 6.002162 9.555518 5.999981 14.49996
10:17:04 AM 6.003139 9.555337 5.999981 14.49985 10:17:04 AM -130.727 -112.015 Right
10:17:05 AM 6.004873 9.623884 5.999981 14.49997 10:17:05 AM -129.967 -111.937 Right
10:17:06 AM 6.011419 9.969964 5.999981 14.49996 10:17:06 AM -131.387 -113.284 Right
10:17:07 AM 6.017867 10.31073 5.999981 14.4998 10:17:07 AM -129.489 -114.628 Right
10:17:08 AM 6.024119 10.64093 5.999981 14.49985 10:17:08 AM -124.298 -113.608 Right
10:17:09 AM 6.030694 10.98676 5.999981 14.49997 10:17:09 AM -115.538 -111.615 Right
10:17:10 AM 6.04765 10.99352 5.999981 14.49996 10:17:10 AM -110.186 -108.531 Right
10:17:11 AM -109.742 -112.638 Left
10:17:12 AM 6.068041 10.9871 5.999981 14.49985 10:17:12 AM -110.432 -107.518 Right
10:17:13 AM 6.077093 10.99598 5.999981 14.4998 10:17:13 AM -105.983 -107.361 Left
10:17:14 AM 6.071382 10.98927 5.999981 14.49997 10:17:14 AM -110.106 -107.525 Right
10:17:15 AM 6.064232 10.98177 5.999981 14.49996 10:17:15 AM -111.033 -107.44 Right
10:17:16 AM 6.057449 10.97628 5.999981 14.49985 10:17:16 AM -110.042 -107.665 Right
10:17:17 AM 6.05679 10.96443 5.999981 14.49997 10:17:17 AM -106.477 -118.164 Left
10:17:18 AM 6.072156 10.93973 5.999981 14.49996 10:17:18 AM -108.472 -119.953 Left
10:17:19 AM 6.072358 10.94778 5.999981 14.4998
10:17:20 AM 6.067204 10.9676 5.999981 14.49996 10:17:20 AM -109.532 -119.306 Left
10:17:21 AM 6.046095 11.00017 5.999981 14.4998 10:17:21 AM -109.895 -123.513 Left
10:17:22 AM 6.014355 11.03808 5.999981 14.49996 10:17:22 AM -109.449 -126.515 Left
10:17:23 AM -110.324 -128.663 Left
10:17:24 AM 5.972888 11.07598 5.999981 14.49996 10:17:24 AM -110.986 -132.091 Left
10:17:25 AM 5.929632 11.10214 5.999981 14.49985 10:17:25 AM -111.862 -136.251 Left
10:17:26 AM 5.883834 11.12943 5.999981 14.4998 10:17:26 AM -112.447 -138.189 Left
10:17:27 AM 5.842865 11.14613 5.999981 14.49997 10:17:27 AM -110.127 -142.235 Left
10:17:28 AM 5.793264 11.14915 5.999981 14.49985 10:17:28 AM -109.969 -146.577 Left
10:17:29 AM 5.74009 11.1601 5.999981 14.49985 10:17:29 AM -106.888 -146.841 Left
10:17:30 AM 5.69971 11.17113 5.999981 14.49997 10:17:30 AM -108.855 -140.573 Left
10:17:31 AM 5.655742 11.19149 5.999981 14.49997 10:17:31 AM -107.092 -141.764 Left
10:17:32 AM 5.611956 11.21838 5.999981 14.49996 10:17:32 AM -106.573 -137.738 Left
10:17:33 AM 5.573955 11.24575 5.999981 14.4998 10:17:33 AM -106.656 -133.394 Left
10:17:34 AM -106.971 -131.146 Left
10:17:35 AM 5.54641 11.27501 5.999981 14.49996 10:17:35 AM -108.498 -130.312 Left
10:17:36 AM 5.519897 11.31632 5.999981 14.49985 10:17:36 AM -115.724 -124.703 Left
10:17:37 AM 5.504065 11.35276 5.999981 14.4998
10:17:38 AM 5.484795 11.39576 5.999981 14.49985 10:17:38 AM -106.949 -122.888 Left
10:17:39 AM 5.465627 11.44603 5.999981 14.49996 10:17:39 AM -110.239 -126.36 Left
10:17:40 AM 5.450945 11.49048 5.999981 14.49985 10:17:40 AM -110.013 -126.911 Left
10:17:41 AM 5.429731 11.54324 5.999981 14.49985 10:17:41 AM -109.354 -127.565 Left
10:17:42 AM 5.413665 11.59411 5.999981 14.49997 10:17:42 AM -107.401 -127.635 Left
10:17:43 AM 5.397177 11.63756 5.999981 14.49996 10:17:43 AM -109.198 -128.934 Left
10:17:44 AM 5.376156 11.69708 5.999981 14.49985 10:17:44 AM -108.216 -129.755 Left
10:17:45 AM 5.361761 11.74582 5.999981 14.49996 10:17:45 AM -111.217 -134.354 Left
10:17:46 AM -109 -127.934 Left
10:17:47 AM 5.340284 11.78464 5.999981 14.4998 10:17:47 AM -110.223 -130.236 Left
10:17:48 AM 5.327333 11.82891 5.999981 14.49996 10:17:48 AM -108.933 -129.492 Left
10:17:49 AM 5.315457 11.88585 5.999981 14.4998 10:17:49 AM -110.138 -127.054 Left
10:17:50 AM 5.315816 11.92651 5.999981 14.4998 10:17:50 AM -110.513 -125.454 Left
10:17:51 AM 5.314696 11.92731 5.999981 14.49996 10:17:51 AM -109.53 -125.917 Left
10:17:52 AM 5.313026 11.92741 5.999981 14.49985
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
277
X Z X Z L R
10:16:53 AM 6 9.5 6 14.5
10:16:54 AM 6 9.5 6 14.5
10:16:55 AM 6 9.5 6 14.5
10:16:56 AM 5.993452 9.500679 5.999981 14.49996
10:16:57 AM 5.999599 9.552241 5.999981 14.49996 10:16:57 AM -132.124 -116.025 Right
10:16:58 AM 6.000326 9.552691 5.999981 14.49985 10:16:58 AM -134.157 -110.572 Right
10:16:59 AM -131.811 -112.343 Right
10:17:00 AM 6.000171 9.553128 5.999981 14.49996 10:17:00 AM -132.424 -112.206 Right
10:17:01 AM 6.000528 9.553026 5.999981 14.49996 10:17:01 AM -132.63 -111.586 Right
10:17:02 AM 6.000697 9.554759 5.999981 14.4998 10:17:02 AM -132.506 -110.914 Right
10:17:03 AM 6.002162 9.555518 5.999981 14.49996
10:17:04 AM 6.003139 9.555337 5.999981 14.49985 10:17:04 AM -130.727 -112.015 Right
10:17:05 AM 6.004873 9.623884 5.999981 14.49997 10:17:05 AM -129.967 -111.937 Right
10:17:06 AM 6.011419 9.969964 5.999981 14.49996 10:17:06 AM -131.387 -113.284 Right
10:17:07 AM 6.017867 10.31073 5.999981 14.4998 10:17:07 AM -129.489 -114.628 Right
10:17:08 AM 6.024119 10.64093 5.999981 14.49985 10:17:08 AM -124.298 -113.608 Right
10:17:09 AM 6.030694 10.98676 5.999981 14.49997 10:17:09 AM -115.538 -111.615 Right
10:17:10 AM 6.04765 10.99352 5.999981 14.49996 10:17:10 AM -110.186 -108.531 Right
10:17:11 AM -109.742 -112.638 Left
10:17:12 AM 6.068041 10.9871 5.999981 14.49985 10:17:12 AM -110.432 -107.518 Right
10:17:13 AM 6.077093 10.99598 5.999981 14.4998 10:17:13 AM -105.983 -107.361 Left
10:17:14 AM 6.071382 10.98927 5.999981 14.49997 10:17:14 AM -110.106 -107.525 Right
10:17:15 AM 6.064232 10.98177 5.999981 14.49996 10:17:15 AM -111.033 -107.44 Right
10:17:16 AM 6.057449 10.97628 5.999981 14.49985 10:17:16 AM -110.042 -107.665 Right
10:17:17 AM 6.05679 10.96443 5.999981 14.49997 10:17:17 AM -106.477 -118.164 Left
10:17:18 AM 6.072156 10.93973 5.999981 14.49996 10:17:18 AM -108.472 -119.953 Left
10:17:19 AM 6.072358 10.94778 5.999981 14.4998
10:17:20 AM 6.067204 10.9676 5.999981 14.49996 10:17:20 AM -109.532 -119.306 Left
10:17:21 AM 6.046095 11.00017 5.999981 14.4998 10:17:21 AM -109.895 -123.513 Left
10:17:22 AM 6.014355 11.03808 5.999981 14.49996 10:17:22 AM -109.449 -126.515 Left
10:17:23 AM -110.324 -128.663 Left
10:17:24 AM 5.972888 11.07598 5.999981 14.49996 10:17:24 AM -110.986 -132.091 Left
10:17:25 AM 5.929632 11.10214 5.999981 14.49985 10:17:25 AM -111.862 -136.251 Left
10:17:26 AM 5.883834 11.12943 5.999981 14.4998 10:17:26 AM -112.447 -138.189 Left
10:17:27 AM 5.842865 11.14613 5.999981 14.49997 10:17:27 AM -110.127 -142.235 Left
10:17:28 AM 5.793264 11.14915 5.999981 14.49985 10:17:28 AM -109.969 -146.577 Left
10:17:29 AM 5.74009 11.1601 5.999981 14.49985 10:17:29 AM -106.888 -146.841 Left
10:17:30 AM 5.69971 11.17113 5.999981 14.49997 10:17:30 AM -108.855 -140.573 Left
10:17:31 AM 5.655742 11.19149 5.999981 14.49997 10:17:31 AM -107.092 -141.764 Left
10:17:32 AM 5.611956 11.21838 5.999981 14.49996 10:17:32 AM -106.573 -137.738 Left
10:17:33 AM 5.573955 11.24575 5.999981 14.4998 10:17:33 AM -106.656 -133.394 Left
10:17:34 AM -106.971 -131.146 Left
10:17:35 AM 5.54641 11.27501 5.999981 14.49996 10:17:35 AM -108.498 -130.312 Left
10:17:36 AM 5.519897 11.31632 5.999981 14.49985 10:17:36 AM -115.724 -124.703 Left
10:17:37 AM 5.504065 11.35276 5.999981 14.4998
10:17:38 AM 5.484795 11.39576 5.999981 14.49985 10:17:38 AM -106.949 -122.888 Left
10:17:39 AM 5.465627 11.44603 5.999981 14.49996 10:17:39 AM -110.239 -126.36 Left
10:17:40 AM 5.450945 11.49048 5.999981 14.49985 10:17:40 AM -110.013 -126.911 Left
10:17:41 AM 5.429731 11.54324 5.999981 14.49985 10:17:41 AM -109.354 -127.565 Left
10:17:42 AM 5.413665 11.59411 5.999981 14.49997 10:17:42 AM -107.401 -127.635 Left
10:17:43 AM 5.397177 11.63756 5.999981 14.49996 10:17:43 AM -109.198 -128.934 Left
10:17:44 AM 5.376156 11.69708 5.999981 14.49985 10:17:44 AM -108.216 -129.755 Left
10:17:45 AM 5.361761 11.74582 5.999981 14.49996 10:17:45 AM -111.217 -134.354 Left
10:17:46 AM -109 -127.934 Left
10:17:47 AM 5.340284 11.78464 5.999981 14.4998 10:17:47 AM -110.223 -130.236 Left
10:17:48 AM 5.327333 11.82891 5.999981 14.49996 10:17:48 AM -108.933 -129.492 Left
10:17:49 AM 5.315457 11.88585 5.999981 14.4998 10:17:49 AM -110.138 -127.054 Left
10:17:50 AM 5.315816 11.92651 5.999981 14.4998 10:17:50 AM -110.513 -125.454 Left
10:17:51 AM 5.314696 11.92731 5.999981 14.49996 10:17:51 AM -109.53 -125.917 Left
10:17:52 AM 5.313026 11.92741 5.999981 14.49985
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
278
Single Non-Uniform Obstruction Sample 4
Motion Path Graph - Single Non-Uniform obstruction – Sample 4.
Combined logs – Single Non-Uniform obstruction – Sample 4.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.25.45.65.866.26.46.66.87
29-6-10-18
CARMI Child
X Z X Z L R
10:18:05 AM 6 9.5 6 14.5
10:18:06 AM 6 9.5 6 14.5
10:18:07 AM 6 9.5 6 14.5
10:18:09 AM 5.993934 9.500367 5.999981 14.49997
10:18:10 AM 6.010031 9.619991 5.999981 14.4998 10:18:10 AM -132.834 -116.985 Right
10:18:11 AM 6.011676 9.621343 5.999981 14.49996 10:18:11 AM -134.814 -110.081 Right
10:18:12 AM 6.012438 9.621804 5.999981 14.49985 10:18:12 AM -132.063 -111.835 Right
10:18:13 AM 6.013259 9.623123 5.999981 14.4998 10:18:13 AM -131.044 -113.105 Right
10:18:14 AM 6.014624 9.623962 5.999981 14.49985 10:18:14 AM -131.16 -112.389 Right
10:18:15 AM 6.015474 9.624219 5.999981 14.49997 10:18:15 AM -131.011 -111.704 Right
10:18:16 AM 6.014895 9.797959 5.999981 14.49997 10:18:16 AM -130.022 -113.046 Right
10:18:17 AM -131.57 -113.303 Right
10:18:18 AM 6.015023 10.14977 5.999981 14.4998 10:18:18 AM -126.974 -113.786 Right
10:18:19 AM 6.015141 10.47532 5.999981 14.49996 10:18:19 AM -117.066 -111.781 Right
10:18:20 AM 6.015267 10.82188 5.999981 14.4998 10:18:20 AM -109.104 -109.444 Left
10:18:21 AM 6.026482 11.04233 5.999981 14.4998
10:18:22 AM 6.034119 11.04236 5.999981 14.4998 10:18:22 AM -110.76 -108.059 Right
10:18:23 AM 6.04326 11.04776 5.999981 14.49985 10:18:23 AM -110.612 -115.867 Left
10:18:24 AM 6.059678 11.04736 5.999981 14.49997 10:18:24 AM -110.446 -123.748 Left
10:18:25 AM 6.05544 11.05963 5.999981 14.49996 10:18:25 AM -110.835 -127.309 Left
10:18:26 AM 6.010893 11.09063 5.999981 14.4998 10:18:26 AM -111.74 -134.211 Left
10:18:27 AM 5.976772 11.11469 5.999981 14.49997 10:18:27 AM -111.982 -136.212 Left
10:18:28 AM -112.318 -137.912 Left
10:18:29 AM 5.936329 11.12874 5.999981 14.49985 10:18:29 AM -112.341 -140.625 Left
10:18:30 AM 5.878414 11.14417 5.999981 14.49996 10:18:30 AM -110.242 -145.539 Left
10:18:31 AM 5.828092 11.14292 5.999981 14.49985 10:18:31 AM -109.433 -146.94 Left
10:18:32 AM 5.779953 11.14536 5.999981 14.49997 10:18:32 AM -106.493 -146.371 Left
10:18:33 AM 5.717555 11.16523 5.999981 14.4998 10:18:33 AM -106.874 -142.403 Left
10:18:34 AM 5.675188 11.18161 5.999981 14.49997 10:18:34 AM -107.15 -140.403 Left
10:18:35 AM 5.641723 11.19602 5.999981 14.49996 10:18:35 AM -107.341 -139.42 Left
10:18:36 AM 5.606854 11.22756 5.999981 14.49985
10:18:37 AM 5.581649 11.25686 5.999981 14.49996 10:18:37 AM -114.069 -133.043 Left
10:18:38 AM 5.5511 11.29459 5.999981 14.49985 10:18:38 AM -106.67 -128.454 Left
10:18:39 AM -107.816 -130.315 Left
10:18:40 AM 5.522519 11.33867 5.999981 14.49996 10:18:40 AM -108.905 -128.1 Left
10:18:41 AM 5.504525 11.39319 5.999981 14.49996 10:18:41 AM -107.914 -124.633 Left
10:18:42 AM 5.48465 11.44384 5.999981 14.49985 10:18:42 AM -108.392 -123.207 Left
10:18:43 AM 5.46643 11.48882 5.999981 14.49985 10:18:43 AM -107.316 -127.406 Left
10:18:44 AM 5.443839 11.53905 5.999981 14.4998 10:18:44 AM -109.651 -128.393 Left
10:18:45 AM 5.422859 11.59307 5.999981 14.49996 10:18:45 AM -109.555 -129.139 Left
10:18:46 AM 5.40688 11.63516 5.999981 14.49997 10:18:46 AM #NAME? #NAME? Mid
10:18:47 AM 5.385821 11.69192 5.999981 14.49996 10:18:47 AM -108.106 -129.574 Left
10:18:48 AM 5.363208 11.74637 5.999981 14.49985 10:18:48 AM -110.058 -131.333 Left
10:18:49 AM 5.350856 11.78458 5.999981 14.4998 10:18:49 AM -108.528 -133.115 Left
10:18:51 AM 5.348593 11.83465 5.999981 14.49996 10:18:51 AM -111.234 -120.562 Left
10:18:52 AM 5.34094 11.87351 5.999981 14.49997 10:18:52 AM -109.552 -121.474 Left
10:18:53 AM 5.339965 11.87398 5.999981 14.49985 10:18:53 AM -110.263 -128.251 Left
10:18:54 AM 5.338418 11.87351 5.999981 14.49996 10:18:54 AM -110.1 -127.286 Left
10:18:55 AM 5.337015 11.87264 5.999981 14.49996 10:18:55 AM -111.259 -123.338 Left
10:18:56 AM 5.336518 11.87282 5.999981 14.4998 10:18:56 AM -112.944 -123.609 Left
10:18:57 AM 5.33497 11.87354 5.999981 14.4998 10:18:57 AM -111.497 -125.395 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
279
X Z X Z L R
10:18:05 AM 6 9.5 6 14.5
10:18:06 AM 6 9.5 6 14.5
10:18:07 AM 6 9.5 6 14.5
10:18:09 AM 5.993934 9.500367 5.999981 14.49997
10:18:10 AM 6.010031 9.619991 5.999981 14.4998 10:18:10 AM -132.834 -116.985 Right
10:18:11 AM 6.011676 9.621343 5.999981 14.49996 10:18:11 AM -134.814 -110.081 Right
10:18:12 AM 6.012438 9.621804 5.999981 14.49985 10:18:12 AM -132.063 -111.835 Right
10:18:13 AM 6.013259 9.623123 5.999981 14.4998 10:18:13 AM -131.044 -113.105 Right
10:18:14 AM 6.014624 9.623962 5.999981 14.49985 10:18:14 AM -131.16 -112.389 Right
10:18:15 AM 6.015474 9.624219 5.999981 14.49997 10:18:15 AM -131.011 -111.704 Right
10:18:16 AM 6.014895 9.797959 5.999981 14.49997 10:18:16 AM -130.022 -113.046 Right
10:18:17 AM -131.57 -113.303 Right
10:18:18 AM 6.015023 10.14977 5.999981 14.4998 10:18:18 AM -126.974 -113.786 Right
10:18:19 AM 6.015141 10.47532 5.999981 14.49996 10:18:19 AM -117.066 -111.781 Right
10:18:20 AM 6.015267 10.82188 5.999981 14.4998 10:18:20 AM -109.104 -109.444 Left
10:18:21 AM 6.026482 11.04233 5.999981 14.4998
10:18:22 AM 6.034119 11.04236 5.999981 14.4998 10:18:22 AM -110.76 -108.059 Right
10:18:23 AM 6.04326 11.04776 5.999981 14.49985 10:18:23 AM -110.612 -115.867 Left
10:18:24 AM 6.059678 11.04736 5.999981 14.49997 10:18:24 AM -110.446 -123.748 Left
10:18:25 AM 6.05544 11.05963 5.999981 14.49996 10:18:25 AM -110.835 -127.309 Left
10:18:26 AM 6.010893 11.09063 5.999981 14.4998 10:18:26 AM -111.74 -134.211 Left
10:18:27 AM 5.976772 11.11469 5.999981 14.49997 10:18:27 AM -111.982 -136.212 Left
10:18:28 AM -112.318 -137.912 Left
10:18:29 AM 5.936329 11.12874 5.999981 14.49985 10:18:29 AM -112.341 -140.625 Left
10:18:30 AM 5.878414 11.14417 5.999981 14.49996 10:18:30 AM -110.242 -145.539 Left
10:18:31 AM 5.828092 11.14292 5.999981 14.49985 10:18:31 AM -109.433 -146.94 Left
10:18:32 AM 5.779953 11.14536 5.999981 14.49997 10:18:32 AM -106.493 -146.371 Left
10:18:33 AM 5.717555 11.16523 5.999981 14.4998 10:18:33 AM -106.874 -142.403 Left
10:18:34 AM 5.675188 11.18161 5.999981 14.49997 10:18:34 AM -107.15 -140.403 Left
10:18:35 AM 5.641723 11.19602 5.999981 14.49996 10:18:35 AM -107.341 -139.42 Left
10:18:36 AM 5.606854 11.22756 5.999981 14.49985
10:18:37 AM 5.581649 11.25686 5.999981 14.49996 10:18:37 AM -114.069 -133.043 Left
10:18:38 AM 5.5511 11.29459 5.999981 14.49985 10:18:38 AM -106.67 -128.454 Left
10:18:39 AM -107.816 -130.315 Left
10:18:40 AM 5.522519 11.33867 5.999981 14.49996 10:18:40 AM -108.905 -128.1 Left
10:18:41 AM 5.504525 11.39319 5.999981 14.49996 10:18:41 AM -107.914 -124.633 Left
10:18:42 AM 5.48465 11.44384 5.999981 14.49985 10:18:42 AM -108.392 -123.207 Left
10:18:43 AM 5.46643 11.48882 5.999981 14.49985 10:18:43 AM -107.316 -127.406 Left
10:18:44 AM 5.443839 11.53905 5.999981 14.4998 10:18:44 AM -109.651 -128.393 Left
10:18:45 AM 5.422859 11.59307 5.999981 14.49996 10:18:45 AM -109.555 -129.139 Left
10:18:46 AM 5.40688 11.63516 5.999981 14.49997 10:18:46 AM #NAME? #NAME? Mid
10:18:47 AM 5.385821 11.69192 5.999981 14.49996 10:18:47 AM -108.106 -129.574 Left
10:18:48 AM 5.363208 11.74637 5.999981 14.49985 10:18:48 AM -110.058 -131.333 Left
10:18:49 AM 5.350856 11.78458 5.999981 14.4998 10:18:49 AM -108.528 -133.115 Left
10:18:51 AM 5.348593 11.83465 5.999981 14.49996 10:18:51 AM -111.234 -120.562 Left
10:18:52 AM 5.34094 11.87351 5.999981 14.49997 10:18:52 AM -109.552 -121.474 Left
10:18:53 AM 5.339965 11.87398 5.999981 14.49985 10:18:53 AM -110.263 -128.251 Left
10:18:54 AM 5.338418 11.87351 5.999981 14.49996 10:18:54 AM -110.1 -127.286 Left
10:18:55 AM 5.337015 11.87264 5.999981 14.49996 10:18:55 AM -111.259 -123.338 Left
10:18:56 AM 5.336518 11.87282 5.999981 14.4998 10:18:56 AM -112.944 -123.609 Left
10:18:57 AM 5.33497 11.87354 5.999981 14.4998 10:18:57 AM -111.497 -125.395 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
280
Single Non-Uniform Obstruction Sample 5
Motion Path Graph - Single Non-Uniform obstruction – Sample 5.
Combined logs – Single Non-Uniform obstruction – Sample 5.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.25.45.65.866.26.46.66.87
29-6-10-19
CARMI Child
X Z X Z L R
10:19:14 AM 6 9.5 6 14.5
10:19:15 AM 6 9.5 6 14.5
10:19:16 AM 6 9.5 6 14.5
10:19:18 AM 5.994834 9.500858 5.999981 14.4998
10:19:19 AM 6.005515 9.621281 5.999981 14.49997 10:19:19 AM -133.298 -112.951 Right
10:19:20 AM 6.006132 9.620813 5.999981 14.49996 10:19:20 AM -131.588 -112.189 Right
10:19:21 AM 6.00739 9.62138 5.999981 14.49996 10:19:21 AM -130.589 -112.424 Right
10:19:22 AM 6.017666 9.909372 5.999981 14.49996 10:19:22 AM -132.701 #NAME? Left
10:19:23 AM 6.029618 10.24524 5.999981 14.49985 10:19:23 AM -131.171 -114.231 Right
10:19:24 AM 6.042325 10.60209 5.999981 14.4998 10:19:24 AM -123.365 -113.751 Right
10:19:25 AM -115.057 -111.575 Right
10:19:26 AM 6.054657 10.94845 5.999981 14.49996 10:19:26 AM -111.461 -108.614 Right
10:19:27 AM 6.064 11.04171 5.999981 14.4998 10:19:27 AM -108.184 -110.313 Left
10:19:28 AM 6.07516 11.04531 5.999981 14.49997
10:19:29 AM 6.08661 11.05206 5.999981 14.49985 10:19:29 AM -110.842 -115.878 Left
10:19:30 AM 6.094765 11.06024 5.999981 14.4998 10:19:30 AM -110.859 -127.694 Left
10:19:31 AM 6.087481 11.07464 5.999981 14.49996 10:19:31 AM -111.013 -131.725 Left
10:19:32 AM 6.03866 11.10422 5.999981 14.4998 10:19:32 AM -111.532 -133.07 Left
10:19:33 AM 5.994674 11.12609 5.999981 14.49996 10:19:33 AM -112.063 -136.432 Left
10:19:35 AM 5.930947 11.14635 5.999981 14.49985 10:19:35 AM -113.141 -143.286 Left
10:19:36 AM 5.893939 11.15053 5.999981 14.4998 10:19:36 AM -110.257 -144.56 Left
10:19:37 AM 5.840425 11.15012 5.999981 14.49985 10:19:37 AM -110.728 -146.857 Left
10:19:38 AM 5.791776 11.15446 5.999981 14.49985 10:19:38 AM -107.782 -149.879 Left
10:19:39 AM -108.587 -144.693 Left
10:19:40 AM 5.741306 11.16485 5.999981 14.4998 10:19:40 AM -108.794 -139.969 Left
10:19:41 AM 5.692986 11.18245 5.999981 14.49996 10:19:41 AM -106.53 -140.074 Left
10:19:42 AM 5.649341 11.20973 5.999981 14.49996 10:19:42 AM -106.784 -136.214 Left
10:19:43 AM 5.609469 11.23792 5.999981 14.49997
10:19:44 AM 5.569406 11.27635 5.999981 14.49985 10:19:44 AM -106.668 -134.867 Left
10:19:45 AM 5.53048 11.32803 5.999981 14.49985 10:19:45 AM -107.426 -133.316 Left
10:19:46 AM 5.50492 11.36169 5.999981 14.49997 10:19:46 AM #NAME? #NAME? Mid
10:19:47 AM -112.825 -126.878 Left
10:19:48 AM 5.479592 11.41774 5.999981 14.49996 10:19:48 AM -112.049 -126.211 Left
10:19:49 AM 5.467192 11.46103 5.999981 14.4998 10:19:49 AM -107.627 -124.836 Left
10:19:50 AM 5.450711 11.51385 5.999981 14.49997 10:19:50 AM -107.473 -127.507 Left
10:19:51 AM 5.42677 11.56558 5.999981 14.49985 10:19:51 AM -109.242 -126.877 Left
10:19:52 AM 5.402543 11.61871 5.999981 14.49997 10:19:52 AM -108.853 -131.572 Left
10:19:53 AM 5.385859 11.65936 5.999981 14.4998 10:19:53 AM -108.143 -130.831 Left
10:19:54 AM 5.365652 11.7017 5.999981 14.49996 10:19:54 AM -109.436 -130.831 Left
10:19:55 AM 5.358759 11.73643 5.999981 14.49985
10:19:56 AM 5.347197 11.77669 5.999981 14.49996 10:19:56 AM -109.317 -126.808 Left
10:19:57 AM 5.336686 11.83338 5.999981 14.49996 10:19:57 AM -105.844 -129.257 Left
10:19:58 AM -111.388 -131.501 Left
10:19:59 AM 5.334013 11.8877 5.999981 14.49996 10:19:59 AM -111.453 -125.751 Left
10:20:00 AM 5.333127 11.92123 5.999981 14.49996 10:20:00 AM -109.896 -125.511 Left
10:20:01 AM 5.331847 11.92002 5.999981 14.49997 10:20:01 AM -111.903 -122.525 Left
10:20:02 AM 5.330778 11.92065 5.999981 14.49985 10:20:02 AM -111.511 -122.629 Left
10:20:03 AM 5.32952 11.92112 5.999981 14.49985 10:20:03 AM -113.588 -120.628 Left
10:20:04 AM 5.329747 11.92111 5.999981 14.49997 10:20:05 AM -115.97 -118.351 Left
10:20:05 AM 5.329026 11.92123 5.999981 14.49985 10:20:06 AM -115.939 -118.424 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
281
X Z X Z L R
10:19:14 AM 6 9.5 6 14.5
10:19:15 AM 6 9.5 6 14.5
10:19:16 AM 6 9.5 6 14.5
10:19:18 AM 5.994834 9.500858 5.999981 14.4998
10:19:19 AM 6.005515 9.621281 5.999981 14.49997 10:19:19 AM -133.298 -112.951 Right
10:19:20 AM 6.006132 9.620813 5.999981 14.49996 10:19:20 AM -131.588 -112.189 Right
10:19:21 AM 6.00739 9.62138 5.999981 14.49996 10:19:21 AM -130.589 -112.424 Right
10:19:22 AM 6.017666 9.909372 5.999981 14.49996 10:19:22 AM -132.701 #NAME? Left
10:19:23 AM 6.029618 10.24524 5.999981 14.49985 10:19:23 AM -131.171 -114.231 Right
10:19:24 AM 6.042325 10.60209 5.999981 14.4998 10:19:24 AM -123.365 -113.751 Right
10:19:25 AM -115.057 -111.575 Right
10:19:26 AM 6.054657 10.94845 5.999981 14.49996 10:19:26 AM -111.461 -108.614 Right
10:19:27 AM 6.064 11.04171 5.999981 14.4998 10:19:27 AM -108.184 -110.313 Left
10:19:28 AM 6.07516 11.04531 5.999981 14.49997
10:19:29 AM 6.08661 11.05206 5.999981 14.49985 10:19:29 AM -110.842 -115.878 Left
10:19:30 AM 6.094765 11.06024 5.999981 14.4998 10:19:30 AM -110.859 -127.694 Left
10:19:31 AM 6.087481 11.07464 5.999981 14.49996 10:19:31 AM -111.013 -131.725 Left
10:19:32 AM 6.03866 11.10422 5.999981 14.4998 10:19:32 AM -111.532 -133.07 Left
10:19:33 AM 5.994674 11.12609 5.999981 14.49996 10:19:33 AM -112.063 -136.432 Left
10:19:35 AM 5.930947 11.14635 5.999981 14.49985 10:19:35 AM -113.141 -143.286 Left
10:19:36 AM 5.893939 11.15053 5.999981 14.4998 10:19:36 AM -110.257 -144.56 Left
10:19:37 AM 5.840425 11.15012 5.999981 14.49985 10:19:37 AM -110.728 -146.857 Left
10:19:38 AM 5.791776 11.15446 5.999981 14.49985 10:19:38 AM -107.782 -149.879 Left
10:19:39 AM -108.587 -144.693 Left
10:19:40 AM 5.741306 11.16485 5.999981 14.4998 10:19:40 AM -108.794 -139.969 Left
10:19:41 AM 5.692986 11.18245 5.999981 14.49996 10:19:41 AM -106.53 -140.074 Left
10:19:42 AM 5.649341 11.20973 5.999981 14.49996 10:19:42 AM -106.784 -136.214 Left
10:19:43 AM 5.609469 11.23792 5.999981 14.49997
10:19:44 AM 5.569406 11.27635 5.999981 14.49985 10:19:44 AM -106.668 -134.867 Left
10:19:45 AM 5.53048 11.32803 5.999981 14.49985 10:19:45 AM -107.426 -133.316 Left
10:19:46 AM 5.50492 11.36169 5.999981 14.49997 10:19:46 AM #NAME? #NAME? Mid
10:19:47 AM -112.825 -126.878 Left
10:19:48 AM 5.479592 11.41774 5.999981 14.49996 10:19:48 AM -112.049 -126.211 Left
10:19:49 AM 5.467192 11.46103 5.999981 14.4998 10:19:49 AM -107.627 -124.836 Left
10:19:50 AM 5.450711 11.51385 5.999981 14.49997 10:19:50 AM -107.473 -127.507 Left
10:19:51 AM 5.42677 11.56558 5.999981 14.49985 10:19:51 AM -109.242 -126.877 Left
10:19:52 AM 5.402543 11.61871 5.999981 14.49997 10:19:52 AM -108.853 -131.572 Left
10:19:53 AM 5.385859 11.65936 5.999981 14.4998 10:19:53 AM -108.143 -130.831 Left
10:19:54 AM 5.365652 11.7017 5.999981 14.49996 10:19:54 AM -109.436 -130.831 Left
10:19:55 AM 5.358759 11.73643 5.999981 14.49985
10:19:56 AM 5.347197 11.77669 5.999981 14.49996 10:19:56 AM -109.317 -126.808 Left
10:19:57 AM 5.336686 11.83338 5.999981 14.49996 10:19:57 AM -105.844 -129.257 Left
10:19:58 AM -111.388 -131.501 Left
10:19:59 AM 5.334013 11.8877 5.999981 14.49996 10:19:59 AM -111.453 -125.751 Left
10:20:00 AM 5.333127 11.92123 5.999981 14.49996 10:20:00 AM -109.896 -125.511 Left
10:20:01 AM 5.331847 11.92002 5.999981 14.49997 10:20:01 AM -111.903 -122.525 Left
10:20:02 AM 5.330778 11.92065 5.999981 14.49985 10:20:02 AM -111.511 -122.629 Left
10:20:03 AM 5.32952 11.92112 5.999981 14.49985 10:20:03 AM -113.588 -120.628 Left
10:20:04 AM 5.329747 11.92111 5.999981 14.49997 10:20:05 AM -115.97 -118.351 Left
10:20:05 AM 5.329026 11.92123 5.999981 14.49985 10:20:06 AM -115.939 -118.424 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
282
Non-Uniform Obstruction with Left Scatter Sample 1
Motion Path Graph - Non-Uniform obstruction with Left Scatter – Sample 1.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.57
29-6-10-25
CARMI Child
283
Combined logs – Non-Uniform obstruction with Left Scatter – Sample 1.
X Z X Z L R
10:25:03 AM 6 9.5 6 14.5
10:25:04 AM 6 9.5 6 14.5
10:25:05 AM 6 9.5 6 14.5
10:25:06 AM 5.995187 9.501067 6.000019 14.49996
10:25:07 AM 5.987306 9.485507 6.000019 14.4998
10:25:08 AM 5.970524 9.462602 6.000019 14.49997
10:25:10 AM 5.939333 9.420695 6.000019 14.4998
10:25:11 AM 5.91065 9.380818 6.000019 14.49996
10:25:12 AM 5.86494 9.347048 6.000019 14.49985
10:25:13 AM 5.80814 9.307255 6.000019 14.4998
10:25:14 AM 5.771358 9.301133 6.000019 14.49997
10:25:15 AM 5.74946 9.292454 6.000019 14.49996 10:25:15 AM -151.439 -115.071 Right
10:25:16 AM 5.792623 9.304709 6.000019 14.49996 10:25:16 AM -148.92 -111.255 Right
10:25:17 AM 5.839953 9.324904 6.000019 14.49996
10:25:18 AM 5.876538 9.342795 6.000019 14.49985 10:25:18 AM -143.523 -116.122 Right
10:25:19 AM 5.920836 9.373268 6.000019 14.4998 10:25:19 AM -143.865 -116.753 Right
10:25:20 AM 5.956855 9.401843 6.000019 14.4998 10:25:20 AM -145.216 -113.429 Right
10:25:21 AM 5.984492 9.431147 6.000019 14.49996 10:25:21 AM -141.544 -111.869 Right
10:25:22 AM -140.688 -112.214 Right
10:25:23 AM 6.016403 9.478471 6.000019 14.49996
10:25:24 AM 6.038756 9.539188 6.000019 14.4998 10:25:24 AM -140.277 -112.204 Right
10:25:25 AM 6.057558 9.596663 6.000019 14.49997 10:25:25 AM -136.584 -112.203 Right
10:25:26 AM 6.065561 9.649698 6.000019 14.49996 10:25:26 AM -134.498 -110.22 Right
10:25:27 AM 6.06628 9.650797 6.000019 14.4998 10:25:27 AM -133.348 -111.044 Right
10:25:28 AM 6.065044 9.650555 6.000019 14.49997 10:25:28 AM -134.632 -119.773 Right
10:25:29 AM 6.067335 9.771054 6.000019 14.49996 10:25:29 AM -137.994 -111.852 Right
10:25:31 AM 6.067936 9.771342 6.000019 14.49985 10:25:31 AM -135.837 -112.944 Right
10:25:32 AM 6.068365 9.842364 6.000019 14.49997 10:25:32 AM -134.666 -114.553 Right
10:25:33 AM 6.068262 10.22044 6.000019 14.49997 10:25:33 AM -138.822 -115.072 Right
10:25:34 AM 6.068174 10.54075 6.000019 14.49985 10:25:34 AM -136.755 -114.54 Right
10:25:35 AM 6.068077 10.89782 6.000019 14.49985 10:25:35 AM -129.916 -112.009 Right
10:25:36 AM 6.078161 11.04485 6.000019 14.49996 10:25:36 AM -126.484 -112.288 Right
10:25:37 AM 6.085254 11.04809 6.000019 14.4998 10:25:37 AM -110.247 -109.924 Right
10:25:38 AM 6.091435 11.05302 6.000019 14.49996 10:25:38 AM -109.28 -121.522 Left
10:25:39 AM -110.316 -124.628 Left
10:25:40 AM 6.103316 11.06274 6.000019 14.49985 10:25:40 AM -110.355 -124.468 Left
10:25:41 AM 6.107357 11.06751 6.000019 14.49996 10:25:41 AM -106.815 -107.2 Left
10:25:42 AM 6.090384 11.07509 6.000019 14.49985 10:25:42 AM -107.014 -107.66 Left
10:25:43 AM 6.085586 11.06713 6.000019 14.4998
10:25:44 AM 6.078386 11.06144 6.000019 14.4998 10:25:44 AM -110.973 -107.608 Right
10:25:45 AM 6.069264 11.05665 6.000019 14.49997 10:25:45 AM -111.599 -107.683 Right
10:25:46 AM 6.074354 11.03458 6.000019 14.49996 10:25:46 AM -107.894 -112.265 Left
10:25:47 AM 6.09642 10.98835 6.000019 14.4998 10:25:47 AM -114.033 -118.694 Left
10:25:48 AM 6.119619 10.94732 6.000019 14.49997 10:25:48 AM -110.08 -119.56 Left
10:25:49 AM -109.253 -119.629 Left
10:25:50 AM 6.13285 10.94548 6.000019 14.49997 10:25:50 AM -110.986 -123.237 Left
10:25:51 AM 6.098702 10.98643 6.000019 14.4998 10:25:51 AM -109.596 -127.516 Left
10:25:52 AM 6.058999 11.02136 6.000019 14.49985 10:25:52 AM -110.28 -132.313 Left
10:25:53 AM 6.028584 11.04479 6.000019 14.49997 10:25:53 AM -110.774 -134.306 Left
10:25:54 AM 5.978067 11.07056 6.000019 14.49997 10:25:54 AM -110.927 -136.701 Left
10:25:55 AM 5.937562 11.09239 6.000019 14.49985 10:25:55 AM -111.41 -140.266 Left
10:25:56 AM 5.89775 11.10438 6.000019 14.49997 10:25:56 AM -109.803 -145.509 Left
10:25:57 AM 5.841621 11.11016 6.000019 14.49985
10:25:58 AM 5.802022 11.11083 6.000019 14.49985 10:25:58 AM -111 -147.54 Left
10:25:59 AM -108.824 -146.444 Left
10:26:00 AM 5.745181 11.11665 6.000019 14.49985 10:26:00 AM -109.196 -143.629 Left
10:26:01 AM 5.686121 11.13708 6.000019 14.4998 10:26:01 AM -106.548 -144.468 Left
10:26:02 AM 5.6387 11.15968 6.000019 14.4998 10:26:02 AM -106.622 -139.037 Left
10:26:03 AM 5.598574 11.18115 6.000019 14.49997 10:26:03 AM -106.313 -136.889 Left
10:26:04 AM 5.555991 11.21827 6.000019 14.4998 10:26:04 AM -106.821 -135.055 Left
10:26:05 AM 5.525547 11.24757 6.000019 14.49996 10:26:05 AM -107.028 -133.555 Left
10:26:06 AM 5.496115 11.29134 6.000019 14.49985 10:26:06 AM -111.213 -131.152 Left
10:26:07 AM 5.475601 11.34074 6.000019 14.49997 10:26:07 AM -108.231 -121.882 Left
10:26:08 AM 5.453451 11.38008 6.000019 14.4998 10:26:08 AM -111.383 -128.165 Left
10:26:09 AM 5.4383 11.43201 6.000019 14.49996 10:26:09 AM -107.701 -127.036 Left
10:26:10 AM 5.415946 11.48621 6.000019 14.49997 10:26:10 AM -107.315 -127.025 Left
10:26:11 AM -109.429 -125.322 Left
10:26:12 AM 5.39477 11.54291 6.000019 14.49985 10:26:12 AM -109.724 -130.219 Left
10:26:13 AM 5.371273 11.60596 6.000019 14.49997
10:26:14 AM 5.356312 11.63996 6.000019 14.49996 10:26:14 AM -108.485 -130.413 Left
10:26:15 AM 5.33979 11.67916 6.000019 14.49985 10:26:15 AM -109.427 -131.244 Left
10:26:16 AM 5.324664 11.719 6.000019 14.49997 10:26:16 AM -108.508 -128.93 Left
10:26:17 AM 5.311745 11.76943 6.000019 14.49997 10:26:17 AM -108.462 -133.452 Left
10:26:18 AM 5.306375 11.81803 6.000019 14.49997 10:26:18 AM -110.356 -128.779 Left
10:26:19 AM 5.301338 11.86718 6.000019 14.4998 10:26:19 AM -110.715 -123.74 Left
10:26:20 AM 5.306932 11.92381 6.000019 14.4998 10:26:20 AM -109.361 -129.663 Left
10:26:21 AM -109.629 -126.896 Left
10:26:22 AM 5.307473 11.94227 6.000019 14.4998 10:26:22 AM -114.044 -120.074 Left
10:26:23 AM 5.308829 11.94139 6.000019 14.49997 10:26:23 AM -111.106 -119.55 Left
10:26:24 AM 5.308735 11.94078 6.000019 14.49996 10:26:24 AM -110.797 -122.485 Left
10:26:25 AM 5.308735 11.94078 6.000019 14.49996 10:26:25 AM -112.035 -121.291 Left
10:26:26 AM 5.308735 11.94078 6.000019 14.49985
10:26:27 AM -110.459 -122.867 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
284
X Z X Z L R
10:25:03 AM 6 9.5 6 14.5
10:25:04 AM 6 9.5 6 14.5
10:25:05 AM 6 9.5 6 14.5
10:25:06 AM 5.995187 9.501067 6.000019 14.49996
10:25:07 AM 5.987306 9.485507 6.000019 14.4998
10:25:08 AM 5.970524 9.462602 6.000019 14.49997
10:25:10 AM 5.939333 9.420695 6.000019 14.4998
10:25:11 AM 5.91065 9.380818 6.000019 14.49996
10:25:12 AM 5.86494 9.347048 6.000019 14.49985
10:25:13 AM 5.80814 9.307255 6.000019 14.4998
10:25:14 AM 5.771358 9.301133 6.000019 14.49997
10:25:15 AM 5.74946 9.292454 6.000019 14.49996 10:25:15 AM -151.439 -115.071 Right
10:25:16 AM 5.792623 9.304709 6.000019 14.49996 10:25:16 AM -148.92 -111.255 Right
10:25:17 AM 5.839953 9.324904 6.000019 14.49996
10:25:18 AM 5.876538 9.342795 6.000019 14.49985 10:25:18 AM -143.523 -116.122 Right
10:25:19 AM 5.920836 9.373268 6.000019 14.4998 10:25:19 AM -143.865 -116.753 Right
10:25:20 AM 5.956855 9.401843 6.000019 14.4998 10:25:20 AM -145.216 -113.429 Right
10:25:21 AM 5.984492 9.431147 6.000019 14.49996 10:25:21 AM -141.544 -111.869 Right
10:25:22 AM -140.688 -112.214 Right
10:25:23 AM 6.016403 9.478471 6.000019 14.49996
10:25:24 AM 6.038756 9.539188 6.000019 14.4998 10:25:24 AM -140.277 -112.204 Right
10:25:25 AM 6.057558 9.596663 6.000019 14.49997 10:25:25 AM -136.584 -112.203 Right
10:25:26 AM 6.065561 9.649698 6.000019 14.49996 10:25:26 AM -134.498 -110.22 Right
10:25:27 AM 6.06628 9.650797 6.000019 14.4998 10:25:27 AM -133.348 -111.044 Right
10:25:28 AM 6.065044 9.650555 6.000019 14.49997 10:25:28 AM -134.632 -119.773 Right
10:25:29 AM 6.067335 9.771054 6.000019 14.49996 10:25:29 AM -137.994 -111.852 Right
10:25:31 AM 6.067936 9.771342 6.000019 14.49985 10:25:31 AM -135.837 -112.944 Right
10:25:32 AM 6.068365 9.842364 6.000019 14.49997 10:25:32 AM -134.666 -114.553 Right
10:25:33 AM 6.068262 10.22044 6.000019 14.49997 10:25:33 AM -138.822 -115.072 Right
10:25:34 AM 6.068174 10.54075 6.000019 14.49985 10:25:34 AM -136.755 -114.54 Right
10:25:35 AM 6.068077 10.89782 6.000019 14.49985 10:25:35 AM -129.916 -112.009 Right
10:25:36 AM 6.078161 11.04485 6.000019 14.49996 10:25:36 AM -126.484 -112.288 Right
10:25:37 AM 6.085254 11.04809 6.000019 14.4998 10:25:37 AM -110.247 -109.924 Right
10:25:38 AM 6.091435 11.05302 6.000019 14.49996 10:25:38 AM -109.28 -121.522 Left
10:25:39 AM -110.316 -124.628 Left
10:25:40 AM 6.103316 11.06274 6.000019 14.49985 10:25:40 AM -110.355 -124.468 Left
10:25:41 AM 6.107357 11.06751 6.000019 14.49996 10:25:41 AM -106.815 -107.2 Left
10:25:42 AM 6.090384 11.07509 6.000019 14.49985 10:25:42 AM -107.014 -107.66 Left
10:25:43 AM 6.085586 11.06713 6.000019 14.4998
10:25:44 AM 6.078386 11.06144 6.000019 14.4998 10:25:44 AM -110.973 -107.608 Right
10:25:45 AM 6.069264 11.05665 6.000019 14.49997 10:25:45 AM -111.599 -107.683 Right
10:25:46 AM 6.074354 11.03458 6.000019 14.49996 10:25:46 AM -107.894 -112.265 Left
10:25:47 AM 6.09642 10.98835 6.000019 14.4998 10:25:47 AM -114.033 -118.694 Left
10:25:48 AM 6.119619 10.94732 6.000019 14.49997 10:25:48 AM -110.08 -119.56 Left
10:25:49 AM -109.253 -119.629 Left
10:25:50 AM 6.13285 10.94548 6.000019 14.49997 10:25:50 AM -110.986 -123.237 Left
10:25:51 AM 6.098702 10.98643 6.000019 14.4998 10:25:51 AM -109.596 -127.516 Left
10:25:52 AM 6.058999 11.02136 6.000019 14.49985 10:25:52 AM -110.28 -132.313 Left
10:25:53 AM 6.028584 11.04479 6.000019 14.49997 10:25:53 AM -110.774 -134.306 Left
10:25:54 AM 5.978067 11.07056 6.000019 14.49997 10:25:54 AM -110.927 -136.701 Left
10:25:55 AM 5.937562 11.09239 6.000019 14.49985 10:25:55 AM -111.41 -140.266 Left
10:25:56 AM 5.89775 11.10438 6.000019 14.49997 10:25:56 AM -109.803 -145.509 Left
10:25:57 AM 5.841621 11.11016 6.000019 14.49985
10:25:58 AM 5.802022 11.11083 6.000019 14.49985 10:25:58 AM -111 -147.54 Left
10:25:59 AM -108.824 -146.444 Left
10:26:00 AM 5.745181 11.11665 6.000019 14.49985 10:26:00 AM -109.196 -143.629 Left
10:26:01 AM 5.686121 11.13708 6.000019 14.4998 10:26:01 AM -106.548 -144.468 Left
10:26:02 AM 5.6387 11.15968 6.000019 14.4998 10:26:02 AM -106.622 -139.037 Left
10:26:03 AM 5.598574 11.18115 6.000019 14.49997 10:26:03 AM -106.313 -136.889 Left
10:26:04 AM 5.555991 11.21827 6.000019 14.4998 10:26:04 AM -106.821 -135.055 Left
10:26:05 AM 5.525547 11.24757 6.000019 14.49996 10:26:05 AM -107.028 -133.555 Left
10:26:06 AM 5.496115 11.29134 6.000019 14.49985 10:26:06 AM -111.213 -131.152 Left
10:26:07 AM 5.475601 11.34074 6.000019 14.49997 10:26:07 AM -108.231 -121.882 Left
10:26:08 AM 5.453451 11.38008 6.000019 14.4998 10:26:08 AM -111.383 -128.165 Left
10:26:09 AM 5.4383 11.43201 6.000019 14.49996 10:26:09 AM -107.701 -127.036 Left
10:26:10 AM 5.415946 11.48621 6.000019 14.49997 10:26:10 AM -107.315 -127.025 Left
10:26:11 AM -109.429 -125.322 Left
10:26:12 AM 5.39477 11.54291 6.000019 14.49985 10:26:12 AM -109.724 -130.219 Left
10:26:13 AM 5.371273 11.60596 6.000019 14.49997
10:26:14 AM 5.356312 11.63996 6.000019 14.49996 10:26:14 AM -108.485 -130.413 Left
10:26:15 AM 5.33979 11.67916 6.000019 14.49985 10:26:15 AM -109.427 -131.244 Left
10:26:16 AM 5.324664 11.719 6.000019 14.49997 10:26:16 AM -108.508 -128.93 Left
10:26:17 AM 5.311745 11.76943 6.000019 14.49997 10:26:17 AM -108.462 -133.452 Left
10:26:18 AM 5.306375 11.81803 6.000019 14.49997 10:26:18 AM -110.356 -128.779 Left
10:26:19 AM 5.301338 11.86718 6.000019 14.4998 10:26:19 AM -110.715 -123.74 Left
10:26:20 AM 5.306932 11.92381 6.000019 14.4998 10:26:20 AM -109.361 -129.663 Left
10:26:21 AM -109.629 -126.896 Left
10:26:22 AM 5.307473 11.94227 6.000019 14.4998 10:26:22 AM -114.044 -120.074 Left
10:26:23 AM 5.308829 11.94139 6.000019 14.49997 10:26:23 AM -111.106 -119.55 Left
10:26:24 AM 5.308735 11.94078 6.000019 14.49996 10:26:24 AM -110.797 -122.485 Left
10:26:25 AM 5.308735 11.94078 6.000019 14.49996 10:26:25 AM -112.035 -121.291 Left
10:26:26 AM 5.308735 11.94078 6.000019 14.49985
10:26:27 AM -110.459 -122.867 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
285
Non-Uniform Obstruction with Left Scatter Sample 2
Motion Path Graph - Non-Uniform obstruction with Left Scatter – Sample 2.
Combined logs – Non-Uniform obstruction with Left Scatter – Sample 2.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.25.45.65.866.26.46.66.87
29-6-10-26
CARMI Child
X Z X Z L R
10:26:35 AM 6 9.5 6 14.5
10:26:36 AM 6 9.5 6 14.5
10:26:37 AM 6 9.5 6 14.5
10:26:38 AM 6.000052 9.500901 6.000019 14.49996
10:26:40 AM 5.993961 9.501074 6.000019 14.4998 10:26:40 AM -134.064 -116.402 Right
10:26:41 AM 6.011438 9.621527 6.000019 14.49996 10:26:41 AM -137.523 -110.395 Right
10:26:42 AM 6.012232 9.6207 6.000019 14.49997 10:26:42 AM -134.233 -112.492 Right
10:26:43 AM 6.012931 9.621051 6.000019 14.49996 10:26:43 AM -135.923 -111.547 Right
10:26:44 AM 6.013783 9.620283 6.000019 14.4998 10:26:44 AM -134.181 -112.518 Right
10:26:45 AM 6.014473 9.621649 6.000019 14.4998
10:26:46 AM 6.015044 9.679293 6.000019 14.49996 10:26:46 AM -134.205 -111.837 Right
10:26:47 AM -133.27 -112.384 Right
10:26:48 AM 6.015076 10.02583 6.000019 14.49997 10:26:48 AM -138.066 -113.3 Right
10:26:49 AM 6.015108 10.37238 6.000019 14.49985 10:26:49 AM -137.388 -113.761 Right
10:26:50 AM 6.01514 10.70842 6.000019 14.49985 10:26:50 AM -130.543 -111.832 Right
10:26:51 AM 6.015638 11.04551 6.000019 14.49985 10:26:51 AM -121.811 -109.251 Right
10:26:52 AM 6.03046 11.04189 6.000019 14.49996 10:26:52 AM -110.65 -108.992 Right
10:26:53 AM 6.041857 11.04336 6.000019 14.4998 10:26:53 AM -110.405 -118.983 Left
10:26:54 AM 6.053732 11.04617 6.000019 14.4998 10:26:54 AM -109.191 -125.626 Left
10:26:55 AM -110.817 -127.316 Left
10:26:56 AM 6.058239 11.05242 6.000019 14.49997 10:26:56 AM -111.159 -131.129 Left
10:26:57 AM 6.028661 11.07939 6.000019 14.49996
10:26:58 AM 5.990767 11.10788 6.000019 14.49985 10:26:58 AM -111.934 -134.315 Left
10:26:59 AM 5.93907 11.13029 6.000019 14.49985 10:26:59 AM -112.403 -137.93 Left
10:27:00 AM 5.897036 11.14688 6.000019 14.49996 10:27:00 AM -113.38 -141.15 Left
10:27:01 AM 5.837491 11.15529 6.000019 14.4998 10:27:01 AM -110.254 -143.621 Left
10:27:02 AM -110.873 -145.801 Left
10:27:04 AM 5.776953 11.16374 6.000019 14.49985 10:27:04 AM #NAME? #NAME? Mid
10:27:05 AM 5.726399 11.17293 6.000019 14.4998
10:27:06 AM 5.680781 11.18738 6.000019 14.49985 10:27:06 AM -109.413 -139.856 Left
10:27:07 AM 5.632584 11.21202 6.000019 14.4998 10:27:07 AM -106.452 -139.859 Left
10:27:08 AM 5.598484 11.23532 6.000019 14.49996 10:27:08 AM -106.64 -137.584 Left
10:27:09 AM 5.555641 11.27266 6.000019 14.49985 10:27:09 AM -107.584 -135.636 Left
10:27:10 AM 5.523552 11.30593 6.000019 14.49985 10:27:10 AM -107.971 -133.679 Left
10:27:11 AM 5.495688 11.34421 6.000019 14.49997 10:27:11 AM -107.867 -129.107 Left
10:27:12 AM 5.47869 11.38482 6.000019 14.4998 10:27:12 AM -107.227 -126.307 Left
10:27:13 AM 5.460893 11.43135 6.000019 14.49996 10:27:13 AM -107.853 -127.133 Left
10:27:14 AM -107.379 -126.857 Left
10:27:15 AM 5.443073 11.47096 6.000019 14.49985 10:27:15 AM -107.234 -126.9 Left
10:27:16 AM 5.423987 11.51841 6.000019 14.49996 10:27:16 AM -107.796 -127.278 Left
10:27:17 AM 5.411617 11.55918 6.000019 14.4998
10:27:18 AM 5.389745 11.61686 6.000019 14.49985 10:27:18 AM -109.904 -129.671 Left
10:27:19 AM 5.374179 11.66 6.000019 14.4998 10:27:19 AM -108.863 -128.983 Left
10:27:20 AM 5.352884 11.69341 6.000019 14.49997 10:27:20 AM -108.936 -131.065 Left
10:27:21 AM 5.334223 11.74571 6.000019 14.49997 10:27:21 AM -109.879 -130.404 Left
10:27:22 AM 5.320929 11.78638 6.000019 14.49996 10:27:22 AM -110.86 -129.563 Left
10:27:23 AM 5.313086 11.84917 6.000019 14.49985 10:27:23 AM -109.63 -127.733 Left
10:27:24 AM 5.309034 11.89369 6.000019 14.49997 10:27:24 AM -108.02 -126.813 Left
10:27:25 AM 5.3077 11.91376 6.000019 14.49985 10:27:25 AM -109.424 -125.642 Left
10:27:26 AM -111.797 -120.599 Left
10:27:27 AM 5.306705 11.91388 6.000019 14.4998
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
286
X Z X Z L R
10:26:35 AM 6 9.5 6 14.5
10:26:36 AM 6 9.5 6 14.5
10:26:37 AM 6 9.5 6 14.5
10:26:38 AM 6.000052 9.500901 6.000019 14.49996
10:26:40 AM 5.993961 9.501074 6.000019 14.4998 10:26:40 AM -134.064 -116.402 Right
10:26:41 AM 6.011438 9.621527 6.000019 14.49996 10:26:41 AM -137.523 -110.395 Right
10:26:42 AM 6.012232 9.6207 6.000019 14.49997 10:26:42 AM -134.233 -112.492 Right
10:26:43 AM 6.012931 9.621051 6.000019 14.49996 10:26:43 AM -135.923 -111.547 Right
10:26:44 AM 6.013783 9.620283 6.000019 14.4998 10:26:44 AM -134.181 -112.518 Right
10:26:45 AM 6.014473 9.621649 6.000019 14.4998
10:26:46 AM 6.015044 9.679293 6.000019 14.49996 10:26:46 AM -134.205 -111.837 Right
10:26:47 AM -133.27 -112.384 Right
10:26:48 AM 6.015076 10.02583 6.000019 14.49997 10:26:48 AM -138.066 -113.3 Right
10:26:49 AM 6.015108 10.37238 6.000019 14.49985 10:26:49 AM -137.388 -113.761 Right
10:26:50 AM 6.01514 10.70842 6.000019 14.49985 10:26:50 AM -130.543 -111.832 Right
10:26:51 AM 6.015638 11.04551 6.000019 14.49985 10:26:51 AM -121.811 -109.251 Right
10:26:52 AM 6.03046 11.04189 6.000019 14.49996 10:26:52 AM -110.65 -108.992 Right
10:26:53 AM 6.041857 11.04336 6.000019 14.4998 10:26:53 AM -110.405 -118.983 Left
10:26:54 AM 6.053732 11.04617 6.000019 14.4998 10:26:54 AM -109.191 -125.626 Left
10:26:55 AM -110.817 -127.316 Left
10:26:56 AM 6.058239 11.05242 6.000019 14.49997 10:26:56 AM -111.159 -131.129 Left
10:26:57 AM 6.028661 11.07939 6.000019 14.49996
10:26:58 AM 5.990767 11.10788 6.000019 14.49985 10:26:58 AM -111.934 -134.315 Left
10:26:59 AM 5.93907 11.13029 6.000019 14.49985 10:26:59 AM -112.403 -137.93 Left
10:27:00 AM 5.897036 11.14688 6.000019 14.49996 10:27:00 AM -113.38 -141.15 Left
10:27:01 AM 5.837491 11.15529 6.000019 14.4998 10:27:01 AM -110.254 -143.621 Left
10:27:02 AM -110.873 -145.801 Left
10:27:04 AM 5.776953 11.16374 6.000019 14.49985 10:27:04 AM #NAME? #NAME? Mid
10:27:05 AM 5.726399 11.17293 6.000019 14.4998
10:27:06 AM 5.680781 11.18738 6.000019 14.49985 10:27:06 AM -109.413 -139.856 Left
10:27:07 AM 5.632584 11.21202 6.000019 14.4998 10:27:07 AM -106.452 -139.859 Left
10:27:08 AM 5.598484 11.23532 6.000019 14.49996 10:27:08 AM -106.64 -137.584 Left
10:27:09 AM 5.555641 11.27266 6.000019 14.49985 10:27:09 AM -107.584 -135.636 Left
10:27:10 AM 5.523552 11.30593 6.000019 14.49985 10:27:10 AM -107.971 -133.679 Left
10:27:11 AM 5.495688 11.34421 6.000019 14.49997 10:27:11 AM -107.867 -129.107 Left
10:27:12 AM 5.47869 11.38482 6.000019 14.4998 10:27:12 AM -107.227 -126.307 Left
10:27:13 AM 5.460893 11.43135 6.000019 14.49996 10:27:13 AM -107.853 -127.133 Left
10:27:14 AM -107.379 -126.857 Left
10:27:15 AM 5.443073 11.47096 6.000019 14.49985 10:27:15 AM -107.234 -126.9 Left
10:27:16 AM 5.423987 11.51841 6.000019 14.49996 10:27:16 AM -107.796 -127.278 Left
10:27:17 AM 5.411617 11.55918 6.000019 14.4998
10:27:18 AM 5.389745 11.61686 6.000019 14.49985 10:27:18 AM -109.904 -129.671 Left
10:27:19 AM 5.374179 11.66 6.000019 14.4998 10:27:19 AM -108.863 -128.983 Left
10:27:20 AM 5.352884 11.69341 6.000019 14.49997 10:27:20 AM -108.936 -131.065 Left
10:27:21 AM 5.334223 11.74571 6.000019 14.49997 10:27:21 AM -109.879 -130.404 Left
10:27:22 AM 5.320929 11.78638 6.000019 14.49996 10:27:22 AM -110.86 -129.563 Left
10:27:23 AM 5.313086 11.84917 6.000019 14.49985 10:27:23 AM -109.63 -127.733 Left
10:27:24 AM 5.309034 11.89369 6.000019 14.49997 10:27:24 AM -108.02 -126.813 Left
10:27:25 AM 5.3077 11.91376 6.000019 14.49985 10:27:25 AM -109.424 -125.642 Left
10:27:26 AM -111.797 -120.599 Left
10:27:27 AM 5.306705 11.91388 6.000019 14.4998
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
287
Non-Uniform Obstruction with Left Scatter Sample 3
Motion Path Graph - Non-Uniform obstruction with Left Scatter – Sample 3.
Combined logs – Non-Uniform obstruction with Left Scatter – Sample 3.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.25.45.65.866.26.46.66.87
29-6-10-27
CARMI Child
X Z X Z L R
10:27:51 AM 6 9.5 6 14.5
10:27:52 AM 6 9.5 6 14.5
10:27:53 AM 6 9.5 6 14.5
10:27:54 AM 5.998454 9.500811 6.000019 14.49997
10:27:56 AM 5.998494 9.537737 6.000019 14.49997 10:27:56 AM -135.239 -116.065 Right
10:27:57 AM 6.017342 9.684696 6.000019 14.49996 10:27:57 AM -138.108 -110.325 Right
10:27:58 AM 6.018397 9.684046 6.000019 14.49985 10:27:58 AM -135.403 -111.447 Right
10:27:59 AM 6.019402 9.684674 6.000019 14.49997 10:27:59 AM -134.304 -113.401 Right
10:28:00 AM 6.026766 9.894994 6.000019 14.49996 10:28:00 AM -138.557 -113.6 Right
10:28:01 AM 6.038336 10.23513 6.000019 14.49985 10:28:01 AM -138.829 -114.629 Right
10:28:02 AM 6.049934 10.57411 6.000019 14.4998
10:28:03 AM 6.061596 10.91259 6.000019 14.49997 10:28:03 AM -134.814 -113.604 Right
10:28:04 AM -127.473 -110.742 Right
10:28:05 AM 6.070399 11.03259 6.000019 14.4998 10:28:05 AM -118.461 -109.984 Right
10:28:06 AM 6.078525 11.03496 6.000019 14.49997 10:28:06 AM -108.59 -111.097 Left
10:28:07 AM 6.086283 11.03974 6.000019 14.49997 10:28:07 AM -108.445 -118.76 Left
10:28:08 AM 6.092246 11.04339 6.000019 14.49996 10:28:08 AM -109.271 -122.474 Left
10:28:09 AM 6.097696 11.04694 6.000019 14.49997 10:28:09 AM -109.377 -122.747 Left
10:28:10 AM 6.110958 11.05076 6.000019 14.49997 10:28:10 AM -110.773 -125.34 Left
10:28:11 AM 6.073308 11.07943 6.000019 14.49985 10:28:11 AM -110.723 -132.627 Left
10:28:12 AM 6.031433 11.10486 6.000019 14.49996 10:28:12 AM -111.582 -134.965 Left
10:28:13 AM -112.039 -136.44 Left
10:28:14 AM 5.99231 11.12788 6.000019 14.49985
10:28:15 AM 5.950308 11.14446 6.000019 14.49997 10:28:15 AM -112.727 -140.56 Left
10:28:16 AM 5.885548 11.15042 6.000019 14.4998 10:28:16 AM -110.45 -146.169 Left
10:28:17 AM 5.836405 11.14352 6.000019 14.49996 10:28:17 AM -110.598 -149.71 Left
10:28:18 AM 5.784748 11.14578 6.000019 14.4998 10:28:18 AM -108.616 -145.864 Left
10:28:19 AM 5.738927 11.15322 6.000019 14.49996 10:28:19 AM -107.227 -143.446 Left
10:28:20 AM 5.692919 11.17212 6.000019 14.49997 10:28:20 AM -106.942 -141.51 Left
10:28:21 AM 5.650949 11.19277 6.000019 14.4998 10:28:21 AM -108.467 -137.858 Left
10:28:22 AM 5.607926 11.2202 6.000019 14.49996 10:28:22 AM -106.584 -138.086 Left
10:28:23 AM -109.344 -134.811 Left
10:28:24 AM 5.558819 11.26068 6.000019 14.49985 10:28:24 AM -108.263 -131.44 Left
10:28:25 AM 5.524682 11.2997 6.000019 14.49996 10:28:25 AM -107.905 -129.079 Left
10:28:26 AM 5.502385 11.33383 6.000019 14.4998
10:28:27 AM 5.488326 11.36887 6.000019 14.49996 10:28:27 AM -106.935 -125.873 Left
10:28:28 AM 5.469213 11.43046 6.000019 14.49997 10:28:28 AM -110.018 -125.943 Left
10:28:29 AM 5.447887 11.46063 6.000019 14.4998 10:28:29 AM -107.83 -128.344 Left
10:28:30 AM 5.427122 11.5077 6.000019 14.49997 10:28:30 AM -113.634 -121.826 Left
10:28:31 AM 5.395703 11.55596 6.000019 14.49985 10:28:31 AM -108.286 -130.209 Left
10:28:32 AM 5.380172 11.612 6.000019 14.49997 10:28:32 AM -110.945 -131.57 Left
10:28:33 AM -109.313 -129.094 Left
10:28:34 AM 5.358354 11.65795 6.000019 14.49985 10:28:34 AM -108.96 -130.161 Left
10:28:35 AM 5.337102 11.70815 6.000019 14.49996 10:28:35 AM -111.804 -131.204 Left
10:28:36 AM 5.32457 11.76071 6.000019 14.49985 10:28:36 AM -110.114 -128.007 Left
10:28:37 AM 5.309299 11.80063 6.000019 14.49997 10:28:37 AM -110.186 -131.025 Left
10:28:38 AM 5.304522 11.84407 6.000019 14.49997 10:28:38 AM -109.562 -126.029 Left
10:28:39 AM 5.305171 11.88791 6.000019 14.49985 10:28:39 AM -113.175 -121.476 Left
10:28:40 AM 5.304163 11.8889 6.000019 14.49997
10:28:41 AM 5.302428 11.88875 6.000019 14.49985 10:28:41 AM -111.232 -122.701 Left
10:28:42 AM 5.302111 11.89018 6.000019 14.49997 10:28:42 AM -111.554 -122.609 Left
10:28:43 AM 5.302137 11.89017 6.000019 14.49985 10:28:43 AM -112.03 -121.145 Left
10:28:44 AM 5.302223 11.89016 6.000019 14.4998 10:28:44 AM -113.275 -119.968 Left
10:28:45 AM -113.284 -119.785 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
288
X Z X Z L R
10:27:51 AM 6 9.5 6 14.5
10:27:52 AM 6 9.5 6 14.5
10:27:53 AM 6 9.5 6 14.5
10:27:54 AM 5.998454 9.500811 6.000019 14.49997
10:27:56 AM 5.998494 9.537737 6.000019 14.49997 10:27:56 AM -135.239 -116.065 Right
10:27:57 AM 6.017342 9.684696 6.000019 14.49996 10:27:57 AM -138.108 -110.325 Right
10:27:58 AM 6.018397 9.684046 6.000019 14.49985 10:27:58 AM -135.403 -111.447 Right
10:27:59 AM 6.019402 9.684674 6.000019 14.49997 10:27:59 AM -134.304 -113.401 Right
10:28:00 AM 6.026766 9.894994 6.000019 14.49996 10:28:00 AM -138.557 -113.6 Right
10:28:01 AM 6.038336 10.23513 6.000019 14.49985 10:28:01 AM -138.829 -114.629 Right
10:28:02 AM 6.049934 10.57411 6.000019 14.4998
10:28:03 AM 6.061596 10.91259 6.000019 14.49997 10:28:03 AM -134.814 -113.604 Right
10:28:04 AM -127.473 -110.742 Right
10:28:05 AM 6.070399 11.03259 6.000019 14.4998 10:28:05 AM -118.461 -109.984 Right
10:28:06 AM 6.078525 11.03496 6.000019 14.49997 10:28:06 AM -108.59 -111.097 Left
10:28:07 AM 6.086283 11.03974 6.000019 14.49997 10:28:07 AM -108.445 -118.76 Left
10:28:08 AM 6.092246 11.04339 6.000019 14.49996 10:28:08 AM -109.271 -122.474 Left
10:28:09 AM 6.097696 11.04694 6.000019 14.49997 10:28:09 AM -109.377 -122.747 Left
10:28:10 AM 6.110958 11.05076 6.000019 14.49997 10:28:10 AM -110.773 -125.34 Left
10:28:11 AM 6.073308 11.07943 6.000019 14.49985 10:28:11 AM -110.723 -132.627 Left
10:28:12 AM 6.031433 11.10486 6.000019 14.49996 10:28:12 AM -111.582 -134.965 Left
10:28:13 AM -112.039 -136.44 Left
10:28:14 AM 5.99231 11.12788 6.000019 14.49985
10:28:15 AM 5.950308 11.14446 6.000019 14.49997 10:28:15 AM -112.727 -140.56 Left
10:28:16 AM 5.885548 11.15042 6.000019 14.4998 10:28:16 AM -110.45 -146.169 Left
10:28:17 AM 5.836405 11.14352 6.000019 14.49996 10:28:17 AM -110.598 -149.71 Left
10:28:18 AM 5.784748 11.14578 6.000019 14.4998 10:28:18 AM -108.616 -145.864 Left
10:28:19 AM 5.738927 11.15322 6.000019 14.49996 10:28:19 AM -107.227 -143.446 Left
10:28:20 AM 5.692919 11.17212 6.000019 14.49997 10:28:20 AM -106.942 -141.51 Left
10:28:21 AM 5.650949 11.19277 6.000019 14.4998 10:28:21 AM -108.467 -137.858 Left
10:28:22 AM 5.607926 11.2202 6.000019 14.49996 10:28:22 AM -106.584 -138.086 Left
10:28:23 AM -109.344 -134.811 Left
10:28:24 AM 5.558819 11.26068 6.000019 14.49985 10:28:24 AM -108.263 -131.44 Left
10:28:25 AM 5.524682 11.2997 6.000019 14.49996 10:28:25 AM -107.905 -129.079 Left
10:28:26 AM 5.502385 11.33383 6.000019 14.4998
10:28:27 AM 5.488326 11.36887 6.000019 14.49996 10:28:27 AM -106.935 -125.873 Left
10:28:28 AM 5.469213 11.43046 6.000019 14.49997 10:28:28 AM -110.018 -125.943 Left
10:28:29 AM 5.447887 11.46063 6.000019 14.4998 10:28:29 AM -107.83 -128.344 Left
10:28:30 AM 5.427122 11.5077 6.000019 14.49997 10:28:30 AM -113.634 -121.826 Left
10:28:31 AM 5.395703 11.55596 6.000019 14.49985 10:28:31 AM -108.286 -130.209 Left
10:28:32 AM 5.380172 11.612 6.000019 14.49997 10:28:32 AM -110.945 -131.57 Left
10:28:33 AM -109.313 -129.094 Left
10:28:34 AM 5.358354 11.65795 6.000019 14.49985 10:28:34 AM -108.96 -130.161 Left
10:28:35 AM 5.337102 11.70815 6.000019 14.49996 10:28:35 AM -111.804 -131.204 Left
10:28:36 AM 5.32457 11.76071 6.000019 14.49985 10:28:36 AM -110.114 -128.007 Left
10:28:37 AM 5.309299 11.80063 6.000019 14.49997 10:28:37 AM -110.186 -131.025 Left
10:28:38 AM 5.304522 11.84407 6.000019 14.49997 10:28:38 AM -109.562 -126.029 Left
10:28:39 AM 5.305171 11.88791 6.000019 14.49985 10:28:39 AM -113.175 -121.476 Left
10:28:40 AM 5.304163 11.8889 6.000019 14.49997
10:28:41 AM 5.302428 11.88875 6.000019 14.49985 10:28:41 AM -111.232 -122.701 Left
10:28:42 AM 5.302111 11.89018 6.000019 14.49997 10:28:42 AM -111.554 -122.609 Left
10:28:43 AM 5.302137 11.89017 6.000019 14.49985 10:28:43 AM -112.03 -121.145 Left
10:28:44 AM 5.302223 11.89016 6.000019 14.4998 10:28:44 AM -113.275 -119.968 Left
10:28:45 AM -113.284 -119.785 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
289
Non-Uniform Obstruction with Left Scatter Sample 4
Motion Path Graph - Non-Uniform obstruction with Left Scatter – Sample 4.
Combined logs – Non-Uniform obstruction with Left Scatter – Sample 4.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.25.45.65.866.26.46.66.87
29-6-10-29
CARMI Child
X Z X Z L R
10:29:02 AM 6 9.5 6 14.5
10:29:03 AM 6 9.5 6 14.5
10:29:04 AM 6 9.5 6 14.5
10:29:06 AM 5.993899 9.500897 6.000019 14.49985
10:29:07 AM 6.001026 9.552586 6.000019 14.49997 10:29:07 AM -134.623 -117.155 Right
10:29:08 AM 6.001791 9.552605 6.000019 14.49997 10:29:08 AM -136.954 -111.392 Right
10:29:09 AM 6.002508 9.552366 6.000019 14.49996 10:29:09 AM -135.503 -111.929 Right
10:29:10 AM 6.003298 9.552692 6.000019 14.49985 10:29:10 AM -135.892 -111.842 Right
10:29:11 AM 6.00343 9.554021 6.000019 14.4998 10:29:11 AM -135.956 -111.422 Right
10:29:12 AM 6.004618 9.554567 6.000019 14.49997 10:29:12 AM -135.579 -111.32 Right
10:29:13 AM 6.005475 9.554563 6.000019 14.49996 10:29:13 AM -134.125 -111.692 Right
10:29:14 AM 6.006477 9.554731 6.000019 14.4998 10:29:14 AM -133.504 -111.174 Right
10:29:15 AM 6.013505 9.755704 6.000019 14.4998 10:29:15 AM -132.764 -113.254 Right
10:29:16 AM -138.87 -114.332 Right
10:29:17 AM 6.0254 10.10101 6.000019 14.49996 10:29:17 AM -138.776 -114.908 Right
10:29:18 AM 6.037297 10.44524 6.000019 14.4998 10:29:18 AM -133.915 -113.251 Right
10:29:19 AM 6.049037 10.78276 6.000019 14.4998 10:29:19 AM -126.403 -109.987 Right
10:29:20 AM 6.060687 11.03611 6.000019 14.49997
10:29:21 AM 6.074476 11.03667 6.000019 14.49985 10:29:21 AM -110.917 -110.393 Right
10:29:22 AM 6.086651 11.0353 6.000019 14.49997 10:29:22 AM -108.5 -115.974 Left
10:29:23 AM 6.108089 11.03732 6.000019 14.49997 10:29:23 AM -109.07 -121.546 Left
10:29:24 AM 6.112276 11.04187 6.000019 14.49997 10:29:24 AM -110.547 -124.69 Left
10:29:25 AM -110.488 -131.279 Left
10:29:26 AM 6.106983 11.05216 6.000019 14.49997 10:29:26 AM -110.847 -132.425 Left
10:29:27 AM 6.063187 11.08296 6.000019 14.49996 10:29:27 AM -111.246 -133.773 Left
10:29:28 AM 6.019441 11.11028 6.000019 14.49996 10:29:28 AM -112.018 -138.849 Left
10:29:29 AM 5.9763 11.12583 6.000019 14.4998 10:29:29 AM -112.37 -143.224 Left
10:29:30 AM 5.930936 11.13321 6.000019 14.4998 10:29:30 AM -109.969 -145.554 Left
10:29:31 AM 5.882591 11.12912 6.000019 14.4998 10:29:31 AM -109.882 -147.993 Left
10:29:32 AM 5.824316 11.12665 6.000019 14.49996 10:29:32 AM -106.531 -151.692 Left
10:29:33 AM 5.776063 11.13215 6.000019 14.49996
10:29:34 AM 5.734162 11.14516 6.000019 14.4998 10:29:34 AM -108.16 -143.569 Left
10:29:35 AM 5.691587 11.16363 6.000019 14.49997 10:29:35 AM -108.524 -138.639 Left
10:29:36 AM -106.406 -140.587 Left
10:29:37 AM 5.65205 11.18499 6.000019 14.49985 10:29:37 AM -108.121 -137.836 Left
10:29:38 AM 5.606458 11.2185 6.000019 14.49997 10:29:38 AM -106.897 -133.821 Left
10:29:39 AM 5.569025 11.25131 6.000019 14.49997 10:29:39 AM -106.656 -131.33 Left
10:29:40 AM 5.545821 11.27992 6.000019 14.4998 10:29:40 AM -108.524 -130.364 Left
10:29:41 AM 5.522609 11.3123 6.000019 14.49985
10:29:42 AM 5.508795 11.35174 6.000019 14.49997 10:29:42 AM -119.03 -129.514 Left
10:29:43 AM 5.496674 11.39544 6.000019 14.49985 10:29:43 AM -112.295 -122.14 Left
10:29:44 AM 5.473576 11.4423 6.000019 14.49997 10:29:44 AM -111.806 -126.558 Left
10:29:45 AM -110.352 -128.026 Left
10:29:46 AM 5.458086 11.48223 6.000019 14.49996 10:29:46 AM -109.969 -128.075 Left
10:29:47 AM 5.436299 11.53727 6.000019 14.49997 10:29:47 AM -109.281 -127.894 Left
10:29:48 AM 5.419587 11.58112 6.000019 14.49997 10:29:48 AM -109.301 -128.106 Left
10:29:49 AM 5.398224 11.63745 6.000019 14.49985 10:29:49 AM -109.305 -129.052 Left
10:29:50 AM 5.38126 11.67622 6.000019 14.49996 10:29:50 AM -108.783 -131.326 Left
10:29:51 AM 5.361076 11.72224 6.000019 14.4998 10:29:51 AM -110.035 -132.788 Left
10:29:52 AM 5.342669 11.78299 6.000019 14.49996 10:29:52 AM -110.785 -129.515 Left
10:29:53 AM 5.334421 11.82183 6.000019 14.49997 10:29:53 AM -110.064 -123.765 Left
10:29:54 AM 5.332245 11.88556 6.000019 14.49985 10:29:54 AM -109.403 -126.081 Left
10:29:55 AM 5.331328 11.92441 6.000019 14.4998
10:29:56 AM 5.329638 11.93354 6.000019 14.49997 10:29:56 AM -109.148 -127.626 Left
10:29:57 AM -109.639 -123.619 Left
10:29:58 AM 5.329639 11.93354 6.000019 14.49996 10:29:58 AM -113.845 -119.096 Left
10:29:59 AM 5.329639 11.93354 6.000019 14.49996 10:29:59 AM -112.018 -120.831 Left
10:30:00 AM 5.329639 11.93354 6.000019 14.4998 10:30:00 AM -112.023 -120.812 Left
10:30:01 AM 5.329639 11.93354 6.000019 14.49996 10:30:01 AM -112.023 -120.789 Left
10:30:02 AM 5.329639 11.93354 6.000019 14.49997 10:30:02 AM -112.023 -120.814 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
290
X Z X Z L R
10:29:02 AM 6 9.5 6 14.5
10:29:03 AM 6 9.5 6 14.5
10:29:04 AM 6 9.5 6 14.5
10:29:06 AM 5.993899 9.500897 6.000019 14.49985
10:29:07 AM 6.001026 9.552586 6.000019 14.49997 10:29:07 AM -134.623 -117.155 Right
10:29:08 AM 6.001791 9.552605 6.000019 14.49997 10:29:08 AM -136.954 -111.392 Right
10:29:09 AM 6.002508 9.552366 6.000019 14.49996 10:29:09 AM -135.503 -111.929 Right
10:29:10 AM 6.003298 9.552692 6.000019 14.49985 10:29:10 AM -135.892 -111.842 Right
10:29:11 AM 6.00343 9.554021 6.000019 14.4998 10:29:11 AM -135.956 -111.422 Right
10:29:12 AM 6.004618 9.554567 6.000019 14.49997 10:29:12 AM -135.579 -111.32 Right
10:29:13 AM 6.005475 9.554563 6.000019 14.49996 10:29:13 AM -134.125 -111.692 Right
10:29:14 AM 6.006477 9.554731 6.000019 14.4998 10:29:14 AM -133.504 -111.174 Right
10:29:15 AM 6.013505 9.755704 6.000019 14.4998 10:29:15 AM -132.764 -113.254 Right
10:29:16 AM -138.87 -114.332 Right
10:29:17 AM 6.0254 10.10101 6.000019 14.49996 10:29:17 AM -138.776 -114.908 Right
10:29:18 AM 6.037297 10.44524 6.000019 14.4998 10:29:18 AM -133.915 -113.251 Right
10:29:19 AM 6.049037 10.78276 6.000019 14.4998 10:29:19 AM -126.403 -109.987 Right
10:29:20 AM 6.060687 11.03611 6.000019 14.49997
10:29:21 AM 6.074476 11.03667 6.000019 14.49985 10:29:21 AM -110.917 -110.393 Right
10:29:22 AM 6.086651 11.0353 6.000019 14.49997 10:29:22 AM -108.5 -115.974 Left
10:29:23 AM 6.108089 11.03732 6.000019 14.49997 10:29:23 AM -109.07 -121.546 Left
10:29:24 AM 6.112276 11.04187 6.000019 14.49997 10:29:24 AM -110.547 -124.69 Left
10:29:25 AM -110.488 -131.279 Left
10:29:26 AM 6.106983 11.05216 6.000019 14.49997 10:29:26 AM -110.847 -132.425 Left
10:29:27 AM 6.063187 11.08296 6.000019 14.49996 10:29:27 AM -111.246 -133.773 Left
10:29:28 AM 6.019441 11.11028 6.000019 14.49996 10:29:28 AM -112.018 -138.849 Left
10:29:29 AM 5.9763 11.12583 6.000019 14.4998 10:29:29 AM -112.37 -143.224 Left
10:29:30 AM 5.930936 11.13321 6.000019 14.4998 10:29:30 AM -109.969 -145.554 Left
10:29:31 AM 5.882591 11.12912 6.000019 14.4998 10:29:31 AM -109.882 -147.993 Left
10:29:32 AM 5.824316 11.12665 6.000019 14.49996 10:29:32 AM -106.531 -151.692 Left
10:29:33 AM 5.776063 11.13215 6.000019 14.49996
10:29:34 AM 5.734162 11.14516 6.000019 14.4998 10:29:34 AM -108.16 -143.569 Left
10:29:35 AM 5.691587 11.16363 6.000019 14.49997 10:29:35 AM -108.524 -138.639 Left
10:29:36 AM -106.406 -140.587 Left
10:29:37 AM 5.65205 11.18499 6.000019 14.49985 10:29:37 AM -108.121 -137.836 Left
10:29:38 AM 5.606458 11.2185 6.000019 14.49997 10:29:38 AM -106.897 -133.821 Left
10:29:39 AM 5.569025 11.25131 6.000019 14.49997 10:29:39 AM -106.656 -131.33 Left
10:29:40 AM 5.545821 11.27992 6.000019 14.4998 10:29:40 AM -108.524 -130.364 Left
10:29:41 AM 5.522609 11.3123 6.000019 14.49985
10:29:42 AM 5.508795 11.35174 6.000019 14.49997 10:29:42 AM -119.03 -129.514 Left
10:29:43 AM 5.496674 11.39544 6.000019 14.49985 10:29:43 AM -112.295 -122.14 Left
10:29:44 AM 5.473576 11.4423 6.000019 14.49997 10:29:44 AM -111.806 -126.558 Left
10:29:45 AM -110.352 -128.026 Left
10:29:46 AM 5.458086 11.48223 6.000019 14.49996 10:29:46 AM -109.969 -128.075 Left
10:29:47 AM 5.436299 11.53727 6.000019 14.49997 10:29:47 AM -109.281 -127.894 Left
10:29:48 AM 5.419587 11.58112 6.000019 14.49997 10:29:48 AM -109.301 -128.106 Left
10:29:49 AM 5.398224 11.63745 6.000019 14.49985 10:29:49 AM -109.305 -129.052 Left
10:29:50 AM 5.38126 11.67622 6.000019 14.49996 10:29:50 AM -108.783 -131.326 Left
10:29:51 AM 5.361076 11.72224 6.000019 14.4998 10:29:51 AM -110.035 -132.788 Left
10:29:52 AM 5.342669 11.78299 6.000019 14.49996 10:29:52 AM -110.785 -129.515 Left
10:29:53 AM 5.334421 11.82183 6.000019 14.49997 10:29:53 AM -110.064 -123.765 Left
10:29:54 AM 5.332245 11.88556 6.000019 14.49985 10:29:54 AM -109.403 -126.081 Left
10:29:55 AM 5.331328 11.92441 6.000019 14.4998
10:29:56 AM 5.329638 11.93354 6.000019 14.49997 10:29:56 AM -109.148 -127.626 Left
10:29:57 AM -109.639 -123.619 Left
10:29:58 AM 5.329639 11.93354 6.000019 14.49996 10:29:58 AM -113.845 -119.096 Left
10:29:59 AM 5.329639 11.93354 6.000019 14.49996 10:29:59 AM -112.018 -120.831 Left
10:30:00 AM 5.329639 11.93354 6.000019 14.4998 10:30:00 AM -112.023 -120.812 Left
10:30:01 AM 5.329639 11.93354 6.000019 14.49996 10:30:01 AM -112.023 -120.789 Left
10:30:02 AM 5.329639 11.93354 6.000019 14.49997 10:30:02 AM -112.023 -120.814 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
291
Non-Uniform Obstruction with Left Scatter Sample 5
Motion Path Graph - Non-Uniform obstruction with Left Scatter – Sample 5.
Combined logs – Non-Uniform obstruction with Left Scatter – Sample 5.
0
2
4
6
8
10
12
14
16
5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.1
29-6-10-31
CARMI Child
X Z X Z L R
10:31:49 AM 6 9.5 6 14.5
10:31:50 AM 6 9.5 6 14.5
10:31:51 AM 6 9.5 6 14.5
10:31:52 AM 6 9.5 6 14.5
10:31:54 AM 5.992441 9.500944 6.000019 14.49985
10:31:55 AM 6.015962 9.651443 6.000019 14.49996 10:31:55 AM -134.028 -117.627 Right
10:31:56 AM 6.017711 9.651225 6.000019 14.49997 10:31:56 AM -138.898 -111.76 Right
10:31:57 AM 6.018039 9.652341 6.000019 14.49997 10:31:57 AM -135.641 -110.615 Right
10:31:58 AM 6.019086 9.652852 6.000019 14.49985 10:31:58 AM -136.477 -112.367 Right
10:31:59 AM -136.42 -111.494 Right
10:32:00 AM 6.020158 9.652794 6.000019 14.49985 10:32:00 AM -134.61 -112.603 Right
10:32:01 AM 6.020701 9.652414 6.000019 14.49997 10:32:01 AM -134.423 -112.868 Right
10:32:02 AM 6.021148 9.653369 6.000019 14.49996 10:32:02 AM -134.427 -112.663 Right
10:32:03 AM 6.022253 9.652926 6.000019 14.4998 10:32:03 AM -134.311 -111.208 Right
10:32:04 AM 6.019351 9.800809 6.000019 14.49985 10:32:04 AM -135.271 -114.655 Right
10:32:05 AM 6.011848 10.14206 6.000019 14.4998 10:32:05 AM -138.447 -114.434 Right
10:32:06 AM 6.004345 10.48331 6.000019 14.4998 10:32:06 AM -135.086 -113.539 Right
10:32:07 AM 5.996724 10.82981 6.000019 14.49985 10:32:07 AM -125.475 -110.912 Right
10:32:08 AM 5.998251 11.03976 6.000019 14.49997
10:32:09 AM 6.007415 11.04215 6.000019 14.49997 10:32:09 AM -109.141 -111.038 Left
10:32:10 AM -109.4 -114.326 Left
10:32:11 AM 6.016801 11.04697 6.000019 14.49985 10:32:11 AM -111.038 -121.661 Left
10:32:12 AM 6.031215 11.04751 6.000019 14.49996 10:32:12 AM -110.616 -127.104 Left
10:32:13 AM 5.999884 11.07943 6.000019 14.49996 10:32:13 AM -111.077 -129.548 Left
10:32:14 AM 5.962042 11.11023 6.000019 14.49997 10:32:14 AM -111.982 -133.921 Left
10:32:15 AM 5.918005 11.13874 6.000019 14.49997 10:32:15 AM -112.83 -136.566 Left
10:32:16 AM 5.869604 11.15947 6.000019 14.49996 10:32:16 AM -114.13 -139.255 Left
10:32:17 AM 5.817402 11.17077 6.000019 14.49996 10:32:17 AM -110.474 -143.333 Left
10:32:18 AM 5.767788 11.1756 6.000019 14.49997 10:32:18 AM -106.671 -147.982 Left
10:32:19 AM 5.720627 11.18741 6.000019 14.4998 10:32:19 AM -106.796 -142.741 Left
10:32:20 AM -107.334 -139.671 Left
10:32:21 AM 5.668927 11.21321 6.000019 14.49997
10:32:22 AM 5.627196 11.23389 6.000019 14.49985 10:32:22 AM -106.302 -138.98 Left
10:32:23 AM 5.582974 11.26388 6.000019 14.49985 10:32:23 AM -106.696 -137.111 Left
10:32:24 AM 5.554739 11.29339 6.000019 14.49996 10:32:24 AM -107.714 -133.328 Left
10:32:25 AM 5.528443 11.32741 6.000019 14.4998 10:32:25 AM -108.426 -131.465 Left
10:32:26 AM 5.501231 11.36947 6.000019 14.49985 10:32:26 AM -108.066 -129.399 Left
10:32:27 AM 5.484584 11.41826 6.000019 14.49997 10:32:27 AM -111.852 -126.552 Left
10:32:28 AM 5.463676 11.47809 6.000019 14.49996 10:32:28 AM -110.017 -126.358 Left
10:32:29 AM 5.439849 11.52605 6.000019 14.49997 10:32:29 AM -110.59 -129.566 Left
10:32:30 AM 5.420736 11.57261 6.000019 14.49985 10:32:30 AM -111.504 -131.287 Left
10:32:31 AM -108.406 -127.863 Left
10:32:32 AM 5.403268 11.61649 6.000019 14.4998 10:32:32 AM -109.13 -129.686 Left
10:32:33 AM 5.383748 11.65424 6.000019 14.49997 10:32:33 AM -108.987 -130.957 Left
10:32:34 AM 5.361908 11.69757 6.000019 14.49997 10:32:34 AM -112.006 -128.823 Left
10:32:35 AM 5.352765 11.74458 6.000019 14.49997 10:32:35 AM -110.421 -127.427 Left
10:32:36 AM 5.343889 11.80588 6.000019 14.4998
10:32:37 AM 5.327412 11.84401 6.000019 14.49996 10:32:37 AM -110.991 -130.026 Left
10:32:38 AM 5.327576 11.8968 6.000019 14.49985 10:32:38 AM -110.08 -129.711 Left
10:32:39 AM 5.326814 11.92074 6.000019 14.4998 10:32:39 AM -111.935 -122.264 Left
10:32:40 AM 5.326798 11.92074 6.000019 14.49996 10:32:40 AM -111.33 -119.963 Left
10:32:41 AM 5.32645 11.92021 6.000019 14.49997 10:32:41 AM -110.89 -120.137 Left
10:32:42 AM 5.325131 11.9213 6.000019 14.49985 10:32:42 AM -111.223 -122.454 Left
10:32:43 AM -110.026 -124.116 Left
10:32:44 AM 5.324026 11.92062 6.000019 14.4998 10:32:44 AM -111.433 -124.272 Left
10:32:45 AM 5.322851 11.91928 6.000019 14.49985 10:32:45 AM -111.565 -122.669 Left
10:32:46 AM 5.322619 11.91943 6.000019 14.49996 10:32:46 AM -115.466 -119.337 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
292
X Z X Z L R
10:31:49 AM 6 9.5 6 14.5
10:31:50 AM 6 9.5 6 14.5
10:31:51 AM 6 9.5 6 14.5
10:31:52 AM 6 9.5 6 14.5
10:31:54 AM 5.992441 9.500944 6.000019 14.49985
10:31:55 AM 6.015962 9.651443 6.000019 14.49996 10:31:55 AM -134.028 -117.627 Right
10:31:56 AM 6.017711 9.651225 6.000019 14.49997 10:31:56 AM -138.898 -111.76 Right
10:31:57 AM 6.018039 9.652341 6.000019 14.49997 10:31:57 AM -135.641 -110.615 Right
10:31:58 AM 6.019086 9.652852 6.000019 14.49985 10:31:58 AM -136.477 -112.367 Right
10:31:59 AM -136.42 -111.494 Right
10:32:00 AM 6.020158 9.652794 6.000019 14.49985 10:32:00 AM -134.61 -112.603 Right
10:32:01 AM 6.020701 9.652414 6.000019 14.49997 10:32:01 AM -134.423 -112.868 Right
10:32:02 AM 6.021148 9.653369 6.000019 14.49996 10:32:02 AM -134.427 -112.663 Right
10:32:03 AM 6.022253 9.652926 6.000019 14.4998 10:32:03 AM -134.311 -111.208 Right
10:32:04 AM 6.019351 9.800809 6.000019 14.49985 10:32:04 AM -135.271 -114.655 Right
10:32:05 AM 6.011848 10.14206 6.000019 14.4998 10:32:05 AM -138.447 -114.434 Right
10:32:06 AM 6.004345 10.48331 6.000019 14.4998 10:32:06 AM -135.086 -113.539 Right
10:32:07 AM 5.996724 10.82981 6.000019 14.49985 10:32:07 AM -125.475 -110.912 Right
10:32:08 AM 5.998251 11.03976 6.000019 14.49997
10:32:09 AM 6.007415 11.04215 6.000019 14.49997 10:32:09 AM -109.141 -111.038 Left
10:32:10 AM -109.4 -114.326 Left
10:32:11 AM 6.016801 11.04697 6.000019 14.49985 10:32:11 AM -111.038 -121.661 Left
10:32:12 AM 6.031215 11.04751 6.000019 14.49996 10:32:12 AM -110.616 -127.104 Left
10:32:13 AM 5.999884 11.07943 6.000019 14.49996 10:32:13 AM -111.077 -129.548 Left
10:32:14 AM 5.962042 11.11023 6.000019 14.49997 10:32:14 AM -111.982 -133.921 Left
10:32:15 AM 5.918005 11.13874 6.000019 14.49997 10:32:15 AM -112.83 -136.566 Left
10:32:16 AM 5.869604 11.15947 6.000019 14.49996 10:32:16 AM -114.13 -139.255 Left
10:32:17 AM 5.817402 11.17077 6.000019 14.49996 10:32:17 AM -110.474 -143.333 Left
10:32:18 AM 5.767788 11.1756 6.000019 14.49997 10:32:18 AM -106.671 -147.982 Left
10:32:19 AM 5.720627 11.18741 6.000019 14.4998 10:32:19 AM -106.796 -142.741 Left
10:32:20 AM -107.334 -139.671 Left
10:32:21 AM 5.668927 11.21321 6.000019 14.49997
10:32:22 AM 5.627196 11.23389 6.000019 14.49985 10:32:22 AM -106.302 -138.98 Left
10:32:23 AM 5.582974 11.26388 6.000019 14.49985 10:32:23 AM -106.696 -137.111 Left
10:32:24 AM 5.554739 11.29339 6.000019 14.49996 10:32:24 AM -107.714 -133.328 Left
10:32:25 AM 5.528443 11.32741 6.000019 14.4998 10:32:25 AM -108.426 -131.465 Left
10:32:26 AM 5.501231 11.36947 6.000019 14.49985 10:32:26 AM -108.066 -129.399 Left
10:32:27 AM 5.484584 11.41826 6.000019 14.49997 10:32:27 AM -111.852 -126.552 Left
10:32:28 AM 5.463676 11.47809 6.000019 14.49996 10:32:28 AM -110.017 -126.358 Left
10:32:29 AM 5.439849 11.52605 6.000019 14.49997 10:32:29 AM -110.59 -129.566 Left
10:32:30 AM 5.420736 11.57261 6.000019 14.49985 10:32:30 AM -111.504 -131.287 Left
10:32:31 AM -108.406 -127.863 Left
10:32:32 AM 5.403268 11.61649 6.000019 14.4998 10:32:32 AM -109.13 -129.686 Left
10:32:33 AM 5.383748 11.65424 6.000019 14.49997 10:32:33 AM -108.987 -130.957 Left
10:32:34 AM 5.361908 11.69757 6.000019 14.49997 10:32:34 AM -112.006 -128.823 Left
10:32:35 AM 5.352765 11.74458 6.000019 14.49997 10:32:35 AM -110.421 -127.427 Left
10:32:36 AM 5.343889 11.80588 6.000019 14.4998
10:32:37 AM 5.327412 11.84401 6.000019 14.49996 10:32:37 AM -110.991 -130.026 Left
10:32:38 AM 5.327576 11.8968 6.000019 14.49985 10:32:38 AM -110.08 -129.711 Left
10:32:39 AM 5.326814 11.92074 6.000019 14.4998 10:32:39 AM -111.935 -122.264 Left
10:32:40 AM 5.326798 11.92074 6.000019 14.49996 10:32:40 AM -111.33 -119.963 Left
10:32:41 AM 5.32645 11.92021 6.000019 14.49997 10:32:41 AM -110.89 -120.137 Left
10:32:42 AM 5.325131 11.9213 6.000019 14.49985 10:32:42 AM -111.223 -122.454 Left
10:32:43 AM -110.026 -124.116 Left
10:32:44 AM 5.324026 11.92062 6.000019 14.4998 10:32:44 AM -111.433 -124.272 Left
10:32:45 AM 5.322851 11.91928 6.000019 14.49985 10:32:45 AM -111.565 -122.669 Left
10:32:46 AM 5.322619 11.91943 6.000019 14.49996 10:32:46 AM -115.466 -119.337 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
293
Non-Uniform Obstruction with Left Scatter Sample 6
Motion Path Graph - Non-Uniform obstruction with Left Scatter – Sample 6.
Combined logs – Non-Uniform obstruction with Left Scatter – Sample 6.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.25.45.65.866.26.46.66.87
29-6-10-35
CARMI Child
X Z X Z L R
10:35:42 AM 6 9.5 6 14.5
10:35:43 AM 6 9.5 6 14.5
10:35:44 AM 6 9.5 6 14.5
10:35:45 AM 5.997726 9.500861 6.000019 14.49985
10:35:47 AM 5.998831 9.538358 6.000019 14.49996 10:35:47 AM -131.762 -116.799 Right
10:35:48 AM 6.021124 9.687458 6.000019 14.49985 10:35:48 AM -139.042 -112.463 Right
10:35:49 AM 6.021504 9.688312 6.000019 14.49996 10:35:49 AM -136.254 -109.694 Right
10:35:50 AM 6.021977 9.688683 6.000019 14.4998 10:35:50 AM -134.902 -113.562 Right
10:35:51 AM 6.033209 9.825398 6.000019 14.49996 10:35:51 AM -137.137 -114.924 Right
10:35:52 AM 6.060241 10.16522 6.000019 14.49985 10:35:52 AM -139.954 -117.075 Right
10:35:53 AM 6.068151 10.24783 6.000019 14.49985 10:35:53 AM -139.076 -114.805 Right
10:35:54 AM 6.069379 10.24768 6.000019 14.49985 10:35:54 AM -136.638 -115.614 Right
10:35:55 AM 6.069839 10.24837 6.000019 14.49997 10:35:55 AM -135.859 -115.493 Right
10:35:56 AM 6.073844 10.41429 6.000019 14.4998 10:35:56 AM -138.212 -115.434 Right
10:35:57 AM 6.081593 10.75526 6.000019 14.4998 10:35:57 AM -133.549 -113.438 Right
10:35:59 AM 6.089834 11.01756 6.000019 14.49997 10:35:59 AM -127.751 -111.639 Right
10:36:00 AM 6.099893 11.01826 6.000019 14.4998 10:36:00 AM -108.259 -110.335 Left
10:36:01 AM 6.107203 11.02236 6.000019 14.4998 10:36:01 AM -108.686 -116.945 Left
10:36:02 AM 6.116633 11.02734 6.000019 14.49985 10:36:02 AM -108.901 -122.28 Left
10:36:03 AM 6.125064 11.03553 6.000019 14.4998 10:36:03 AM -110.376 -125.011 Left
10:36:04 AM 6.119187 11.04694 6.000019 14.4998 10:36:04 AM -110.353 -131.277 Left
10:36:05 AM 6.076388 11.07811 6.000019 14.4998 10:36:05 AM -111.141 -135.267 Left
10:36:06 AM 6.031554 11.09907 6.000019 14.49996 10:36:06 AM -111.604 -135.651 Left
10:36:07 AM 5.973724 11.12075 6.000019 14.49985 10:36:07 AM -113.38 -139.666 Left
10:36:08 AM 5.922245 11.13306 6.000019 14.4998 10:36:08 AM -112.208 -145.839 Left
10:36:09 AM -110.46 -148.786 Left
10:36:10 AM 5.871832 11.13128 6.000019 14.49985 10:36:10 AM -108.121 -151.425 Left
10:36:11 AM 5.823204 11.12848 6.000019 14.49996
10:36:12 AM 5.77553 11.13321 6.000019 14.4998 10:36:12 AM -109.121 -144.983 Left
10:36:13 AM 5.721715 11.1478 6.000019 14.49996 10:36:13 AM -110.377 -139.231 Left
10:36:14 AM 5.668785 11.16832 6.000019 14.49996 10:36:14 AM -107.699 -141.931 Left
10:36:15 AM 5.620876 11.19143 6.000019 14.4998 10:36:15 AM -106.352 -141.758 Left
10:36:16 AM 5.582611 11.21489 6.000019 14.49997 10:36:16 AM -106.704 -137.384 Left
10:36:17 AM 5.545479 11.24505 6.000019 14.49997 10:36:17 AM -107.339 -135.923 Left
10:36:18 AM 5.502849 11.28734 6.000019 14.49985 10:36:18 AM -106.431 -133.28 Left
10:36:19 AM -109.908 -132.686 Left
10:36:20 AM 5.473536 11.33265 6.000019 14.49997 10:36:20 AM -108.542 -128.464 Left
10:36:21 AM 5.451928 11.38219 6.000019 14.49985
10:36:22 AM 5.432741 11.42766 6.000019 14.4998 10:36:22 AM -109.107 -124.747 Left
10:36:23 AM 5.4143 11.47338 6.000019 14.49997 10:36:23 AM -109.315 -125.552 Left
10:36:24 AM 5.401236 11.51884 6.000019 14.4998 10:36:24 AM -107.486 -126.888 Left
10:36:25 AM 5.382052 11.55947 6.000019 14.49996 10:36:25 AM -106.629 -127.941 Left
10:36:26 AM 5.365375 11.60686 6.000019 14.4998 10:36:26 AM -109.273 -130.118 Left
10:36:27 AM -108.856 -130.367 Left
10:36:28 AM 5.344848 11.651 6.000019 14.49996 10:36:28 AM -108.969 -131.193 Left
10:36:29 AM 5.325261 11.69282 6.000019 14.4998 10:36:29 AM -107.763 -133.266 Left
10:36:30 AM 5.30911 11.73516 6.000019 14.49997 10:36:30 AM -113.472 -125.353 Left
10:36:31 AM 5.301552 11.77855 6.000019 14.49985 10:36:31 AM -109.109 -124.53 Left
10:36:32 AM 5.29265 11.82998 6.000019 14.49996 10:36:32 AM -110.135 -126.948 Left
10:36:33 AM 5.285671 11.88521 6.000019 14.49985 10:36:33 AM -108.693 -126.548 Left
10:36:34 AM 5.287668 11.90834 6.000019 14.4998 10:36:34 AM -110.894 -118.906 Left
10:36:35 AM 5.287676 11.90833 6.000019 14.49985 10:36:35 AM -109.864 -119.268 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
294
X Z X Z L R
10:35:42 AM 6 9.5 6 14.5
10:35:43 AM 6 9.5 6 14.5
10:35:44 AM 6 9.5 6 14.5
10:35:45 AM 5.997726 9.500861 6.000019 14.49985
10:35:47 AM 5.998831 9.538358 6.000019 14.49996 10:35:47 AM -131.762 -116.799 Right
10:35:48 AM 6.021124 9.687458 6.000019 14.49985 10:35:48 AM -139.042 -112.463 Right
10:35:49 AM 6.021504 9.688312 6.000019 14.49996 10:35:49 AM -136.254 -109.694 Right
10:35:50 AM 6.021977 9.688683 6.000019 14.4998 10:35:50 AM -134.902 -113.562 Right
10:35:51 AM 6.033209 9.825398 6.000019 14.49996 10:35:51 AM -137.137 -114.924 Right
10:35:52 AM 6.060241 10.16522 6.000019 14.49985 10:35:52 AM -139.954 -117.075 Right
10:35:53 AM 6.068151 10.24783 6.000019 14.49985 10:35:53 AM -139.076 -114.805 Right
10:35:54 AM 6.069379 10.24768 6.000019 14.49985 10:35:54 AM -136.638 -115.614 Right
10:35:55 AM 6.069839 10.24837 6.000019 14.49997 10:35:55 AM -135.859 -115.493 Right
10:35:56 AM 6.073844 10.41429 6.000019 14.4998 10:35:56 AM -138.212 -115.434 Right
10:35:57 AM 6.081593 10.75526 6.000019 14.4998 10:35:57 AM -133.549 -113.438 Right
10:35:59 AM 6.089834 11.01756 6.000019 14.49997 10:35:59 AM -127.751 -111.639 Right
10:36:00 AM 6.099893 11.01826 6.000019 14.4998 10:36:00 AM -108.259 -110.335 Left
10:36:01 AM 6.107203 11.02236 6.000019 14.4998 10:36:01 AM -108.686 -116.945 Left
10:36:02 AM 6.116633 11.02734 6.000019 14.49985 10:36:02 AM -108.901 -122.28 Left
10:36:03 AM 6.125064 11.03553 6.000019 14.4998 10:36:03 AM -110.376 -125.011 Left
10:36:04 AM 6.119187 11.04694 6.000019 14.4998 10:36:04 AM -110.353 -131.277 Left
10:36:05 AM 6.076388 11.07811 6.000019 14.4998 10:36:05 AM -111.141 -135.267 Left
10:36:06 AM 6.031554 11.09907 6.000019 14.49996 10:36:06 AM -111.604 -135.651 Left
10:36:07 AM 5.973724 11.12075 6.000019 14.49985 10:36:07 AM -113.38 -139.666 Left
10:36:08 AM 5.922245 11.13306 6.000019 14.4998 10:36:08 AM -112.208 -145.839 Left
10:36:09 AM -110.46 -148.786 Left
10:36:10 AM 5.871832 11.13128 6.000019 14.49985 10:36:10 AM -108.121 -151.425 Left
10:36:11 AM 5.823204 11.12848 6.000019 14.49996
10:36:12 AM 5.77553 11.13321 6.000019 14.4998 10:36:12 AM -109.121 -144.983 Left
10:36:13 AM 5.721715 11.1478 6.000019 14.49996 10:36:13 AM -110.377 -139.231 Left
10:36:14 AM 5.668785 11.16832 6.000019 14.49996 10:36:14 AM -107.699 -141.931 Left
10:36:15 AM 5.620876 11.19143 6.000019 14.4998 10:36:15 AM -106.352 -141.758 Left
10:36:16 AM 5.582611 11.21489 6.000019 14.49997 10:36:16 AM -106.704 -137.384 Left
10:36:17 AM 5.545479 11.24505 6.000019 14.49997 10:36:17 AM -107.339 -135.923 Left
10:36:18 AM 5.502849 11.28734 6.000019 14.49985 10:36:18 AM -106.431 -133.28 Left
10:36:19 AM -109.908 -132.686 Left
10:36:20 AM 5.473536 11.33265 6.000019 14.49997 10:36:20 AM -108.542 -128.464 Left
10:36:21 AM 5.451928 11.38219 6.000019 14.49985
10:36:22 AM 5.432741 11.42766 6.000019 14.4998 10:36:22 AM -109.107 -124.747 Left
10:36:23 AM 5.4143 11.47338 6.000019 14.49997 10:36:23 AM -109.315 -125.552 Left
10:36:24 AM 5.401236 11.51884 6.000019 14.4998 10:36:24 AM -107.486 -126.888 Left
10:36:25 AM 5.382052 11.55947 6.000019 14.49996 10:36:25 AM -106.629 -127.941 Left
10:36:26 AM 5.365375 11.60686 6.000019 14.4998 10:36:26 AM -109.273 -130.118 Left
10:36:27 AM -108.856 -130.367 Left
10:36:28 AM 5.344848 11.651 6.000019 14.49996 10:36:28 AM -108.969 -131.193 Left
10:36:29 AM 5.325261 11.69282 6.000019 14.4998 10:36:29 AM -107.763 -133.266 Left
10:36:30 AM 5.30911 11.73516 6.000019 14.49997 10:36:30 AM -113.472 -125.353 Left
10:36:31 AM 5.301552 11.77855 6.000019 14.49985 10:36:31 AM -109.109 -124.53 Left
10:36:32 AM 5.29265 11.82998 6.000019 14.49996 10:36:32 AM -110.135 -126.948 Left
10:36:33 AM 5.285671 11.88521 6.000019 14.49985 10:36:33 AM -108.693 -126.548 Left
10:36:34 AM 5.287668 11.90834 6.000019 14.4998 10:36:34 AM -110.894 -118.906 Left
10:36:35 AM 5.287676 11.90833 6.000019 14.49985 10:36:35 AM -109.864 -119.268 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
295
Non-Uniform Obstruction with Right Scatter Sample 1
Motion Path Graph – Non-Uniform obstruction with Right Scatter – Sample 1.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.25.45.65.866.26.46.66.87
29-6-10-45
CARMI Child
296
Combined logs – Non-Uniform obstruction with Right Scatter – Sample 1.
X Z X Z L R
10:45:30 AM 6 9.5 6 14.5
10:45:31 AM 6 9.5 6 14.5
10:45:32 AM 6 9.5 6 14.5
10:45:33 AM 6 9.5 6 14.5
10:45:34 AM 5.991169 9.498323 6.000019 14.49996
10:45:36 AM 5.980903 9.470992 6.000019 14.4998
10:45:37 AM 5.951832 9.432722 6.000019 14.49996
10:45:38 AM 5.918835 9.405048 6.000019 14.4998
10:45:39 AM 5.889732 9.392619 6.000019 14.4998
10:45:40 AM 5.857801 9.382647 6.000019 14.49985
10:45:41 AM 5.805346 9.375422 6.000019 14.4998
10:45:42 AM 5.766459 9.363517 6.000019 14.49996
10:45:43 AM 5.758475 9.361457 6.000019 14.4998
10:45:44 AM 5.805631 9.362635 6.000019 14.49997 10:45:44 AM -153.253 -119.599 Right
10:45:45 AM 5.850259 9.371466 6.000019 14.49997 10:45:45 AM -151.4 -117.636 Right
10:45:46 AM -145.025 -120.262 Right
10:45:47 AM 5.898289 9.38806 6.000019 14.49985 10:45:47 AM -143.613 -122.579 Right
10:45:48 AM 5.950254 9.410283 6.000019 14.4998 10:45:48 AM -144.469 -118.267 Right
10:45:49 AM 5.993522 9.438661 6.000019 14.49996 10:45:49 AM -142.009 -117.252 Right
10:45:50 AM 6.033807 9.473278 6.000019 14.4998 10:45:50 AM -139.094 -119.684 Right
10:45:51 AM 6.068832 9.515831 6.000019 14.49997 10:45:51 AM -138.598 -120.25 Right
10:45:52 AM 6.098202 9.559823 6.000019 14.49997 10:45:52 AM -138.117 -118.864 Right
10:45:53 AM 6.126612 9.618297 6.000019 14.4998 10:45:53 AM -135.211 -118.057 Right
10:45:54 AM 6.138898 9.670525 6.000019 14.49997 10:45:54 AM -131.223 -121.282 Right
10:45:55 AM 6.146984 9.787813 6.000019 14.4998 10:45:55 AM -132.739 -123.703 Right
10:45:56 AM 6.166361 10.10063 6.000019 14.49996 10:45:56 AM -132.337 -126.144 Right
10:45:57 AM -131.709 -126.399 Right
10:45:58 AM 6.166996 10.10195 6.000019 14.4998
10:45:59 AM 6.167827 10.10053 6.000019 14.4998 10:45:59 AM -129.633 -128.143 Right
10:46:00 AM 6.168312 10.10058 6.000019 14.4998 10:46:00 AM -129.589 -128.083 Right
10:46:01 AM 6.168487 10.12768 6.000019 14.49985 10:46:01 AM -128.695 -128.366 Right
10:46:02 AM 6.172882 10.45613 6.000019 14.49997 10:46:02 AM -125.826 -126.719 Left
10:46:03 AM 6.177131 10.80173 6.000019 14.49985 10:46:03 AM -117.256 -124.154 Left
10:46:04 AM 6.1734 11.0282 6.000019 14.49997 10:46:04 AM -109.48 -123.021 Left
10:46:05 AM 6.160651 11.0334 6.000019 14.4998 10:46:05 AM -112.477 -116.749 Left
10:46:06 AM 6.155008 11.03693 6.000019 14.49985 10:46:06 AM -123.527 -118.376 Right
10:46:07 AM -127.453 -121.532 Right
10:46:08 AM 6.149774 11.04185 6.000019 14.49996 10:46:08 AM -127.757 -111.029 Right
10:46:09 AM 6.143581 11.0509 6.000019 14.49997 10:46:09 AM -134.996 -108.71 Right
10:46:10 AM 6.14938 11.06598 6.000019 14.4998
10:46:11 AM 6.194893 11.08014 6.000019 14.49996 10:46:11 AM -142.535 -111.03 Right
10:46:12 AM 6.240885 11.0917 6.000019 14.49985 10:46:12 AM -146.783 -107.979 Right
10:46:13 AM 6.291157 11.09692 6.000019 14.49997 10:46:13 AM -146.949 -108.898 Right
10:46:14 AM 6.348597 11.09426 6.000019 14.49996 10:46:14 AM -151.145 -109.481 Right
10:46:15 AM 6.401205 11.08679 6.000019 14.4998 10:46:15 AM -155.375 -108.422 Right
10:46:16 AM 6.449315 11.0744 6.000019 14.49996 10:46:16 AM -157.406 -108.263 Right
10:46:17 AM -160.04 -109.135 Right
10:46:18 AM 6.498727 11.05199 6.000019 14.4998 10:46:18 AM -160.04 -108.423 Right
10:46:19 AM 6.541477 11.0306 6.000019 14.49996 10:46:19 AM 121 Recenter-Right
10:46:20 AM 6.56611 11.02435 6.000019 14.49996 10:46:20 AM -159.806 -107.807 Right
10:46:21 AM 6.56626 11.02353 6.000019 14.4998 10:46:21 AM -158.956 -107.945 Right
10:46:22 AM 6.566901 11.02335 6.000019 14.4998 10:46:22 AM -159.122 -107.475 Right
10:46:23 AM 6.567548 11.02321 6.000019 14.49996
10:46:24 AM -158.734 -107.302 Right
10:46:25 AM 6.567902 11.02105 6.000019 14.49997 10:46:25 AM -157.335 -107.629 Right
10:46:26 AM 6.567883 11.02042 6.000019 14.49985 10:46:26 AM -157.421 -107.581 Right
10:46:27 AM 6.568752 11.01934 6.000019 14.49997 10:46:27 AM -157.56 -106.949 Right
10:46:28 AM 6.569962 11.01767 6.000019 14.49985 10:46:28 AM -156.513 -107.091 Right
10:46:29 AM 6.56933 11.01732 6.000019 14.49996 10:46:29 AM -155.849 -107.5 Right
10:46:30 AM 6.569633 11.01657 6.000019 14.49996 10:46:30 AM -156.09 -107.31 Right
10:46:31 AM 6.570367 11.01589 6.000019 14.49996 10:46:31 AM -156.096 -106.878 Right
10:46:32 AM 6.570165 11.01528 6.000019 14.4998 10:46:32 AM -154.892 -106.888 Right
10:46:33 AM 6.570065 11.0145 6.000019 14.49997 10:46:33 AM -154.838 -106.856 Right
10:46:34 AM -155.12 -106.82 Right
10:46:35 AM 6.569612 11.01409 6.000019 14.49985 10:46:35 AM -154.389 -106.844 Right
10:46:36 AM 6.569494 11.01345 6.000019 14.4998 10:46:36 AM -153.744 -106.875 Right
10:46:37 AM 6.569689 11.01248 6.000019 14.49996
10:46:38 AM 6.569174 11.01228 6.000019 14.49997 10:46:38 AM -151.94 -107.435 Right
10:46:39 AM 6.56943 11.0114 6.000019 14.49985 10:46:39 AM -152.071 -107.06 Right
10:46:40 AM 6.570269 11.01109 6.000019 14.49985 10:46:40 AM -152.299 -106.911 Right
10:46:41 AM 6.570642 11.01048 6.000019 14.49996 10:46:41 AM -152.339 -106.956 Right
10:46:42 AM 6.571862 11.01055 6.000019 14.49997 10:46:42 AM -152.588 -106.869 Right
10:46:43 AM 6.571805 11.00985 6.000019 14.49985 10:46:43 AM -152.231 -107.164 Right
10:46:44 AM 6.571815 11.00929 6.000019 14.4998 10:46:44 AM -150.656 -106.878 Right
10:46:45 AM -150.598 -106.937 Right
10:46:46 AM 6.571054 11.00822 6.000019 14.49996 10:46:46 AM -150.701 -106.953 Right
10:46:47 AM 6.570355 11.00682 6.000019 14.49985 10:46:47 AM -150.279 -107.942 Right
10:46:48 AM 6.569802 11.00642 6.000019 14.4998 10:46:48 AM -149.358 -107.257 Right
10:46:49 AM 6.570101 11.00573 6.000019 14.4998 10:46:49 AM -149.345 -107.75 Right
10:46:50 AM 6.569285 11.00417 6.000019 14.49997
10:46:51 AM 6.571017 11.00324 6.000019 14.49997 10:46:51 AM -148.566 -109.288 Right
10:46:52 AM 6.571182 11.00173 6.000019 14.49997 10:46:52 AM -147.382 -108.46 Right
10:46:53 AM 6.571203 11.00113 6.000019 14.49985 10:46:53 AM -146.68 -108.069 Right
10:46:54 AM 6.57149 11.0007 6.000019 14.4998 10:46:54 AM -146.69 -108.233 Right
10:46:55 AM 6.572985 11.00078 6.000019 14.49985 10:46:55 AM -146.67 -109.069 Right
10:46:56 AM 6.573534 11 6.000019 14.49997 10:46:56 AM -146.225 -110.048 Right
10:46:57 AM -145.07 -109.098 Right
10:46:58 AM 6.572688 10.99891 6.000019 14.49996 10:46:58 AM -145.046 -109.714 Right
10:46:59 AM 6.571851 10.99755 6.000019 14.4998
10:47:00 AM 6.573171 10.99592 6.000019 14.49996 10:47:00 AM -144.327 -112.133 Right
10:47:01 AM 6.573877 10.99536 6.000019 14.4998 10:47:01 AM -143.394 -110.754 Right
10:47:02 AM 6.574798 10.99486 6.000019 14.49996 10:47:02 AM -142.292 -109.33 Right
10:47:03 AM 6.575389 10.99266 6.000019 14.4998 10:47:03 AM -142.515 -111.384 Right
10:47:04 AM 6.57592 10.99097 6.000019 14.49985 10:47:04 AM -141.775 -114.416 Right
10:47:05 AM 6.576553 10.98968 6.000019 14.49997 10:47:05 AM -140.203 -113.669 Right
10:47:06 AM 6.577266 10.9883 6.000019 14.49997 10:47:06 AM -139.523 -112.823 Right
10:47:07 AM 6.578104 10.98692 6.000019 14.49985 10:47:07 AM -139.225 -115.469 Right
10:47:08 AM -137.852 -117.125 Right
10:47:09 AM 6.579064 10.98588 6.000019 14.49996 10:47:09 AM -136.94 -115.426 Right
10:47:10 AM 6.580445 10.98445 6.000019 14.49996 10:47:10 AM -136.564 -116.943 Right
10:47:11 AM 6.581757 10.98374 6.000019 14.4998
10:47:12 AM 6.583086 10.98255 6.000019 14.49996 10:47:12 AM -135.306 -117.861 Right
10:47:13 AM 6.584021 10.98164 6.000019 14.49985 10:47:13 AM -134.285 -116.528 Right
10:47:14 AM 6.585552 10.98039 6.000019 14.49997 10:47:14 AM -134.32 -117.56 Right
10:47:15 AM 6.587013 10.97934 6.000019 14.49996 10:47:15 AM -132.897 -119.445 Right
10:47:16 AM 6.588536 10.97813 6.000019 14.4998 10:47:16 AM -131.702 -118.339 Right
10:47:17 AM 6.590101 10.97822 6.000019 14.49997 10:47:17 AM -131.055 -118.816 Right
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
297
X Z X Z L R
10:45:30 AM 6 9.5 6 14.5
10:45:31 AM 6 9.5 6 14.5
10:45:32 AM 6 9.5 6 14.5
10:45:33 AM 6 9.5 6 14.5
10:45:34 AM 5.991169 9.498323 6.000019 14.49996
10:45:36 AM 5.980903 9.470992 6.000019 14.4998
10:45:37 AM 5.951832 9.432722 6.000019 14.49996
10:45:38 AM 5.918835 9.405048 6.000019 14.4998
10:45:39 AM 5.889732 9.392619 6.000019 14.4998
10:45:40 AM 5.857801 9.382647 6.000019 14.49985
10:45:41 AM 5.805346 9.375422 6.000019 14.4998
10:45:42 AM 5.766459 9.363517 6.000019 14.49996
10:45:43 AM 5.758475 9.361457 6.000019 14.4998
10:45:44 AM 5.805631 9.362635 6.000019 14.49997 10:45:44 AM -153.253 -119.599 Right
10:45:45 AM 5.850259 9.371466 6.000019 14.49997 10:45:45 AM -151.4 -117.636 Right
10:45:46 AM -145.025 -120.262 Right
10:45:47 AM 5.898289 9.38806 6.000019 14.49985 10:45:47 AM -143.613 -122.579 Right
10:45:48 AM 5.950254 9.410283 6.000019 14.4998 10:45:48 AM -144.469 -118.267 Right
10:45:49 AM 5.993522 9.438661 6.000019 14.49996 10:45:49 AM -142.009 -117.252 Right
10:45:50 AM 6.033807 9.473278 6.000019 14.4998 10:45:50 AM -139.094 -119.684 Right
10:45:51 AM 6.068832 9.515831 6.000019 14.49997 10:45:51 AM -138.598 -120.25 Right
10:45:52 AM 6.098202 9.559823 6.000019 14.49997 10:45:52 AM -138.117 -118.864 Right
10:45:53 AM 6.126612 9.618297 6.000019 14.4998 10:45:53 AM -135.211 -118.057 Right
10:45:54 AM 6.138898 9.670525 6.000019 14.49997 10:45:54 AM -131.223 -121.282 Right
10:45:55 AM 6.146984 9.787813 6.000019 14.4998 10:45:55 AM -132.739 -123.703 Right
10:45:56 AM 6.166361 10.10063 6.000019 14.49996 10:45:56 AM -132.337 -126.144 Right
10:45:57 AM -131.709 -126.399 Right
10:45:58 AM 6.166996 10.10195 6.000019 14.4998
10:45:59 AM 6.167827 10.10053 6.000019 14.4998 10:45:59 AM -129.633 -128.143 Right
10:46:00 AM 6.168312 10.10058 6.000019 14.4998 10:46:00 AM -129.589 -128.083 Right
10:46:01 AM 6.168487 10.12768 6.000019 14.49985 10:46:01 AM -128.695 -128.366 Right
10:46:02 AM 6.172882 10.45613 6.000019 14.49997 10:46:02 AM -125.826 -126.719 Left
10:46:03 AM 6.177131 10.80173 6.000019 14.49985 10:46:03 AM -117.256 -124.154 Left
10:46:04 AM 6.1734 11.0282 6.000019 14.49997 10:46:04 AM -109.48 -123.021 Left
10:46:05 AM 6.160651 11.0334 6.000019 14.4998 10:46:05 AM -112.477 -116.749 Left
10:46:06 AM 6.155008 11.03693 6.000019 14.49985 10:46:06 AM -123.527 -118.376 Right
10:46:07 AM -127.453 -121.532 Right
10:46:08 AM 6.149774 11.04185 6.000019 14.49996 10:46:08 AM -127.757 -111.029 Right
10:46:09 AM 6.143581 11.0509 6.000019 14.49997 10:46:09 AM -134.996 -108.71 Right
10:46:10 AM 6.14938 11.06598 6.000019 14.4998
10:46:11 AM 6.194893 11.08014 6.000019 14.49996 10:46:11 AM -142.535 -111.03 Right
10:46:12 AM 6.240885 11.0917 6.000019 14.49985 10:46:12 AM -146.783 -107.979 Right
10:46:13 AM 6.291157 11.09692 6.000019 14.49997 10:46:13 AM -146.949 -108.898 Right
10:46:14 AM 6.348597 11.09426 6.000019 14.49996 10:46:14 AM -151.145 -109.481 Right
10:46:15 AM 6.401205 11.08679 6.000019 14.4998 10:46:15 AM -155.375 -108.422 Right
10:46:16 AM 6.449315 11.0744 6.000019 14.49996 10:46:16 AM -157.406 -108.263 Right
10:46:17 AM -160.04 -109.135 Right
10:46:18 AM 6.498727 11.05199 6.000019 14.4998 10:46:18 AM -160.04 -108.423 Right
10:46:19 AM 6.541477 11.0306 6.000019 14.49996 10:46:19 AM 121 Recenter-Right
10:46:20 AM 6.56611 11.02435 6.000019 14.49996 10:46:20 AM -159.806 -107.807 Right
10:46:21 AM 6.56626 11.02353 6.000019 14.4998 10:46:21 AM -158.956 -107.945 Right
10:46:22 AM 6.566901 11.02335 6.000019 14.4998 10:46:22 AM -159.122 -107.475 Right
10:46:23 AM 6.567548 11.02321 6.000019 14.49996
10:46:24 AM -158.734 -107.302 Right
10:46:25 AM 6.567902 11.02105 6.000019 14.49997 10:46:25 AM -157.335 -107.629 Right
10:46:26 AM 6.567883 11.02042 6.000019 14.49985 10:46:26 AM -157.421 -107.581 Right
10:46:27 AM 6.568752 11.01934 6.000019 14.49997 10:46:27 AM -157.56 -106.949 Right
10:46:28 AM 6.569962 11.01767 6.000019 14.49985 10:46:28 AM -156.513 -107.091 Right
10:46:29 AM 6.56933 11.01732 6.000019 14.49996 10:46:29 AM -155.849 -107.5 Right
10:46:30 AM 6.569633 11.01657 6.000019 14.49996 10:46:30 AM -156.09 -107.31 Right
10:46:31 AM 6.570367 11.01589 6.000019 14.49996 10:46:31 AM -156.096 -106.878 Right
10:46:32 AM 6.570165 11.01528 6.000019 14.4998 10:46:32 AM -154.892 -106.888 Right
10:46:33 AM 6.570065 11.0145 6.000019 14.49997 10:46:33 AM -154.838 -106.856 Right
10:46:34 AM -155.12 -106.82 Right
10:46:35 AM 6.569612 11.01409 6.000019 14.49985 10:46:35 AM -154.389 -106.844 Right
10:46:36 AM 6.569494 11.01345 6.000019 14.4998 10:46:36 AM -153.744 -106.875 Right
10:46:37 AM 6.569689 11.01248 6.000019 14.49996
10:46:38 AM 6.569174 11.01228 6.000019 14.49997 10:46:38 AM -151.94 -107.435 Right
10:46:39 AM 6.56943 11.0114 6.000019 14.49985 10:46:39 AM -152.071 -107.06 Right
10:46:40 AM 6.570269 11.01109 6.000019 14.49985 10:46:40 AM -152.299 -106.911 Right
10:46:41 AM 6.570642 11.01048 6.000019 14.49996 10:46:41 AM -152.339 -106.956 Right
10:46:42 AM 6.571862 11.01055 6.000019 14.49997 10:46:42 AM -152.588 -106.869 Right
10:46:43 AM 6.571805 11.00985 6.000019 14.49985 10:46:43 AM -152.231 -107.164 Right
10:46:44 AM 6.571815 11.00929 6.000019 14.4998 10:46:44 AM -150.656 -106.878 Right
10:46:45 AM -150.598 -106.937 Right
10:46:46 AM 6.571054 11.00822 6.000019 14.49996 10:46:46 AM -150.701 -106.953 Right
10:46:47 AM 6.570355 11.00682 6.000019 14.49985 10:46:47 AM -150.279 -107.942 Right
10:46:48 AM 6.569802 11.00642 6.000019 14.4998 10:46:48 AM -149.358 -107.257 Right
10:46:49 AM 6.570101 11.00573 6.000019 14.4998 10:46:49 AM -149.345 -107.75 Right
10:46:50 AM 6.569285 11.00417 6.000019 14.49997
10:46:51 AM 6.571017 11.00324 6.000019 14.49997 10:46:51 AM -148.566 -109.288 Right
10:46:52 AM 6.571182 11.00173 6.000019 14.49997 10:46:52 AM -147.382 -108.46 Right
10:46:53 AM 6.571203 11.00113 6.000019 14.49985 10:46:53 AM -146.68 -108.069 Right
10:46:54 AM 6.57149 11.0007 6.000019 14.4998 10:46:54 AM -146.69 -108.233 Right
10:46:55 AM 6.572985 11.00078 6.000019 14.49985 10:46:55 AM -146.67 -109.069 Right
10:46:56 AM 6.573534 11 6.000019 14.49997 10:46:56 AM -146.225 -110.048 Right
10:46:57 AM -145.07 -109.098 Right
10:46:58 AM 6.572688 10.99891 6.000019 14.49996 10:46:58 AM -145.046 -109.714 Right
10:46:59 AM 6.571851 10.99755 6.000019 14.4998
10:47:00 AM 6.573171 10.99592 6.000019 14.49996 10:47:00 AM -144.327 -112.133 Right
10:47:01 AM 6.573877 10.99536 6.000019 14.4998 10:47:01 AM -143.394 -110.754 Right
10:47:02 AM 6.574798 10.99486 6.000019 14.49996 10:47:02 AM -142.292 -109.33 Right
10:47:03 AM 6.575389 10.99266 6.000019 14.4998 10:47:03 AM -142.515 -111.384 Right
10:47:04 AM 6.57592 10.99097 6.000019 14.49985 10:47:04 AM -141.775 -114.416 Right
10:47:05 AM 6.576553 10.98968 6.000019 14.49997 10:47:05 AM -140.203 -113.669 Right
10:47:06 AM 6.577266 10.9883 6.000019 14.49997 10:47:06 AM -139.523 -112.823 Right
10:47:07 AM 6.578104 10.98692 6.000019 14.49985 10:47:07 AM -139.225 -115.469 Right
10:47:08 AM -137.852 -117.125 Right
10:47:09 AM 6.579064 10.98588 6.000019 14.49996 10:47:09 AM -136.94 -115.426 Right
10:47:10 AM 6.580445 10.98445 6.000019 14.49996 10:47:10 AM -136.564 -116.943 Right
10:47:11 AM 6.581757 10.98374 6.000019 14.4998
10:47:12 AM 6.583086 10.98255 6.000019 14.49996 10:47:12 AM -135.306 -117.861 Right
10:47:13 AM 6.584021 10.98164 6.000019 14.49985 10:47:13 AM -134.285 -116.528 Right
10:47:14 AM 6.585552 10.98039 6.000019 14.49997 10:47:14 AM -134.32 -117.56 Right
10:47:15 AM 6.587013 10.97934 6.000019 14.49996 10:47:15 AM -132.897 -119.445 Right
10:47:16 AM 6.588536 10.97813 6.000019 14.4998 10:47:16 AM -131.702 -118.339 Right
10:47:17 AM 6.590101 10.97822 6.000019 14.49997 10:47:17 AM -131.055 -118.816 Right
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
298
Non-Uniform Obstruction with Right Scatter Sample 2
Motion Path Graph – Non-Uniform obstruction with Right Scatter – Sample 2.
Combined logs – Non-Uniform obstruction with Right Scatter – Sample 2.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.577.588.59
29-6-10-48
CARMI Child
X Z X Z L R
10:48:28 AM 6 9.5 6 14.5
10:48:29 AM 6 9.5 6 14.5
10:48:30 AM 6 9.5 6 14.5
10:48:31 AM 5.99592 9.500831 6.000019 14.4998
10:48:32 AM 6.009068 9.629572 6.000019 14.4998
10:48:33 AM -132.71 -121.923 Right
10:48:34 AM 6.018241 9.722364 6.000019 14.49996 10:48:34 AM -134.793 -120.342 Right
10:48:35 AM 6.018951 9.722609 6.000019 14.49997 10:48:35 AM -132.379 -120.669 Right
10:48:36 AM 6.019325 9.72325 6.000019 14.49997 10:48:36 AM -131.267 -122.602 Right
10:48:37 AM 6.020663 9.723968 6.000019 14.49996 10:48:37 AM -132.982 -121.241 Right
10:48:38 AM 6.021132 9.724427 6.000019 14.4998 10:48:38 AM -132.61 -121.392 Right
10:48:39 AM 6.022233 9.724319 6.000019 14.49996 10:48:39 AM -130.948 -122.342 Right
10:48:40 AM 6.022468 9.724886 6.000019 14.4998 10:48:40 AM -130.999 -121.761 Right
10:48:41 AM 6.022222 9.767302 6.000019 14.49985 10:48:41 AM -130.463 -121.9 Right
10:48:42 AM 6.016362 10.10283 6.000019 14.49985 10:48:42 AM -130.08 -125.872 Right
10:48:43 AM -125.861 -126.63 Left
10:48:44 AM 6.00977 10.48014 6.000019 14.49996 10:48:44 AM -117.647 -125.408 Left
10:48:45 AM 6.00391 10.81541 6.000019 14.49985 10:48:45 AM -108.618 -122.762 Left
10:48:46 AM 5.987459 11.06953 6.000019 14.49996 10:48:46 AM -109.612 -123.527 Left
10:48:47 AM 5.973427 11.06703 6.000019 14.49985 10:48:47 AM -116.899 -114.422 Right
10:48:48 AM 5.96084 11.07514 6.000019 14.49985
10:48:49 AM 5.952713 11.08417 6.000019 14.49997 10:48:49 AM -125.39 -112.528 Right
10:48:50 AM 5.985577 11.10983 6.000019 14.4998 10:48:50 AM -134.342 -108.657 Right
10:48:51 AM 6.028952 11.12534 6.000019 14.49985 10:48:51 AM -140.865 -110.024 Right
10:48:52 AM 6.087961 11.13871 6.000019 14.49996 10:48:52 AM -148.442 -114.168 Right
10:48:53 AM 6.13534 11.14533 6.000019 14.4998 10:48:53 AM -146.531 -109.406 Right
10:48:54 AM 6.191909 11.13982 6.000019 14.49997 10:48:54 AM -149.177 -109.559 Right
10:48:55 AM -155.428 -109.39 Right
10:48:56 AM 6.240949 11.13314 6.000019 14.49997 10:48:56 AM -158.071 -108.683 Right
10:48:57 AM 6.282056 11.11869 6.000019 14.49997 10:48:57 AM -159.71 -107.75 Right
10:48:58 AM 6.332334 11.09471 6.000019 14.4998 10:48:58 AM -159.748 -107.205 Right
10:48:59 AM 6.392854 11.07143 6.000019 14.49985 10:48:59 AM -158.088 -107.451 Right
10:49:00 AM 6.441599 11.0538 6.000019 14.4998 10:49:00 AM -158.056 -108.785 Right
10:49:01 AM 6.488751 11.03888 6.000019 14.49997 10:49:01 AM -159.709 -108.889 Right
10:49:02 AM 6.535773 11.02227 6.000019 14.49996 10:49:02 AM -160.04 -108.38 Right
10:49:03 AM 6.588978 11.00392 6.000019 14.4998
10:49:04 AM 6.636698 10.98684 6.000019 14.4998 10:49:04 AM -159.712 -109.505 Right
10:49:05 AM 6.680408 10.96762 6.000019 14.4998 10:49:05 AM -159.402 -108.284 Right
10:49:06 AM 6.72623 10.95802 6.000019 14.49997 10:49:06 AM -159.332 -107.888 Right
10:49:07 AM 6.77289 10.94839 6.000019 14.49997 10:49:07 AM -158.034 -108.114 Right
10:49:08 AM -157.738 -108.273 Right
10:49:09 AM 6.828001 10.93324 6.000019 14.49985 10:49:09 AM -157.738 -108.324 Right
10:49:10 AM 6.892042 10.92458 6.000019 14.49996 10:49:10 AM -157.022 -107.175 Right
10:49:11 AM 6.935096 10.91855 6.000019 14.49985 10:49:11 AM -156.743 -108.717 Right
10:49:12 AM 6.980083 10.90996 6.000019 14.4998 10:49:12 AM -157.483 -108.025 Right
10:49:13 AM 7.017516 10.90872 6.000019 14.49997 10:49:13 AM -155.416 -109.184 Right
10:49:14 AM 7.063556 10.90722 6.000019 14.49997 10:49:14 AM -155.407 -108.072 Right
10:49:15 AM 7.118571 10.89903 6.000019 14.4998 10:49:15 AM -156.236 -109.088 Right
10:49:16 AM 7.174786 10.89298 6.000019 14.4998 10:49:16 AM -156.419 -109.466 Right
10:49:17 AM 7.215933 10.88938 6.000019 14.49996 10:49:17 AM -156.236 -110.046 Right
10:49:18 AM 7.264478 10.89126 6.000019 14.49985 10:49:18 AM -155.73 -111.292 Right
10:49:20 AM 7.317478 10.88759 6.000019 14.49985 10:49:20 AM -156.758 -110.175 Right
10:49:21 AM 7.370412 10.88661 6.000019 14.4998 10:49:21 AM -156.802 -109.903 Right
10:49:22 AM 7.431563 10.88682 6.000019 14.49996 10:49:22 AM -156.407 -109.974 Right
10:49:23 AM 7.481662 10.88469 6.000019 14.49985 10:49:23 AM -156.403 -111.606 Right
10:49:24 AM 7.534972 10.89207 6.000019 14.49997 10:49:24 AM -157.08 -110.23 Right
10:49:25 AM 7.587667 10.88978 6.000019 14.4998 10:49:25 AM -156.744 -112.294 Right
10:49:26 AM 7.630478 10.89282 6.000019 14.49996 10:49:26 AM -157.09 -112.263 Right
10:49:27 AM 7.681082 10.89302 6.000019 14.49985 10:49:27 AM -157.731 -110.933 Right
10:49:28 AM 7.729269 10.88848 6.000019 14.4998 10:49:28 AM -159.713 -110.795 Right
10:49:29 AM 7.784951 10.88866 6.000019 14.4998 10:49:29 AM -159.737 -111.53 Right
10:49:30 AM -158.392 -110.936 Right
10:49:31 AM 7.830725 10.88566 6.000019 14.4998 10:49:31 AM -158.401 -110.239 Right
10:49:32 AM 7.890501 10.89056 6.000019 14.49985 10:49:32 AM -158.374 -110.504 Right
10:49:33 AM 7.939007 10.88898 6.000019 14.49997 10:49:33 AM -159.051 -111.423 Right
10:49:34 AM 7.986757 10.88636 6.000019 14.49985 10:49:34 AM -159.405 -111.284 Right
10:49:35 AM 8.039586 10.88745 6.000019 14.49996 10:49:35 AM -158.385 -109.397 Right
10:49:36 AM 8.094811 10.88987 6.000019 14.49985
10:49:37 AM 8.131251 10.89508 6.000019 14.49997 10:49:37 AM -158.523 -110.836 Right
10:49:38 AM 8.176563 10.90366 6.000019 14.49997 10:49:38 AM -155.523 -111.281 Right
10:49:39 AM 8.231293 10.91576 6.000019 14.49985 10:49:39 AM -152.779 -110.483 Right
10:49:40 AM 8.289344 10.94089 6.000019 14.49997 10:49:40 AM -153.349 -112.228 Right
10:49:41 AM -150.696 -110.81 Right
10:49:42 AM 8.345519 10.96112 6.000019 14.49996 10:49:42 AM -151.937 -110.94 Right
10:49:43 AM 8.389972 10.98054 6.000019 14.49985 10:49:43 AM -148.683 -111.759 Right
10:49:44 AM 8.431435 11.00328 6.000019 14.49996 10:49:44 AM -145.437 -111.531 Right
10:49:45 AM 8.469328 11.03376 6.000019 14.49985 10:49:45 AM -143.15 -109.378 Right
10:49:46 AM 8.503297 11.06351 6.000019 14.49997 10:49:46 AM -143.65 -110.372 Right
10:49:47 AM 8.532343 11.09614 6.000019 14.49996 10:49:47 AM -139.962 -113.601 Right
10:49:48 AM 8.55698 11.13955 6.000019 14.4998 10:49:48 AM -136.877 -107.859 Right
10:49:49 AM 8.586799 11.1834 6.000019 14.49997 10:49:49 AM -136.966 -110.191 Right
10:49:50 AM 8.60596 11.22488 6.000019 14.49996 10:49:50 AM -134.525 -112.419 Right
10:49:51 AM 8.620514 11.27305 6.000019 14.49997 10:49:51 AM -129.555 -106.039 Right
10:49:52 AM 8.636838 11.32127 6.000019 14.49996 10:49:52 AM -135.688 -106.03 Right
10:49:53 AM -138.026 -106.087 Right
10:49:54 AM 8.660669 11.37096 6.000019 14.49985
10:49:55 AM 8.680889 11.4193 6.000019 14.49997 10:49:55 AM -138.332 -105.745 Right
10:49:56 AM 8.696641 11.45739 6.000019 14.49985 10:49:56 AM -138.194 -105.767 Right
10:49:57 AM 8.714813 11.50523 6.000019 14.49997 10:49:57 AM -137.605 -105.56 Right
10:49:58 AM 8.733751 11.55114 6.000019 14.49997 10:49:58 AM -138.131 -105.691 Right
10:49:59 AM 8.753076 11.59394 6.000019 14.49985 10:49:59 AM -138.673 -105.829 Right
10:50:00 AM 8.768402 11.63455 6.000019 14.49985 10:50:00 AM -137.724 -105.804 Right
10:50:01 AM 8.777872 11.67525 6.000019 14.49996 10:50:01 AM -134.969 -106.888 Right
10:50:02 AM 8.787521 11.71886 6.000019 14.49996 10:50:02 AM -134.061 -107.205 Right
10:50:03 AM 8.797791 11.76655 6.000019 14.4998 10:50:03 AM -133.896 -106.367 Right
10:50:04 AM 8.798552 11.81849 6.000019 14.49996 10:50:04 AM -133.149 -107.397 Right
10:50:05 AM -131.328 -107.105 Right
10:50:06 AM 8.801516 11.87918 6.000019 14.49996 10:50:06 AM -128.457 -108.196 Right
10:50:07 AM 8.792281 11.92729 6.000019 14.49996 10:50:07 AM -125.662 -109.687 Right
10:50:08 AM 8.782799 11.97552 6.000019 14.4998 10:50:08 AM -125.944 -108.996 Right
10:50:09 AM 8.772814 12.01702 6.000019 14.49997 10:50:09 AM -120.094 -109.701 Right
10:50:10 AM 8.753618 12.06511 6.000019 14.4998 10:50:10 AM -120.897 -109.682 Right
10:50:11 AM 8.738868 12.10582 6.000019 14.49997
10:50:12 AM 8.71206 12.1451 6.000019 14.4998 10:50:12 AM -119.367 -109.212 Right
10:50:13 AM 8.68756 12.1848 6.000019 14.4998 10:50:13 AM -118.161 -111.606 Right
10:50:14 AM 8.671518 12.20762 6.000019 14.49997 10:50:14 AM -109.393 -112.875 Left
10:50:15 AM 8.502569 12.34295 6.000019 14.49996 10:50:15 AM -111.599 -112.478 Left
10:50:16 AM 8.243203 12.55266 6.000019 14.49996 10:50:16 AM -109.938 -114.899 Left
10:50:17 AM 8.059642 12.70112 6.000019 14.49996 10:50:17 AM -108.31 -115.247 Left
10:50:18 AM 8.059722 12.70107 6.000019 14.4998 10:50:18 AM -109.198 -115.031 Left
10:50:19 AM -109.188 -115.021 Left
10:50:20 AM 8.059722 12.70107 6.000019 14.49985 10:50:20 AM -109.185 -115.018 Left
10:50:21 AM 8.059722 12.70107 6.000019 14.49996 10:50:21 AM -109.191 -115.031 Left
10:50:22 AM 8.059722 12.70107 6.000019 14.49997
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
299
X Z X Z L R
10:48:28 AM 6 9.5 6 14.5
10:48:29 AM 6 9.5 6 14.5
10:48:30 AM 6 9.5 6 14.5
10:48:31 AM 5.99592 9.500831 6.000019 14.4998
10:48:32 AM 6.009068 9.629572 6.000019 14.4998
10:48:33 AM -132.71 -121.923 Right
10:48:34 AM 6.018241 9.722364 6.000019 14.49996 10:48:34 AM -134.793 -120.342 Right
10:48:35 AM 6.018951 9.722609 6.000019 14.49997 10:48:35 AM -132.379 -120.669 Right
10:48:36 AM 6.019325 9.72325 6.000019 14.49997 10:48:36 AM -131.267 -122.602 Right
10:48:37 AM 6.020663 9.723968 6.000019 14.49996 10:48:37 AM -132.982 -121.241 Right
10:48:38 AM 6.021132 9.724427 6.000019 14.4998 10:48:38 AM -132.61 -121.392 Right
10:48:39 AM 6.022233 9.724319 6.000019 14.49996 10:48:39 AM -130.948 -122.342 Right
10:48:40 AM 6.022468 9.724886 6.000019 14.4998 10:48:40 AM -130.999 -121.761 Right
10:48:41 AM 6.022222 9.767302 6.000019 14.49985 10:48:41 AM -130.463 -121.9 Right
10:48:42 AM 6.016362 10.10283 6.000019 14.49985 10:48:42 AM -130.08 -125.872 Right
10:48:43 AM -125.861 -126.63 Left
10:48:44 AM 6.00977 10.48014 6.000019 14.49996 10:48:44 AM -117.647 -125.408 Left
10:48:45 AM 6.00391 10.81541 6.000019 14.49985 10:48:45 AM -108.618 -122.762 Left
10:48:46 AM 5.987459 11.06953 6.000019 14.49996 10:48:46 AM -109.612 -123.527 Left
10:48:47 AM 5.973427 11.06703 6.000019 14.49985 10:48:47 AM -116.899 -114.422 Right
10:48:48 AM 5.96084 11.07514 6.000019 14.49985
10:48:49 AM 5.952713 11.08417 6.000019 14.49997 10:48:49 AM -125.39 -112.528 Right
10:48:50 AM 5.985577 11.10983 6.000019 14.4998 10:48:50 AM -134.342 -108.657 Right
10:48:51 AM 6.028952 11.12534 6.000019 14.49985 10:48:51 AM -140.865 -110.024 Right
10:48:52 AM 6.087961 11.13871 6.000019 14.49996 10:48:52 AM -148.442 -114.168 Right
10:48:53 AM 6.13534 11.14533 6.000019 14.4998 10:48:53 AM -146.531 -109.406 Right
10:48:54 AM 6.191909 11.13982 6.000019 14.49997 10:48:54 AM -149.177 -109.559 Right
10:48:55 AM -155.428 -109.39 Right
10:48:56 AM 6.240949 11.13314 6.000019 14.49997 10:48:56 AM -158.071 -108.683 Right
10:48:57 AM 6.282056 11.11869 6.000019 14.49997 10:48:57 AM -159.71 -107.75 Right
10:48:58 AM 6.332334 11.09471 6.000019 14.4998 10:48:58 AM -159.748 -107.205 Right
10:48:59 AM 6.392854 11.07143 6.000019 14.49985 10:48:59 AM -158.088 -107.451 Right
10:49:00 AM 6.441599 11.0538 6.000019 14.4998 10:49:00 AM -158.056 -108.785 Right
10:49:01 AM 6.488751 11.03888 6.000019 14.49997 10:49:01 AM -159.709 -108.889 Right
10:49:02 AM 6.535773 11.02227 6.000019 14.49996 10:49:02 AM -160.04 -108.38 Right
10:49:03 AM 6.588978 11.00392 6.000019 14.4998
10:49:04 AM 6.636698 10.98684 6.000019 14.4998 10:49:04 AM -159.712 -109.505 Right
10:49:05 AM 6.680408 10.96762 6.000019 14.4998 10:49:05 AM -159.402 -108.284 Right
10:49:06 AM 6.72623 10.95802 6.000019 14.49997 10:49:06 AM -159.332 -107.888 Right
10:49:07 AM 6.77289 10.94839 6.000019 14.49997 10:49:07 AM -158.034 -108.114 Right
10:49:08 AM -157.738 -108.273 Right
10:49:09 AM 6.828001 10.93324 6.000019 14.49985 10:49:09 AM -157.738 -108.324 Right
10:49:10 AM 6.892042 10.92458 6.000019 14.49996 10:49:10 AM -157.022 -107.175 Right
10:49:11 AM 6.935096 10.91855 6.000019 14.49985 10:49:11 AM -156.743 -108.717 Right
10:49:12 AM 6.980083 10.90996 6.000019 14.4998 10:49:12 AM -157.483 -108.025 Right
10:49:13 AM 7.017516 10.90872 6.000019 14.49997 10:49:13 AM -155.416 -109.184 Right
10:49:14 AM 7.063556 10.90722 6.000019 14.49997 10:49:14 AM -155.407 -108.072 Right
10:49:15 AM 7.118571 10.89903 6.000019 14.4998 10:49:15 AM -156.236 -109.088 Right
10:49:16 AM 7.174786 10.89298 6.000019 14.4998 10:49:16 AM -156.419 -109.466 Right
10:49:17 AM 7.215933 10.88938 6.000019 14.49996 10:49:17 AM -156.236 -110.046 Right
10:49:18 AM 7.264478 10.89126 6.000019 14.49985 10:49:18 AM -155.73 -111.292 Right
10:49:20 AM 7.317478 10.88759 6.000019 14.49985 10:49:20 AM -156.758 -110.175 Right
10:49:21 AM 7.370412 10.88661 6.000019 14.4998 10:49:21 AM -156.802 -109.903 Right
10:49:22 AM 7.431563 10.88682 6.000019 14.49996 10:49:22 AM -156.407 -109.974 Right
10:49:23 AM 7.481662 10.88469 6.000019 14.49985 10:49:23 AM -156.403 -111.606 Right
10:49:24 AM 7.534972 10.89207 6.000019 14.49997 10:49:24 AM -157.08 -110.23 Right
10:49:25 AM 7.587667 10.88978 6.000019 14.4998 10:49:25 AM -156.744 -112.294 Right
10:49:26 AM 7.630478 10.89282 6.000019 14.49996 10:49:26 AM -157.09 -112.263 Right
10:49:27 AM 7.681082 10.89302 6.000019 14.49985 10:49:27 AM -157.731 -110.933 Right
10:49:28 AM 7.729269 10.88848 6.000019 14.4998 10:49:28 AM -159.713 -110.795 Right
10:49:29 AM 7.784951 10.88866 6.000019 14.4998 10:49:29 AM -159.737 -111.53 Right
10:49:30 AM -158.392 -110.936 Right
10:49:31 AM 7.830725 10.88566 6.000019 14.4998 10:49:31 AM -158.401 -110.239 Right
10:49:32 AM 7.890501 10.89056 6.000019 14.49985 10:49:32 AM -158.374 -110.504 Right
10:49:33 AM 7.939007 10.88898 6.000019 14.49997 10:49:33 AM -159.051 -111.423 Right
10:49:34 AM 7.986757 10.88636 6.000019 14.49985 10:49:34 AM -159.405 -111.284 Right
10:49:35 AM 8.039586 10.88745 6.000019 14.49996 10:49:35 AM -158.385 -109.397 Right
10:49:36 AM 8.094811 10.88987 6.000019 14.49985
10:49:37 AM 8.131251 10.89508 6.000019 14.49997 10:49:37 AM -158.523 -110.836 Right
10:49:38 AM 8.176563 10.90366 6.000019 14.49997 10:49:38 AM -155.523 -111.281 Right
10:49:39 AM 8.231293 10.91576 6.000019 14.49985 10:49:39 AM -152.779 -110.483 Right
10:49:40 AM 8.289344 10.94089 6.000019 14.49997 10:49:40 AM -153.349 -112.228 Right
10:49:41 AM -150.696 -110.81 Right
10:49:42 AM 8.345519 10.96112 6.000019 14.49996 10:49:42 AM -151.937 -110.94 Right
10:49:43 AM 8.389972 10.98054 6.000019 14.49985 10:49:43 AM -148.683 -111.759 Right
10:49:44 AM 8.431435 11.00328 6.000019 14.49996 10:49:44 AM -145.437 -111.531 Right
10:49:45 AM 8.469328 11.03376 6.000019 14.49985 10:49:45 AM -143.15 -109.378 Right
10:49:46 AM 8.503297 11.06351 6.000019 14.49997 10:49:46 AM -143.65 -110.372 Right
10:49:47 AM 8.532343 11.09614 6.000019 14.49996 10:49:47 AM -139.962 -113.601 Right
10:49:48 AM 8.55698 11.13955 6.000019 14.4998 10:49:48 AM -136.877 -107.859 Right
10:49:49 AM 8.586799 11.1834 6.000019 14.49997 10:49:49 AM -136.966 -110.191 Right
10:49:50 AM 8.60596 11.22488 6.000019 14.49996 10:49:50 AM -134.525 -112.419 Right
10:49:51 AM 8.620514 11.27305 6.000019 14.49997 10:49:51 AM -129.555 -106.039 Right
10:49:52 AM 8.636838 11.32127 6.000019 14.49996 10:49:52 AM -135.688 -106.03 Right
10:49:53 AM -138.026 -106.087 Right
10:49:54 AM 8.660669 11.37096 6.000019 14.49985
10:49:55 AM 8.680889 11.4193 6.000019 14.49997 10:49:55 AM -138.332 -105.745 Right
10:49:56 AM 8.696641 11.45739 6.000019 14.49985 10:49:56 AM -138.194 -105.767 Right
10:49:57 AM 8.714813 11.50523 6.000019 14.49997 10:49:57 AM -137.605 -105.56 Right
10:49:58 AM 8.733751 11.55114 6.000019 14.49997 10:49:58 AM -138.131 -105.691 Right
10:49:59 AM 8.753076 11.59394 6.000019 14.49985 10:49:59 AM -138.673 -105.829 Right
10:50:00 AM 8.768402 11.63455 6.000019 14.49985 10:50:00 AM -137.724 -105.804 Right
10:50:01 AM 8.777872 11.67525 6.000019 14.49996 10:50:01 AM -134.969 -106.888 Right
10:50:02 AM 8.787521 11.71886 6.000019 14.49996 10:50:02 AM -134.061 -107.205 Right
10:50:03 AM 8.797791 11.76655 6.000019 14.4998 10:50:03 AM -133.896 -106.367 Right
10:50:04 AM 8.798552 11.81849 6.000019 14.49996 10:50:04 AM -133.149 -107.397 Right
10:50:05 AM -131.328 -107.105 Right
10:50:06 AM 8.801516 11.87918 6.000019 14.49996 10:50:06 AM -128.457 -108.196 Right
10:50:07 AM 8.792281 11.92729 6.000019 14.49996 10:50:07 AM -125.662 -109.687 Right
10:50:08 AM 8.782799 11.97552 6.000019 14.4998 10:50:08 AM -125.944 -108.996 Right
10:50:09 AM 8.772814 12.01702 6.000019 14.49997 10:50:09 AM -120.094 -109.701 Right
10:50:10 AM 8.753618 12.06511 6.000019 14.4998 10:50:10 AM -120.897 -109.682 Right
10:50:11 AM 8.738868 12.10582 6.000019 14.49997
10:50:12 AM 8.71206 12.1451 6.000019 14.4998 10:50:12 AM -119.367 -109.212 Right
10:50:13 AM 8.68756 12.1848 6.000019 14.4998 10:50:13 AM -118.161 -111.606 Right
10:50:14 AM 8.671518 12.20762 6.000019 14.49997 10:50:14 AM -109.393 -112.875 Left
10:50:15 AM 8.502569 12.34295 6.000019 14.49996 10:50:15 AM -111.599 -112.478 Left
10:50:16 AM 8.243203 12.55266 6.000019 14.49996 10:50:16 AM -109.938 -114.899 Left
10:50:17 AM 8.059642 12.70112 6.000019 14.49996 10:50:17 AM -108.31 -115.247 Left
10:50:18 AM 8.059722 12.70107 6.000019 14.4998 10:50:18 AM -109.198 -115.031 Left
10:50:19 AM -109.188 -115.021 Left
10:50:20 AM 8.059722 12.70107 6.000019 14.49985 10:50:20 AM -109.185 -115.018 Left
10:50:21 AM 8.059722 12.70107 6.000019 14.49996 10:50:21 AM -109.191 -115.031 Left
10:50:22 AM 8.059722 12.70107 6.000019 14.49997
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
300
X Z X Z L R
10:48:28 AM 6 9.5 6 14.5
10:48:29 AM 6 9.5 6 14.5
10:48:30 AM 6 9.5 6 14.5
10:48:31 AM 5.99592 9.500831 6.000019 14.4998
10:48:32 AM 6.009068 9.629572 6.000019 14.4998
10:48:33 AM -132.71 -121.923 Right
10:48:34 AM 6.018241 9.722364 6.000019 14.49996 10:48:34 AM -134.793 -120.342 Right
10:48:35 AM 6.018951 9.722609 6.000019 14.49997 10:48:35 AM -132.379 -120.669 Right
10:48:36 AM 6.019325 9.72325 6.000019 14.49997 10:48:36 AM -131.267 -122.602 Right
10:48:37 AM 6.020663 9.723968 6.000019 14.49996 10:48:37 AM -132.982 -121.241 Right
10:48:38 AM 6.021132 9.724427 6.000019 14.4998 10:48:38 AM -132.61 -121.392 Right
10:48:39 AM 6.022233 9.724319 6.000019 14.49996 10:48:39 AM -130.948 -122.342 Right
10:48:40 AM 6.022468 9.724886 6.000019 14.4998 10:48:40 AM -130.999 -121.761 Right
10:48:41 AM 6.022222 9.767302 6.000019 14.49985 10:48:41 AM -130.463 -121.9 Right
10:48:42 AM 6.016362 10.10283 6.000019 14.49985 10:48:42 AM -130.08 -125.872 Right
10:48:43 AM -125.861 -126.63 Left
10:48:44 AM 6.00977 10.48014 6.000019 14.49996 10:48:44 AM -117.647 -125.408 Left
10:48:45 AM 6.00391 10.81541 6.000019 14.49985 10:48:45 AM -108.618 -122.762 Left
10:48:46 AM 5.987459 11.06953 6.000019 14.49996 10:48:46 AM -109.612 -123.527 Left
10:48:47 AM 5.973427 11.06703 6.000019 14.49985 10:48:47 AM -116.899 -114.422 Right
10:48:48 AM 5.96084 11.07514 6.000019 14.49985
10:48:49 AM 5.952713 11.08417 6.000019 14.49997 10:48:49 AM -125.39 -112.528 Right
10:48:50 AM 5.985577 11.10983 6.000019 14.4998 10:48:50 AM -134.342 -108.657 Right
10:48:51 AM 6.028952 11.12534 6.000019 14.49985 10:48:51 AM -140.865 -110.024 Right
10:48:52 AM 6.087961 11.13871 6.000019 14.49996 10:48:52 AM -148.442 -114.168 Right
10:48:53 AM 6.13534 11.14533 6.000019 14.4998 10:48:53 AM -146.531 -109.406 Right
10:48:54 AM 6.191909 11.13982 6.000019 14.49997 10:48:54 AM -149.177 -109.559 Right
10:48:55 AM -155.428 -109.39 Right
10:48:56 AM 6.240949 11.13314 6.000019 14.49997 10:48:56 AM -158.071 -108.683 Right
10:48:57 AM 6.282056 11.11869 6.000019 14.49997 10:48:57 AM -159.71 -107.75 Right
10:48:58 AM 6.332334 11.09471 6.000019 14.4998 10:48:58 AM -159.748 -107.205 Right
10:48:59 AM 6.392854 11.07143 6.000019 14.49985 10:48:59 AM -158.088 -107.451 Right
10:49:00 AM 6.441599 11.0538 6.000019 14.4998 10:49:00 AM -158.056 -108.785 Right
10:49:01 AM 6.488751 11.03888 6.000019 14.49997 10:49:01 AM -159.709 -108.889 Right
10:49:02 AM 6.535773 11.02227 6.000019 14.49996 10:49:02 AM -160.04 -108.38 Right
10:49:03 AM 6.588978 11.00392 6.000019 14.4998
10:49:04 AM 6.636698 10.98684 6.000019 14.4998 10:49:04 AM -159.712 -109.505 Right
10:49:05 AM 6.680408 10.96762 6.000019 14.4998 10:49:05 AM -159.402 -108.284 Right
10:49:06 AM 6.72623 10.95802 6.000019 14.49997 10:49:06 AM -159.332 -107.888 Right
10:49:07 AM 6.77289 10.94839 6.000019 14.49997 10:49:07 AM -158.034 -108.114 Right
10:49:08 AM -157.738 -108.273 Right
10:49:09 AM 6.828001 10.93324 6.000019 14.49985 10:49:09 AM -157.738 -108.324 Right
10:49:10 AM 6.892042 10.92458 6.000019 14.49996 10:49:10 AM -157.022 -107.175 Right
10:49:11 AM 6.935096 10.91855 6.000019 14.49985 10:49:11 AM -156.743 -108.717 Right
10:49:12 AM 6.980083 10.90996 6.000019 14.4998 10:49:12 AM -157.483 -108.025 Right
10:49:13 AM 7.017516 10.90872 6.000019 14.49997 10:49:13 AM -155.416 -109.184 Right
10:49:14 AM 7.063556 10.90722 6.000019 14.49997 10:49:14 AM -155.407 -108.072 Right
10:49:15 AM 7.118571 10.89903 6.000019 14.4998 10:49:15 AM -156.236 -109.088 Right
10:49:16 AM 7.174786 10.89298 6.000019 14.4998 10:49:16 AM -156.419 -109.466 Right
10:49:17 AM 7.215933 10.88938 6.000019 14.49996 10:49:17 AM -156.236 -110.046 Right
10:49:18 AM 7.264478 10.89126 6.000019 14.49985 10:49:18 AM -155.73 -111.292 Right
10:49:20 AM 7.317478 10.88759 6.000019 14.49985 10:49:20 AM -156.758 -110.175 Right
10:49:21 AM 7.370412 10.88661 6.000019 14.4998 10:49:21 AM -156.802 -109.903 Right
10:49:22 AM 7.431563 10.88682 6.000019 14.49996 10:49:22 AM -156.407 -109.974 Right
10:49:23 AM 7.481662 10.88469 6.000019 14.49985 10:49:23 AM -156.403 -111.606 Right
10:49:24 AM 7.534972 10.89207 6.000019 14.49997 10:49:24 AM -157.08 -110.23 Right
10:49:25 AM 7.587667 10.88978 6.000019 14.4998 10:49:25 AM -156.744 -112.294 Right
10:49:26 AM 7.630478 10.89282 6.000019 14.49996 10:49:26 AM -157.09 -112.263 Right
10:49:27 AM 7.681082 10.89302 6.000019 14.49985 10:49:27 AM -157.731 -110.933 Right
10:49:28 AM 7.729269 10.88848 6.000019 14.4998 10:49:28 AM -159.713 -110.795 Right
10:49:29 AM 7.784951 10.88866 6.000019 14.4998 10:49:29 AM -159.737 -111.53 Right
10:49:30 AM -158.392 -110.936 Right
10:49:31 AM 7.830725 10.88566 6.000019 14.4998 10:49:31 AM -158.401 -110.239 Right
10:49:32 AM 7.890501 10.89056 6.000019 14.49985 10:49:32 AM -158.374 -110.504 Right
10:49:33 AM 7.939007 10.88898 6.000019 14.49997 10:49:33 AM -159.051 -111.423 Right
10:49:34 AM 7.986757 10.88636 6.000019 14.49985 10:49:34 AM -159.405 -111.284 Right
10:49:35 AM 8.039586 10.88745 6.000019 14.49996 10:49:35 AM -158.385 -109.397 Right
10:49:36 AM 8.094811 10.88987 6.000019 14.49985
10:49:37 AM 8.131251 10.89508 6.000019 14.49997 10:49:37 AM -158.523 -110.836 Right
10:49:38 AM 8.176563 10.90366 6.000019 14.49997 10:49:38 AM -155.523 -111.281 Right
10:49:39 AM 8.231293 10.91576 6.000019 14.49985 10:49:39 AM -152.779 -110.483 Right
10:49:40 AM 8.289344 10.94089 6.000019 14.49997 10:49:40 AM -153.349 -112.228 Right
10:49:41 AM -150.696 -110.81 Right
10:49:42 AM 8.345519 10.96112 6.000019 14.49996 10:49:42 AM -151.937 -110.94 Right
10:49:43 AM 8.389972 10.98054 6.000019 14.49985 10:49:43 AM -148.683 -111.759 Right
10:49:44 AM 8.431435 11.00328 6.000019 14.49996 10:49:44 AM -145.437 -111.531 Right
10:49:45 AM 8.469328 11.03376 6.000019 14.49985 10:49:45 AM -143.15 -109.378 Right
10:49:46 AM 8.503297 11.06351 6.000019 14.49997 10:49:46 AM -143.65 -110.372 Right
10:49:47 AM 8.532343 11.09614 6.000019 14.49996 10:49:47 AM -139.962 -113.601 Right
10:49:48 AM 8.55698 11.13955 6.000019 14.4998 10:49:48 AM -136.877 -107.859 Right
10:49:49 AM 8.586799 11.1834 6.000019 14.49997 10:49:49 AM -136.966 -110.191 Right
10:49:50 AM 8.60596 11.22488 6.000019 14.49996 10:49:50 AM -134.525 -112.419 Right
10:49:51 AM 8.620514 11.27305 6.000019 14.49997 10:49:51 AM -129.555 -106.039 Right
10:49:52 AM 8.636838 11.32127 6.000019 14.49996 10:49:52 AM -135.688 -106.03 Right
10:49:53 AM -138.026 -106.087 Right
10:49:54 AM 8.660669 11.37096 6.000019 14.49985
10:49:55 AM 8.680889 11.4193 6.000019 14.49997 10:49:55 AM -138.332 -105.745 Right
10:49:56 AM 8.696641 11.45739 6.000019 14.49985 10:49:56 AM -138.194 -105.767 Right
10:49:57 AM 8.714813 11.50523 6.000019 14.49997 10:49:57 AM -137.605 -105.56 Right
10:49:58 AM 8.733751 11.55114 6.000019 14.49997 10:49:58 AM -138.131 -105.691 Right
10:49:59 AM 8.753076 11.59394 6.000019 14.49985 10:49:59 AM -138.673 -105.829 Right
10:50:00 AM 8.768402 11.63455 6.000019 14.49985 10:50:00 AM -137.724 -105.804 Right
10:50:01 AM 8.777872 11.67525 6.000019 14.49996 10:50:01 AM -134.969 -106.888 Right
10:50:02 AM 8.787521 11.71886 6.000019 14.49996 10:50:02 AM -134.061 -107.205 Right
10:50:03 AM 8.797791 11.76655 6.000019 14.4998 10:50:03 AM -133.896 -106.367 Right
10:50:04 AM 8.798552 11.81849 6.000019 14.49996 10:50:04 AM -133.149 -107.397 Right
10:50:05 AM -131.328 -107.105 Right
10:50:06 AM 8.801516 11.87918 6.000019 14.49996 10:50:06 AM -128.457 -108.196 Right
10:50:07 AM 8.792281 11.92729 6.000019 14.49996 10:50:07 AM -125.662 -109.687 Right
10:50:08 AM 8.782799 11.97552 6.000019 14.4998 10:50:08 AM -125.944 -108.996 Right
10:50:09 AM 8.772814 12.01702 6.000019 14.49997 10:50:09 AM -120.094 -109.701 Right
10:50:10 AM 8.753618 12.06511 6.000019 14.4998 10:50:10 AM -120.897 -109.682 Right
10:50:11 AM 8.738868 12.10582 6.000019 14.49997
10:50:12 AM 8.71206 12.1451 6.000019 14.4998 10:50:12 AM -119.367 -109.212 Right
10:50:13 AM 8.68756 12.1848 6.000019 14.4998 10:50:13 AM -118.161 -111.606 Right
10:50:14 AM 8.671518 12.20762 6.000019 14.49997 10:50:14 AM -109.393 -112.875 Left
10:50:15 AM 8.502569 12.34295 6.000019 14.49996 10:50:15 AM -111.599 -112.478 Left
10:50:16 AM 8.243203 12.55266 6.000019 14.49996 10:50:16 AM -109.938 -114.899 Left
10:50:17 AM 8.059642 12.70112 6.000019 14.49996 10:50:17 AM -108.31 -115.247 Left
10:50:18 AM 8.059722 12.70107 6.000019 14.4998 10:50:18 AM -109.198 -115.031 Left
10:50:19 AM -109.188 -115.021 Left
10:50:20 AM 8.059722 12.70107 6.000019 14.49985 10:50:20 AM -109.185 -115.018 Left
10:50:21 AM 8.059722 12.70107 6.000019 14.49996 10:50:21 AM -109.191 -115.031 Left
10:50:22 AM 8.059722 12.70107 6.000019 14.49997
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
301
Non-Uniform Obstruction with Right Scatter Sample 3
Motion Path Graph – Non-Uniform obstruction with Right Scatter – Sample 3.
Combined logs – Non-Uniform obstruction with Right Scatter – Sample 3.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.566.577.588.59
29-6-10-52
CARMI Child
X Z X Z L R
10:52:40 AM 6 9.5 6 14.5
10:52:41 AM 6 9.5 6 14.5
10:52:42 AM 6 9.5 6 14.5
10:52:43 AM 5.990528 9.498259 6.000019 14.49985
10:52:44 AM 5.983474 9.48295 6.000019 14.49985
10:52:45 AM 5.964692 9.447685 6.000019 14.49997
10:52:46 AM -135.351 -112.688 Right
10:52:47 AM 5.976819 9.472078 6.000019 14.49997
10:52:48 AM 5.995047 9.528976 6.000019 14.49996 10:52:48 AM -132.694 -112.963 Right
10:52:49 AM 6.001012 9.544574 6.000019 14.49997 10:52:49 AM -129.037 -123.193 Right
10:52:50 AM 5.999889 9.54507 6.000019 14.49996 10:52:50 AM -130.817 -122.891 Right
10:52:51 AM 6.004016 9.72869 6.000019 14.4998 10:52:51 AM -133.053 -120.466 Right
10:52:52 AM 6.003926 9.733294 6.000019 14.49997 10:52:52 AM -131.333 -121.208 Right
10:52:53 AM 6.003814 9.891059 6.000019 14.49985 10:52:53 AM -130.561 -123.944 Right
10:52:54 AM 6.003565 10.23235 6.000019 14.49985 10:52:54 AM -130.185 -125.26 Right
10:52:55 AM 6.003317 10.57364 6.000019 14.49997 10:52:55 AM -123.365 -125.353 Left
10:52:56 AM 6.003062 10.92462 6.000019 14.4998 10:52:56 AM -114.681 -123.52 Left
10:52:57 AM 5.995727 11.01945 6.000019 14.49996 10:52:57 AM -108.929 -123.517 Left
10:52:58 AM -116.685 -114.294 Right
10:52:59 AM 5.987431 11.02194 6.000019 14.4998
10:53:00 AM 5.979954 11.02625 6.000019 14.49996 10:53:00 AM -125.151 -121.906 Right
10:53:01 AM 5.980797 11.04088 6.000019 14.49985 10:53:01 AM -126.948 -122.591 Right
10:53:02 AM 5.97635 11.04935 6.000019 14.49997 10:53:02 AM -129.48 -110.527 Right
10:53:03 AM 5.972887 11.05684 6.000019 14.4998 10:53:03 AM -135.907 -110.954 Right
10:53:04 AM 6.014514 11.08062 6.000019 14.4998 10:53:04 AM -140.432 -112.371 Right
10:53:05 AM 6.063323 11.10123 6.000019 14.4998 10:53:05 AM -142.056 -109.402 Right
10:53:06 AM 6.107279 11.10958 6.000019 14.49997 10:53:06 AM -146.553 -109.286 Right
10:53:07 AM 6.159662 11.10649 6.000019 14.49985 10:53:07 AM -155.076 -109.011 Right
10:53:08 AM 6.212564 11.09899 6.000019 14.49985 10:53:08 AM -155.751 -108.187 Right
10:53:09 AM -157.718 -109.506 Right
10:53:10 AM 6.25217 11.08564 6.000019 14.4998 10:53:10 AM -158.383 -108.117 Right
10:53:11 AM 6.29176 11.06707 6.000019 14.49997 10:53:11 AM -158.377 -106.647 Right
10:53:12 AM 6.333875 11.05068 6.000019 14.49996 10:53:12 AM -158.384 -107.564 Right
10:53:13 AM 6.381655 11.03246 6.000019 14.49996 10:53:13 AM -158.394 -108.101 Right
10:53:14 AM 6.417175 11.01487 6.000019 14.49996 10:53:14 AM -158.387 -109.07 Right
10:53:15 AM 6.461335 10.99534 6.000019 14.4998
10:53:16 AM 6.505663 10.97607 6.000019 14.49997 10:53:16 AM -158.054 -106.582 Right
10:53:17 AM 6.553511 10.96292 6.000019 14.49996 10:53:17 AM -158.308 -107.949 Right
10:53:18 AM 6.598693 10.9533 6.000019 14.49985 10:53:18 AM -157.61 -107.732 Right
10:53:19 AM 6.638103 10.94376 6.000019 14.49997 10:53:19 AM -156.833 -107.427 Right
10:53:20 AM 6.720123 10.93359 6.000019 14.49985 10:53:20 AM -157.147 -107.164 Right
10:53:21 AM -156.413 -109.544 Right
10:53:22 AM 6.757442 10.92128 6.000019 14.49997 10:53:22 AM -157.677 -107.981 Right
10:53:23 AM 6.813384 10.91371 6.000019 14.49985 10:53:23 AM -156.376 -108.387 Right
10:53:24 AM 6.875503 10.90206 6.000019 14.49996 10:53:24 AM -155.914 -109.041 Right
10:53:25 AM 6.929533 10.90117 6.000019 14.49985 10:53:25 AM -156.186 -110.265 Right
10:53:26 AM 6.986537 10.89872 6.000019 14.4998 10:53:26 AM -155.74 -109.148 Right
10:53:27 AM 7.038148 10.89448 6.000019 14.49997 10:53:27 AM -154.433 -109.864 Right
10:53:28 AM 7.083647 10.89595 6.000019 14.49985 10:53:28 AM -154.601 -112.082 Right
10:53:29 AM 7.128892 10.88597 6.000019 14.49985 10:53:29 AM -156.872 -109.209 Right
10:53:30 AM 7.179657 10.88296 6.000019 14.4998 10:53:30 AM -156.306 -109.468 Right
10:53:31 AM 7.23559 10.88592 6.000019 14.49996
10:53:32 AM 7.29436 10.88703 6.000019 14.49985 10:53:32 AM -155.111 -110.351 Right
10:53:33 AM -154.422 -109.447 Right
10:53:34 AM 7.348278 10.88949 6.000019 14.4998 10:53:34 AM -155.486 -112.373 Right
10:53:35 AM 7.405777 10.89291 6.000019 14.49996 10:53:35 AM -156.363 -107.649 Right
10:53:36 AM 7.458394 10.89381 6.000019 14.49997 10:53:36 AM -156.399 -108.95 Right
10:53:37 AM 7.506371 10.8902 6.000019 14.49996 10:53:37 AM -157.064 -111.3 Right
10:53:38 AM 7.558899 10.8893 6.000019 14.4998 10:53:38 AM -158.081 -110.418 Right
10:53:39 AM 7.601651 10.88891 6.000019 14.4998 10:53:39 AM -158.058 -109.881 Right
10:53:40 AM 7.653618 10.88245 6.000019 14.49996 10:53:40 AM -158.054 -111.747 Right
10:53:41 AM 7.704514 10.88886 6.000019 14.49997 10:53:41 AM -157.73 -110.094 Right
10:53:42 AM 7.750939 10.8865 6.000019 14.49996 10:53:42 AM -157.74 -111.849 Right
10:53:43 AM 7.807344 10.8842 6.000019 14.49997 10:53:43 AM -158.396 -111.206 Right
10:53:44 AM 7.859224 10.88212 6.000019 14.4998 10:53:44 AM -158.389 -111.773 Right
10:53:45 AM -158.397 -110.244 Right
10:53:46 AM 7.911872 10.89171 6.000019 14.49997 10:53:46 AM -159.069 -113.59 Right
10:53:47 AM 7.966067 10.88703 6.000019 14.4998 10:53:47 AM -159.71 -111.984 Right
10:53:48 AM 8.021511 10.88443 6.000019 14.49996 10:53:48 AM -159.763 -110.955 Right
10:53:49 AM 8.072015 10.88268 6.000019 14.4998
10:53:50 AM 8.120579 10.88816 6.000019 14.49985 10:53:50 AM -158.223 -110.914 Right
10:53:51 AM 8.165326 10.89625 6.000019 14.4998 10:53:51 AM -154.928 -111.881 Right
10:53:52 AM 8.218029 10.90933 6.000019 14.49985 10:53:52 AM -153.377 -111.077 Right
10:53:53 AM 8.261042 10.92638 6.000019 14.49985 10:53:53 AM -154.02 -110.843 Right
10:53:54 AM 8.298958 10.93854 6.000019 14.49997 10:53:54 AM -151.259 -110.928 Right
10:53:55 AM 8.348174 10.96123 6.000019 14.49985 10:53:55 AM -150.58 -111.4 Right
10:53:56 AM -149.91 -110.882 Right
10:53:57 AM 8.388549 10.98051 6.000019 14.4998 10:53:57 AM -148.548 -112.013 Right
10:53:58 AM 8.427267 11.00776 6.000019 14.49985 10:53:58 AM -144.717 -112.751 Right
10:53:59 AM 8.473227 11.04639 6.000019 14.49997 10:53:59 AM -143.321 -110.777 Right
10:54:00 AM 8.506916 11.08338 6.000019 14.49985 10:54:00 AM -142.628 -111.792 Right
10:54:01 AM 8.542924 11.12478 6.000019 14.49997 10:54:01 AM -139.227 -110.664 Right
10:54:02 AM 8.568088 11.15876 6.000019 14.49996 10:54:02 AM -136.317 -109.043 Right
10:54:03 AM 8.591011 11.2053 6.000019 14.49985 10:54:03 AM -135.81 -106.855 Right
10:54:04 AM 8.610962 11.25364 6.000019 14.49997 10:54:04 AM -135.477 -106.463 Right
10:54:05 AM 8.623846 11.30002 6.000019 14.49996 10:54:05 AM -135.518 -105.044 Right
10:54:06 AM 8.646067 11.35177 6.000019 14.4998 10:54:06 AM -136.213 -106.328 Right
10:54:08 AM 8.662441 11.39136 6.000019 14.49997 10:54:08 AM -136.133 -104.911 Right
10:54:09 AM 8.676679 11.43199 6.000019 14.4998 10:54:09 AM -138.061 -104.817 Right
10:54:10 AM 8.694728 11.47722 6.000019 14.49997 10:54:10 AM -138.703 -105.996 Right
10:54:11 AM 8.715306 11.52419 6.000019 14.49985 10:54:11 AM -138.371 -105.558 Right
10:54:12 AM 8.732287 11.57507 6.000019 14.49997 10:54:12 AM -136.94 -104.998 Right
10:54:13 AM 8.754978 11.62936 6.000019 14.49985 10:54:13 AM -135.752 -107.284 Right
10:54:14 AM 8.767001 11.67349 6.000019 14.4998 10:54:14 AM -137.311 -105.438 Right
10:54:15 AM 8.779992 11.72279 6.000019 14.49996 10:54:15 AM -132.107 -107.746 Right
10:54:16 AM 8.787507 11.77128 6.000019 14.49996 10:54:16 AM -131.524 -105.908 Right
10:54:17 AM 8.791326 11.81702 6.000019 14.49996 10:54:17 AM -132.436 -107.511 Right
10:54:18 AM 8.793837 11.86645 6.000019 14.49996 10:54:18 AM -130.448 -107.309 Right
10:54:19 AM -128.027 -108.854 Right
10:54:20 AM 8.786958 11.91652 6.000019 14.49985 10:54:20 AM -127.804 -108.292 Right
10:54:21 AM 8.782507 11.96116 6.000019 14.49997 10:54:21 AM -127.025 -109.036 Right
10:54:22 AM 8.769834 12.00736 6.000019 14.49996 10:54:22 AM -117.526 -111.993 Right
10:54:23 AM 8.755476 12.04625 6.000019 14.49985 10:54:23 AM -121.104 -107.875 Right
10:54:24 AM 8.738588 12.08933 6.000019 14.49997 10:54:24 AM -123.031 -110.738 Right
10:54:25 AM 8.716022 12.14155 6.000019 14.49996 10:54:25 AM -113.126 -112.385 Right
10:54:26 AM 8.68796 12.18189 6.000019 14.49985
10:54:27 AM 8.681116 12.19664 6.000019 14.49997 10:54:27 AM -110.794 -113.232 Left
10:54:28 AM 8.621801 12.25184 6.000019 14.49996 10:54:28 AM -113.744 -114.925 Left
10:54:29 AM 8.373569 12.49205 6.000019 14.4998 10:54:29 AM -116.175 -111.088 Right
10:54:30 AM 8.140544 12.71758 6.000019 14.49996 10:54:30 AM -117.775 -111.673 Right
10:54:31 AM -110.326 -111.802 Left
10:54:32 AM 8.139504 12.71844 6.000019 14.49985 10:54:32 AM -110.243 -111.927 Left
10:54:33 AM 8.139413 12.71875 6.000019 14.49997 10:54:33 AM -110.108 -111.913 Left
10:54:34 AM 8.139585 12.71886 6.000019 14.49985 10:54:34 AM -110.14 -111.911 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
302
X Z X Z L R
10:52:40 AM 6 9.5 6 14.5
10:52:41 AM 6 9.5 6 14.5
10:52:42 AM 6 9.5 6 14.5
10:52:43 AM 5.990528 9.498259 6.000019 14.49985
10:52:44 AM 5.983474 9.48295 6.000019 14.49985
10:52:45 AM 5.964692 9.447685 6.000019 14.49997
10:52:46 AM -135.351 -112.688 Right
10:52:47 AM 5.976819 9.472078 6.000019 14.49997
10:52:48 AM 5.995047 9.528976 6.000019 14.49996 10:52:48 AM -132.694 -112.963 Right
10:52:49 AM 6.001012 9.544574 6.000019 14.49997 10:52:49 AM -129.037 -123.193 Right
10:52:50 AM 5.999889 9.54507 6.000019 14.49996 10:52:50 AM -130.817 -122.891 Right
10:52:51 AM 6.004016 9.72869 6.000019 14.4998 10:52:51 AM -133.053 -120.466 Right
10:52:52 AM 6.003926 9.733294 6.000019 14.49997 10:52:52 AM -131.333 -121.208 Right
10:52:53 AM 6.003814 9.891059 6.000019 14.49985 10:52:53 AM -130.561 -123.944 Right
10:52:54 AM 6.003565 10.23235 6.000019 14.49985 10:52:54 AM -130.185 -125.26 Right
10:52:55 AM 6.003317 10.57364 6.000019 14.49997 10:52:55 AM -123.365 -125.353 Left
10:52:56 AM 6.003062 10.92462 6.000019 14.4998 10:52:56 AM -114.681 -123.52 Left
10:52:57 AM 5.995727 11.01945 6.000019 14.49996 10:52:57 AM -108.929 -123.517 Left
10:52:58 AM -116.685 -114.294 Right
10:52:59 AM 5.987431 11.02194 6.000019 14.4998
10:53:00 AM 5.979954 11.02625 6.000019 14.49996 10:53:00 AM -125.151 -121.906 Right
10:53:01 AM 5.980797 11.04088 6.000019 14.49985 10:53:01 AM -126.948 -122.591 Right
10:53:02 AM 5.97635 11.04935 6.000019 14.49997 10:53:02 AM -129.48 -110.527 Right
10:53:03 AM 5.972887 11.05684 6.000019 14.4998 10:53:03 AM -135.907 -110.954 Right
10:53:04 AM 6.014514 11.08062 6.000019 14.4998 10:53:04 AM -140.432 -112.371 Right
10:53:05 AM 6.063323 11.10123 6.000019 14.4998 10:53:05 AM -142.056 -109.402 Right
10:53:06 AM 6.107279 11.10958 6.000019 14.49997 10:53:06 AM -146.553 -109.286 Right
10:53:07 AM 6.159662 11.10649 6.000019 14.49985 10:53:07 AM -155.076 -109.011 Right
10:53:08 AM 6.212564 11.09899 6.000019 14.49985 10:53:08 AM -155.751 -108.187 Right
10:53:09 AM -157.718 -109.506 Right
10:53:10 AM 6.25217 11.08564 6.000019 14.4998 10:53:10 AM -158.383 -108.117 Right
10:53:11 AM 6.29176 11.06707 6.000019 14.49997 10:53:11 AM -158.377 -106.647 Right
10:53:12 AM 6.333875 11.05068 6.000019 14.49996 10:53:12 AM -158.384 -107.564 Right
10:53:13 AM 6.381655 11.03246 6.000019 14.49996 10:53:13 AM -158.394 -108.101 Right
10:53:14 AM 6.417175 11.01487 6.000019 14.49996 10:53:14 AM -158.387 -109.07 Right
10:53:15 AM 6.461335 10.99534 6.000019 14.4998
10:53:16 AM 6.505663 10.97607 6.000019 14.49997 10:53:16 AM -158.054 -106.582 Right
10:53:17 AM 6.553511 10.96292 6.000019 14.49996 10:53:17 AM -158.308 -107.949 Right
10:53:18 AM 6.598693 10.9533 6.000019 14.49985 10:53:18 AM -157.61 -107.732 Right
10:53:19 AM 6.638103 10.94376 6.000019 14.49997 10:53:19 AM -156.833 -107.427 Right
10:53:20 AM 6.720123 10.93359 6.000019 14.49985 10:53:20 AM -157.147 -107.164 Right
10:53:21 AM -156.413 -109.544 Right
10:53:22 AM 6.757442 10.92128 6.000019 14.49997 10:53:22 AM -157.677 -107.981 Right
10:53:23 AM 6.813384 10.91371 6.000019 14.49985 10:53:23 AM -156.376 -108.387 Right
10:53:24 AM 6.875503 10.90206 6.000019 14.49996 10:53:24 AM -155.914 -109.041 Right
10:53:25 AM 6.929533 10.90117 6.000019 14.49985 10:53:25 AM -156.186 -110.265 Right
10:53:26 AM 6.986537 10.89872 6.000019 14.4998 10:53:26 AM -155.74 -109.148 Right
10:53:27 AM 7.038148 10.89448 6.000019 14.49997 10:53:27 AM -154.433 -109.864 Right
10:53:28 AM 7.083647 10.89595 6.000019 14.49985 10:53:28 AM -154.601 -112.082 Right
10:53:29 AM 7.128892 10.88597 6.000019 14.49985 10:53:29 AM -156.872 -109.209 Right
10:53:30 AM 7.179657 10.88296 6.000019 14.4998 10:53:30 AM -156.306 -109.468 Right
10:53:31 AM 7.23559 10.88592 6.000019 14.49996
10:53:32 AM 7.29436 10.88703 6.000019 14.49985 10:53:32 AM -155.111 -110.351 Right
10:53:33 AM -154.422 -109.447 Right
10:53:34 AM 7.348278 10.88949 6.000019 14.4998 10:53:34 AM -155.486 -112.373 Right
10:53:35 AM 7.405777 10.89291 6.000019 14.49996 10:53:35 AM -156.363 -107.649 Right
10:53:36 AM 7.458394 10.89381 6.000019 14.49997 10:53:36 AM -156.399 -108.95 Right
10:53:37 AM 7.506371 10.8902 6.000019 14.49996 10:53:37 AM -157.064 -111.3 Right
10:53:38 AM 7.558899 10.8893 6.000019 14.4998 10:53:38 AM -158.081 -110.418 Right
10:53:39 AM 7.601651 10.88891 6.000019 14.4998 10:53:39 AM -158.058 -109.881 Right
10:53:40 AM 7.653618 10.88245 6.000019 14.49996 10:53:40 AM -158.054 -111.747 Right
10:53:41 AM 7.704514 10.88886 6.000019 14.49997 10:53:41 AM -157.73 -110.094 Right
10:53:42 AM 7.750939 10.8865 6.000019 14.49996 10:53:42 AM -157.74 -111.849 Right
10:53:43 AM 7.807344 10.8842 6.000019 14.49997 10:53:43 AM -158.396 -111.206 Right
10:53:44 AM 7.859224 10.88212 6.000019 14.4998 10:53:44 AM -158.389 -111.773 Right
10:53:45 AM -158.397 -110.244 Right
10:53:46 AM 7.911872 10.89171 6.000019 14.49997 10:53:46 AM -159.069 -113.59 Right
10:53:47 AM 7.966067 10.88703 6.000019 14.4998 10:53:47 AM -159.71 -111.984 Right
10:53:48 AM 8.021511 10.88443 6.000019 14.49996 10:53:48 AM -159.763 -110.955 Right
10:53:49 AM 8.072015 10.88268 6.000019 14.4998
10:53:50 AM 8.120579 10.88816 6.000019 14.49985 10:53:50 AM -158.223 -110.914 Right
10:53:51 AM 8.165326 10.89625 6.000019 14.4998 10:53:51 AM -154.928 -111.881 Right
10:53:52 AM 8.218029 10.90933 6.000019 14.49985 10:53:52 AM -153.377 -111.077 Right
10:53:53 AM 8.261042 10.92638 6.000019 14.49985 10:53:53 AM -154.02 -110.843 Right
10:53:54 AM 8.298958 10.93854 6.000019 14.49997 10:53:54 AM -151.259 -110.928 Right
10:53:55 AM 8.348174 10.96123 6.000019 14.49985 10:53:55 AM -150.58 -111.4 Right
10:53:56 AM -149.91 -110.882 Right
10:53:57 AM 8.388549 10.98051 6.000019 14.4998 10:53:57 AM -148.548 -112.013 Right
10:53:58 AM 8.427267 11.00776 6.000019 14.49985 10:53:58 AM -144.717 -112.751 Right
10:53:59 AM 8.473227 11.04639 6.000019 14.49997 10:53:59 AM -143.321 -110.777 Right
10:54:00 AM 8.506916 11.08338 6.000019 14.49985 10:54:00 AM -142.628 -111.792 Right
10:54:01 AM 8.542924 11.12478 6.000019 14.49997 10:54:01 AM -139.227 -110.664 Right
10:54:02 AM 8.568088 11.15876 6.000019 14.49996 10:54:02 AM -136.317 -109.043 Right
10:54:03 AM 8.591011 11.2053 6.000019 14.49985 10:54:03 AM -135.81 -106.855 Right
10:54:04 AM 8.610962 11.25364 6.000019 14.49997 10:54:04 AM -135.477 -106.463 Right
10:54:05 AM 8.623846 11.30002 6.000019 14.49996 10:54:05 AM -135.518 -105.044 Right
10:54:06 AM 8.646067 11.35177 6.000019 14.4998 10:54:06 AM -136.213 -106.328 Right
10:54:08 AM 8.662441 11.39136 6.000019 14.49997 10:54:08 AM -136.133 -104.911 Right
10:54:09 AM 8.676679 11.43199 6.000019 14.4998 10:54:09 AM -138.061 -104.817 Right
10:54:10 AM 8.694728 11.47722 6.000019 14.49997 10:54:10 AM -138.703 -105.996 Right
10:54:11 AM 8.715306 11.52419 6.000019 14.49985 10:54:11 AM -138.371 -105.558 Right
10:54:12 AM 8.732287 11.57507 6.000019 14.49997 10:54:12 AM -136.94 -104.998 Right
10:54:13 AM 8.754978 11.62936 6.000019 14.49985 10:54:13 AM -135.752 -107.284 Right
10:54:14 AM 8.767001 11.67349 6.000019 14.4998 10:54:14 AM -137.311 -105.438 Right
10:54:15 AM 8.779992 11.72279 6.000019 14.49996 10:54:15 AM -132.107 -107.746 Right
10:54:16 AM 8.787507 11.77128 6.000019 14.49996 10:54:16 AM -131.524 -105.908 Right
10:54:17 AM 8.791326 11.81702 6.000019 14.49996 10:54:17 AM -132.436 -107.511 Right
10:54:18 AM 8.793837 11.86645 6.000019 14.49996 10:54:18 AM -130.448 -107.309 Right
10:54:19 AM -128.027 -108.854 Right
10:54:20 AM 8.786958 11.91652 6.000019 14.49985 10:54:20 AM -127.804 -108.292 Right
10:54:21 AM 8.782507 11.96116 6.000019 14.49997 10:54:21 AM -127.025 -109.036 Right
10:54:22 AM 8.769834 12.00736 6.000019 14.49996 10:54:22 AM -117.526 -111.993 Right
10:54:23 AM 8.755476 12.04625 6.000019 14.49985 10:54:23 AM -121.104 -107.875 Right
10:54:24 AM 8.738588 12.08933 6.000019 14.49997 10:54:24 AM -123.031 -110.738 Right
10:54:25 AM 8.716022 12.14155 6.000019 14.49996 10:54:25 AM -113.126 -112.385 Right
10:54:26 AM 8.68796 12.18189 6.000019 14.49985
10:54:27 AM 8.681116 12.19664 6.000019 14.49997 10:54:27 AM -110.794 -113.232 Left
10:54:28 AM 8.621801 12.25184 6.000019 14.49996 10:54:28 AM -113.744 -114.925 Left
10:54:29 AM 8.373569 12.49205 6.000019 14.4998 10:54:29 AM -116.175 -111.088 Right
10:54:30 AM 8.140544 12.71758 6.000019 14.49996 10:54:30 AM -117.775 -111.673 Right
10:54:31 AM -110.326 -111.802 Left
10:54:32 AM 8.139504 12.71844 6.000019 14.49985 10:54:32 AM -110.243 -111.927 Left
10:54:33 AM 8.139413 12.71875 6.000019 14.49997 10:54:33 AM -110.108 -111.913 Left
10:54:34 AM 8.139585 12.71886 6.000019 14.49985 10:54:34 AM -110.14 -111.911 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
303
X Z X Z L R
10:52:40 AM 6 9.5 6 14.5
10:52:41 AM 6 9.5 6 14.5
10:52:42 AM 6 9.5 6 14.5
10:52:43 AM 5.990528 9.498259 6.000019 14.49985
10:52:44 AM 5.983474 9.48295 6.000019 14.49985
10:52:45 AM 5.964692 9.447685 6.000019 14.49997
10:52:46 AM -135.351 -112.688 Right
10:52:47 AM 5.976819 9.472078 6.000019 14.49997
10:52:48 AM 5.995047 9.528976 6.000019 14.49996 10:52:48 AM -132.694 -112.963 Right
10:52:49 AM 6.001012 9.544574 6.000019 14.49997 10:52:49 AM -129.037 -123.193 Right
10:52:50 AM 5.999889 9.54507 6.000019 14.49996 10:52:50 AM -130.817 -122.891 Right
10:52:51 AM 6.004016 9.72869 6.000019 14.4998 10:52:51 AM -133.053 -120.466 Right
10:52:52 AM 6.003926 9.733294 6.000019 14.49997 10:52:52 AM -131.333 -121.208 Right
10:52:53 AM 6.003814 9.891059 6.000019 14.49985 10:52:53 AM -130.561 -123.944 Right
10:52:54 AM 6.003565 10.23235 6.000019 14.49985 10:52:54 AM -130.185 -125.26 Right
10:52:55 AM 6.003317 10.57364 6.000019 14.49997 10:52:55 AM -123.365 -125.353 Left
10:52:56 AM 6.003062 10.92462 6.000019 14.4998 10:52:56 AM -114.681 -123.52 Left
10:52:57 AM 5.995727 11.01945 6.000019 14.49996 10:52:57 AM -108.929 -123.517 Left
10:52:58 AM -116.685 -114.294 Right
10:52:59 AM 5.987431 11.02194 6.000019 14.4998
10:53:00 AM 5.979954 11.02625 6.000019 14.49996 10:53:00 AM -125.151 -121.906 Right
10:53:01 AM 5.980797 11.04088 6.000019 14.49985 10:53:01 AM -126.948 -122.591 Right
10:53:02 AM 5.97635 11.04935 6.000019 14.49997 10:53:02 AM -129.48 -110.527 Right
10:53:03 AM 5.972887 11.05684 6.000019 14.4998 10:53:03 AM -135.907 -110.954 Right
10:53:04 AM 6.014514 11.08062 6.000019 14.4998 10:53:04 AM -140.432 -112.371 Right
10:53:05 AM 6.063323 11.10123 6.000019 14.4998 10:53:05 AM -142.056 -109.402 Right
10:53:06 AM 6.107279 11.10958 6.000019 14.49997 10:53:06 AM -146.553 -109.286 Right
10:53:07 AM 6.159662 11.10649 6.000019 14.49985 10:53:07 AM -155.076 -109.011 Right
10:53:08 AM 6.212564 11.09899 6.000019 14.49985 10:53:08 AM -155.751 -108.187 Right
10:53:09 AM -157.718 -109.506 Right
10:53:10 AM 6.25217 11.08564 6.000019 14.4998 10:53:10 AM -158.383 -108.117 Right
10:53:11 AM 6.29176 11.06707 6.000019 14.49997 10:53:11 AM -158.377 -106.647 Right
10:53:12 AM 6.333875 11.05068 6.000019 14.49996 10:53:12 AM -158.384 -107.564 Right
10:53:13 AM 6.381655 11.03246 6.000019 14.49996 10:53:13 AM -158.394 -108.101 Right
10:53:14 AM 6.417175 11.01487 6.000019 14.49996 10:53:14 AM -158.387 -109.07 Right
10:53:15 AM 6.461335 10.99534 6.000019 14.4998
10:53:16 AM 6.505663 10.97607 6.000019 14.49997 10:53:16 AM -158.054 -106.582 Right
10:53:17 AM 6.553511 10.96292 6.000019 14.49996 10:53:17 AM -158.308 -107.949 Right
10:53:18 AM 6.598693 10.9533 6.000019 14.49985 10:53:18 AM -157.61 -107.732 Right
10:53:19 AM 6.638103 10.94376 6.000019 14.49997 10:53:19 AM -156.833 -107.427 Right
10:53:20 AM 6.720123 10.93359 6.000019 14.49985 10:53:20 AM -157.147 -107.164 Right
10:53:21 AM -156.413 -109.544 Right
10:53:22 AM 6.757442 10.92128 6.000019 14.49997 10:53:22 AM -157.677 -107.981 Right
10:53:23 AM 6.813384 10.91371 6.000019 14.49985 10:53:23 AM -156.376 -108.387 Right
10:53:24 AM 6.875503 10.90206 6.000019 14.49996 10:53:24 AM -155.914 -109.041 Right
10:53:25 AM 6.929533 10.90117 6.000019 14.49985 10:53:25 AM -156.186 -110.265 Right
10:53:26 AM 6.986537 10.89872 6.000019 14.4998 10:53:26 AM -155.74 -109.148 Right
10:53:27 AM 7.038148 10.89448 6.000019 14.49997 10:53:27 AM -154.433 -109.864 Right
10:53:28 AM 7.083647 10.89595 6.000019 14.49985 10:53:28 AM -154.601 -112.082 Right
10:53:29 AM 7.128892 10.88597 6.000019 14.49985 10:53:29 AM -156.872 -109.209 Right
10:53:30 AM 7.179657 10.88296 6.000019 14.4998 10:53:30 AM -156.306 -109.468 Right
10:53:31 AM 7.23559 10.88592 6.000019 14.49996
10:53:32 AM 7.29436 10.88703 6.000019 14.49985 10:53:32 AM -155.111 -110.351 Right
10:53:33 AM -154.422 -109.447 Right
10:53:34 AM 7.348278 10.88949 6.000019 14.4998 10:53:34 AM -155.486 -112.373 Right
10:53:35 AM 7.405777 10.89291 6.000019 14.49996 10:53:35 AM -156.363 -107.649 Right
10:53:36 AM 7.458394 10.89381 6.000019 14.49997 10:53:36 AM -156.399 -108.95 Right
10:53:37 AM 7.506371 10.8902 6.000019 14.49996 10:53:37 AM -157.064 -111.3 Right
10:53:38 AM 7.558899 10.8893 6.000019 14.4998 10:53:38 AM -158.081 -110.418 Right
10:53:39 AM 7.601651 10.88891 6.000019 14.4998 10:53:39 AM -158.058 -109.881 Right
10:53:40 AM 7.653618 10.88245 6.000019 14.49996 10:53:40 AM -158.054 -111.747 Right
10:53:41 AM 7.704514 10.88886 6.000019 14.49997 10:53:41 AM -157.73 -110.094 Right
10:53:42 AM 7.750939 10.8865 6.000019 14.49996 10:53:42 AM -157.74 -111.849 Right
10:53:43 AM 7.807344 10.8842 6.000019 14.49997 10:53:43 AM -158.396 -111.206 Right
10:53:44 AM 7.859224 10.88212 6.000019 14.4998 10:53:44 AM -158.389 -111.773 Right
10:53:45 AM -158.397 -110.244 Right
10:53:46 AM 7.911872 10.89171 6.000019 14.49997 10:53:46 AM -159.069 -113.59 Right
10:53:47 AM 7.966067 10.88703 6.000019 14.4998 10:53:47 AM -159.71 -111.984 Right
10:53:48 AM 8.021511 10.88443 6.000019 14.49996 10:53:48 AM -159.763 -110.955 Right
10:53:49 AM 8.072015 10.88268 6.000019 14.4998
10:53:50 AM 8.120579 10.88816 6.000019 14.49985 10:53:50 AM -158.223 -110.914 Right
10:53:51 AM 8.165326 10.89625 6.000019 14.4998 10:53:51 AM -154.928 -111.881 Right
10:53:52 AM 8.218029 10.90933 6.000019 14.49985 10:53:52 AM -153.377 -111.077 Right
10:53:53 AM 8.261042 10.92638 6.000019 14.49985 10:53:53 AM -154.02 -110.843 Right
10:53:54 AM 8.298958 10.93854 6.000019 14.49997 10:53:54 AM -151.259 -110.928 Right
10:53:55 AM 8.348174 10.96123 6.000019 14.49985 10:53:55 AM -150.58 -111.4 Right
10:53:56 AM -149.91 -110.882 Right
10:53:57 AM 8.388549 10.98051 6.000019 14.4998 10:53:57 AM -148.548 -112.013 Right
10:53:58 AM 8.427267 11.00776 6.000019 14.49985 10:53:58 AM -144.717 -112.751 Right
10:53:59 AM 8.473227 11.04639 6.000019 14.49997 10:53:59 AM -143.321 -110.777 Right
10:54:00 AM 8.506916 11.08338 6.000019 14.49985 10:54:00 AM -142.628 -111.792 Right
10:54:01 AM 8.542924 11.12478 6.000019 14.49997 10:54:01 AM -139.227 -110.664 Right
10:54:02 AM 8.568088 11.15876 6.000019 14.49996 10:54:02 AM -136.317 -109.043 Right
10:54:03 AM 8.591011 11.2053 6.000019 14.49985 10:54:03 AM -135.81 -106.855 Right
10:54:04 AM 8.610962 11.25364 6.000019 14.49997 10:54:04 AM -135.477 -106.463 Right
10:54:05 AM 8.623846 11.30002 6.000019 14.49996 10:54:05 AM -135.518 -105.044 Right
10:54:06 AM 8.646067 11.35177 6.000019 14.4998 10:54:06 AM -136.213 -106.328 Right
10:54:08 AM 8.662441 11.39136 6.000019 14.49997 10:54:08 AM -136.133 -104.911 Right
10:54:09 AM 8.676679 11.43199 6.000019 14.4998 10:54:09 AM -138.061 -104.817 Right
10:54:10 AM 8.694728 11.47722 6.000019 14.49997 10:54:10 AM -138.703 -105.996 Right
10:54:11 AM 8.715306 11.52419 6.000019 14.49985 10:54:11 AM -138.371 -105.558 Right
10:54:12 AM 8.732287 11.57507 6.000019 14.49997 10:54:12 AM -136.94 -104.998 Right
10:54:13 AM 8.754978 11.62936 6.000019 14.49985 10:54:13 AM -135.752 -107.284 Right
10:54:14 AM 8.767001 11.67349 6.000019 14.4998 10:54:14 AM -137.311 -105.438 Right
10:54:15 AM 8.779992 11.72279 6.000019 14.49996 10:54:15 AM -132.107 -107.746 Right
10:54:16 AM 8.787507 11.77128 6.000019 14.49996 10:54:16 AM -131.524 -105.908 Right
10:54:17 AM 8.791326 11.81702 6.000019 14.49996 10:54:17 AM -132.436 -107.511 Right
10:54:18 AM 8.793837 11.86645 6.000019 14.49996 10:54:18 AM -130.448 -107.309 Right
10:54:19 AM -128.027 -108.854 Right
10:54:20 AM 8.786958 11.91652 6.000019 14.49985 10:54:20 AM -127.804 -108.292 Right
10:54:21 AM 8.782507 11.96116 6.000019 14.49997 10:54:21 AM -127.025 -109.036 Right
10:54:22 AM 8.769834 12.00736 6.000019 14.49996 10:54:22 AM -117.526 -111.993 Right
10:54:23 AM 8.755476 12.04625 6.000019 14.49985 10:54:23 AM -121.104 -107.875 Right
10:54:24 AM 8.738588 12.08933 6.000019 14.49997 10:54:24 AM -123.031 -110.738 Right
10:54:25 AM 8.716022 12.14155 6.000019 14.49996 10:54:25 AM -113.126 -112.385 Right
10:54:26 AM 8.68796 12.18189 6.000019 14.49985
10:54:27 AM 8.681116 12.19664 6.000019 14.49997 10:54:27 AM -110.794 -113.232 Left
10:54:28 AM 8.621801 12.25184 6.000019 14.49996 10:54:28 AM -113.744 -114.925 Left
10:54:29 AM 8.373569 12.49205 6.000019 14.4998 10:54:29 AM -116.175 -111.088 Right
10:54:30 AM 8.140544 12.71758 6.000019 14.49996 10:54:30 AM -117.775 -111.673 Right
10:54:31 AM -110.326 -111.802 Left
10:54:32 AM 8.139504 12.71844 6.000019 14.49985 10:54:32 AM -110.243 -111.927 Left
10:54:33 AM 8.139413 12.71875 6.000019 14.49997 10:54:33 AM -110.108 -111.913 Left
10:54:34 AM 8.139585 12.71886 6.000019 14.49985 10:54:34 AM -110.14 -111.911 Left
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
304
Non-Uniform Obstruction with Right Scatter Sample 4
Motion Path Graph – Non-Uniform obstruction with Right Scatter – Sample 4.
Combined logs – Non-Uniform obstruction with Right Scatter – Sample 4.
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
55.25.45.65.866.26.46.66.87
29-6-10-55
CARMI Child
X Z X Z L R
10:55:01 AM 6 9.5 6 14.5
10:55:02 AM 6 9.5 6 14.5
10:55:03 AM 6 9.5 6 14.5
10:55:05 AM 5.99464 9.501786 6.000019 14.49996
10:55:06 AM 6.006845 9.621773 6.000019 14.4998 10:55:06 AM -132.732 -120.382 Right
10:55:07 AM 6.00764 9.621551 6.000019 14.49997 10:55:07 AM -133.257 -119.024 Right
10:55:08 AM 6.00829 9.62191 6.000019 14.4998 10:55:08 AM -131.037 -119.924 Right
10:55:09 AM 6.015332 9.805927 6.000019 14.49996 10:55:09 AM -131.831 -120.684 Right
10:55:10 AM 6.027221 10.14706 6.000019 14.49985 10:55:10 AM -132.337 -124.046 Right
10:55:11 AM -127.567 -125.826 Right
10:55:12 AM 6.039108 10.48818 6.000019 14.4998 10:55:12 AM -119.081 -124.387 Left
10:55:13 AM 6.050994 10.82931 6.000019 14.49997 10:55:13 AM -109.898 -121.835 Left
10:55:14 AM 6.053479 11.04907 6.000019 14.4998 10:55:14 AM -111.485 -115.142 Left
10:55:15 AM 6.042463 11.05197 6.000019 14.49996 10:55:15 AM -120.429 -110.739 Right
10:55:16 AM 6.014875 11.0544 6.000019 14.4998
10:55:17 AM 6.008541 11.06881 6.000019 14.49997 10:55:17 AM -132.948 -108.783 Right
10:55:18 AM 6.058201 11.09011 6.000019 14.4998 10:55:18 AM -141.23 -109.531 Right
10:55:19 AM 6.106407 11.10443 6.000019 14.49996 10:55:19 AM -144.5 -110.117 Right
10:55:20 AM 6.154459 11.11225 6.000019 14.49985 10:55:20 AM -144.966 -107.954 Right
10:55:21 AM -151.79 -109.332 Right
10:55:22 AM 6.201403 11.11016 6.000019 14.49985 10:55:22 AM -155.436 -109.908 Right
10:55:23 AM 6.257267 11.10237 6.000019 14.4998 10:55:23 AM -156.397 -108.47 Right
10:55:24 AM 6.311574 11.07715 6.000019 14.49996 10:55:24 AM 121 Recenter-Right
10:55:25 AM 6.33921 11.06219 6.000019 14.49997 10:55:25 AM -159.71 -108.207 Right
10:55:26 AM 6.339808 11.06081 6.000019 14.49996 10:55:26 AM -155.983 -107.931 Right
10:55:27 AM 6.340755 11.05966 6.000019 14.4998 10:55:27 AM -155.426 -108.139 Right
10:55:28 AM 6.340847 11.05806 6.000019 14.49997 10:55:28 AM -155.548 -108.232 Right
10:55:29 AM 6.341109 11.05728 6.000019 14.49985 10:55:29 AM -155.989 -107.883 Right
10:55:30 AM 6.340553 11.05637 6.000019 14.4998
10:55:31 AM 6.341751 11.0553 6.000019 14.49997 10:55:31 AM -156.481 -107.382 Right
10:55:32 AM 6.342431 11.05495 6.000019 14.49985 10:55:32 AM -156.302 -107.033 Right
10:55:33 AM -155.435 -107.111 Right
10:55:34 AM 6.342547 11.05361 6.000019 14.4998 10:55:34 AM -154.012 -107.586 Right
10:55:35 AM 6.342362 11.05349 6.000019 14.49996 10:55:35 AM -154.3 -107.257 Right
10:55:36 AM 6.342402 11.05256 6.000019 14.49996 10:55:36 AM -154.509 -107.1 Right
10:55:37 AM 6.342904 11.05222 6.000019 14.49985 10:55:37 AM -154.401 -107.262 Right
10:55:38 AM 6.342176 11.05195 6.000019 14.49997 10:55:38 AM -153.941 -107.888 Right
10:55:39 AM 6.344245 11.05095 6.000019 14.49997 10:55:39 AM -152.166 -107.229 Right
10:55:40 AM 6.344777 11.05007 6.000019 14.49985 10:55:40 AM -152.037 -107.299 Right
10:55:41 AM 6.345498 11.04932 6.000019 14.49996 10:55:41 AM -152.316 -107.51 Right
10:55:42 AM 6.346025 11.0486 6.000019 14.49985 10:55:42 AM -152.249 -107.796 Right
10:55:43 AM 6.346815 11.04809 6.000019 14.4998 10:55:43 AM -152.238 -108.433 Right
10:55:44 AM 6.347118 11.04744 6.000019 14.49996 10:55:44 AM -151.463 -108.472 Right
10:55:45 AM -150.587 -107.691 Right
10:55:46 AM 6.347539 11.04639 6.000019 14.49996 10:55:46 AM -150.645 -108.084 Right
10:55:47 AM 6.348439 11.0453 6.000019 14.49997
10:55:48 AM 6.348692 11.04462 6.000019 14.49985 10:55:48 AM -150.529 -108.861 Right
10:55:49 AM 6.348737 11.04416 6.000019 14.49997 10:55:49 AM -150.049 -109.66 Right
10:55:50 AM 6.348502 11.04354 6.000019 14.49997 10:55:50 AM -148.899 -108.117 Right
10:55:51 AM 6.349028 11.04311 6.000019 14.49997 10:55:51 AM -148.956 -108.633 Right
10:55:52 AM 6.350311 11.04244 6.000019 14.49985 10:55:52 AM -148.994 -109.037 Right
10:55:53 AM 6.350255 11.04188 6.000019 14.4998 10:55:53 AM -148.675 -110.084 Right
10:55:54 AM 6.350607 11.04123 6.000019 14.49997 10:55:54 AM -148.408 -110.438 Right
10:55:55 AM 6.351353 11.04047 6.000019 14.49985 10:55:55 AM -146.97 -109.437 Right
10:55:56 AM -146.982 -109.519 Right
10:55:57 AM 6.352343 11.04001 6.000019 14.49997 10:55:57 AM -147.318 -110.058 Right
10:55:58 AM 6.352273 11.03846 6.000019 14.49996 10:55:58 AM -147.105 -110.726 Right
10:55:59 AM 6.352028 11.03798 6.000019 14.4998 10:55:59 AM -146.174 -111.632 Right
10:56:00 AM 6.351964 11.03701 6.000019 14.4998 10:56:00 AM -145.251 -109.969 Right
10:56:01 AM 6.350976 11.03564 6.000019 14.49996 10:56:01 AM -145.345 -110.616 Right
10:56:02 AM 6.351787 11.03488 6.000019 14.4998 10:56:02 AM -145.292 -111.425 Right
10:56:03 AM 6.35277 11.03427 6.000019 14.49997 10:56:03 AM -144.874 -112.449 Right
10:56:04 AM 6.352327 11.03283 6.000019 14.49996
10:56:05 AM 6.352884 11.03106 6.000019 14.49985 10:56:05 AM -143.681 -111.995 Right
10:56:06 AM 6.353895 11.02998 6.000019 14.49997 10:56:06 AM -143.014 -111.851 Right
10:56:07 AM 6.354368 11.0292 6.000019 14.49997 10:56:07 AM -143.284 -112.98 Right
10:56:08 AM -142.688 -114.614 Right
10:56:09 AM 6.354547 11.02813 6.000019 14.49997 10:56:09 AM -141.341 -114.004 Right
10:56:10 AM 6.354764 11.02725 6.000019 14.49996 10:56:10 AM -141.029 -113.367 Right
10:56:11 AM 6.355392 11.02581 6.000019 14.49997 10:56:11 AM -140.585 -116.503 Right
10:56:12 AM 6.356277 11.02415 6.000019 14.49996
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
305
X Z X Z L R
10:55:01 AM 6 9.5 6 14.5
10:55:02 AM 6 9.5 6 14.5
10:55:03 AM 6 9.5 6 14.5
10:55:05 AM 5.99464 9.501786 6.000019 14.49996
10:55:06 AM 6.006845 9.621773 6.000019 14.4998 10:55:06 AM -132.732 -120.382 Right
10:55:07 AM 6.00764 9.621551 6.000019 14.49997 10:55:07 AM -133.257 -119.024 Right
10:55:08 AM 6.00829 9.62191 6.000019 14.4998 10:55:08 AM -131.037 -119.924 Right
10:55:09 AM 6.015332 9.805927 6.000019 14.49996 10:55:09 AM -131.831 -120.684 Right
10:55:10 AM 6.027221 10.14706 6.000019 14.49985 10:55:10 AM -132.337 -124.046 Right
10:55:11 AM -127.567 -125.826 Right
10:55:12 AM 6.039108 10.48818 6.000019 14.4998 10:55:12 AM -119.081 -124.387 Left
10:55:13 AM 6.050994 10.82931 6.000019 14.49997 10:55:13 AM -109.898 -121.835 Left
10:55:14 AM 6.053479 11.04907 6.000019 14.4998 10:55:14 AM -111.485 -115.142 Left
10:55:15 AM 6.042463 11.05197 6.000019 14.49996 10:55:15 AM -120.429 -110.739 Right
10:55:16 AM 6.014875 11.0544 6.000019 14.4998
10:55:17 AM 6.008541 11.06881 6.000019 14.49997 10:55:17 AM -132.948 -108.783 Right
10:55:18 AM 6.058201 11.09011 6.000019 14.4998 10:55:18 AM -141.23 -109.531 Right
10:55:19 AM 6.106407 11.10443 6.000019 14.49996 10:55:19 AM -144.5 -110.117 Right
10:55:20 AM 6.154459 11.11225 6.000019 14.49985 10:55:20 AM -144.966 -107.954 Right
10:55:21 AM -151.79 -109.332 Right
10:55:22 AM 6.201403 11.11016 6.000019 14.49985 10:55:22 AM -155.436 -109.908 Right
10:55:23 AM 6.257267 11.10237 6.000019 14.4998 10:55:23 AM -156.397 -108.47 Right
10:55:24 AM 6.311574 11.07715 6.000019 14.49996 10:55:24 AM 121 Recenter-Right
10:55:25 AM 6.33921 11.06219 6.000019 14.49997 10:55:25 AM -159.71 -108.207 Right
10:55:26 AM 6.339808 11.06081 6.000019 14.49996 10:55:26 AM -155.983 -107.931 Right
10:55:27 AM 6.340755 11.05966 6.000019 14.4998 10:55:27 AM -155.426 -108.139 Right
10:55:28 AM 6.340847 11.05806 6.000019 14.49997 10:55:28 AM -155.548 -108.232 Right
10:55:29 AM 6.341109 11.05728 6.000019 14.49985 10:55:29 AM -155.989 -107.883 Right
10:55:30 AM 6.340553 11.05637 6.000019 14.4998
10:55:31 AM 6.341751 11.0553 6.000019 14.49997 10:55:31 AM -156.481 -107.382 Right
10:55:32 AM 6.342431 11.05495 6.000019 14.49985 10:55:32 AM -156.302 -107.033 Right
10:55:33 AM -155.435 -107.111 Right
10:55:34 AM 6.342547 11.05361 6.000019 14.4998 10:55:34 AM -154.012 -107.586 Right
10:55:35 AM 6.342362 11.05349 6.000019 14.49996 10:55:35 AM -154.3 -107.257 Right
10:55:36 AM 6.342402 11.05256 6.000019 14.49996 10:55:36 AM -154.509 -107.1 Right
10:55:37 AM 6.342904 11.05222 6.000019 14.49985 10:55:37 AM -154.401 -107.262 Right
10:55:38 AM 6.342176 11.05195 6.000019 14.49997 10:55:38 AM -153.941 -107.888 Right
10:55:39 AM 6.344245 11.05095 6.000019 14.49997 10:55:39 AM -152.166 -107.229 Right
10:55:40 AM 6.344777 11.05007 6.000019 14.49985 10:55:40 AM -152.037 -107.299 Right
10:55:41 AM 6.345498 11.04932 6.000019 14.49996 10:55:41 AM -152.316 -107.51 Right
10:55:42 AM 6.346025 11.0486 6.000019 14.49985 10:55:42 AM -152.249 -107.796 Right
10:55:43 AM 6.346815 11.04809 6.000019 14.4998 10:55:43 AM -152.238 -108.433 Right
10:55:44 AM 6.347118 11.04744 6.000019 14.49996 10:55:44 AM -151.463 -108.472 Right
10:55:45 AM -150.587 -107.691 Right
10:55:46 AM 6.347539 11.04639 6.000019 14.49996 10:55:46 AM -150.645 -108.084 Right
10:55:47 AM 6.348439 11.0453 6.000019 14.49997
10:55:48 AM 6.348692 11.04462 6.000019 14.49985 10:55:48 AM -150.529 -108.861 Right
10:55:49 AM 6.348737 11.04416 6.000019 14.49997 10:55:49 AM -150.049 -109.66 Right
10:55:50 AM 6.348502 11.04354 6.000019 14.49997 10:55:50 AM -148.899 -108.117 Right
10:55:51 AM 6.349028 11.04311 6.000019 14.49997 10:55:51 AM -148.956 -108.633 Right
10:55:52 AM 6.350311 11.04244 6.000019 14.49985 10:55:52 AM -148.994 -109.037 Right
10:55:53 AM 6.350255 11.04188 6.000019 14.4998 10:55:53 AM -148.675 -110.084 Right
10:55:54 AM 6.350607 11.04123 6.000019 14.49997 10:55:54 AM -148.408 -110.438 Right
10:55:55 AM 6.351353 11.04047 6.000019 14.49985 10:55:55 AM -146.97 -109.437 Right
10:55:56 AM -146.982 -109.519 Right
10:55:57 AM 6.352343 11.04001 6.000019 14.49997 10:55:57 AM -147.318 -110.058 Right
10:55:58 AM 6.352273 11.03846 6.000019 14.49996 10:55:58 AM -147.105 -110.726 Right
10:55:59 AM 6.352028 11.03798 6.000019 14.4998 10:55:59 AM -146.174 -111.632 Right
10:56:00 AM 6.351964 11.03701 6.000019 14.4998 10:56:00 AM -145.251 -109.969 Right
10:56:01 AM 6.350976 11.03564 6.000019 14.49996 10:56:01 AM -145.345 -110.616 Right
10:56:02 AM 6.351787 11.03488 6.000019 14.4998 10:56:02 AM -145.292 -111.425 Right
10:56:03 AM 6.35277 11.03427 6.000019 14.49997 10:56:03 AM -144.874 -112.449 Right
10:56:04 AM 6.352327 11.03283 6.000019 14.49996
10:56:05 AM 6.352884 11.03106 6.000019 14.49985 10:56:05 AM -143.681 -111.995 Right
10:56:06 AM 6.353895 11.02998 6.000019 14.49997 10:56:06 AM -143.014 -111.851 Right
10:56:07 AM 6.354368 11.0292 6.000019 14.49997 10:56:07 AM -143.284 -112.98 Right
10:56:08 AM -142.688 -114.614 Right
10:56:09 AM 6.354547 11.02813 6.000019 14.49997 10:56:09 AM -141.341 -114.004 Right
10:56:10 AM 6.354764 11.02725 6.000019 14.49996 10:56:10 AM -141.029 -113.367 Right
10:56:11 AM 6.355392 11.02581 6.000019 14.49997 10:56:11 AM -140.585 -116.503 Right
10:56:12 AM 6.356277 11.02415 6.000019 14.49996
Referee Log PathDecider Log
TimeStamp
CARMI Child
TimeStamp
Tendencies
Verdict
306
Local Minima Test Result Dataset
Combine logs for the Local Minima Scenario
Referee Log PathDecider Log
TimeStamp CARMI Child TimeStamp Tendencies Verdict
X Z X Z L R
1:51:19 AM 6 9.5 6 14.5
1:51:20 AM 6 9.5 6 14.5
1:51:22 AM 6 9.5 6 14.5
1:51:23 AM 6 9.5 6 14.5
1:51:24 AM 5.997074 9.500979 6.000019 14.49985
1:51:25 AM 5.998401 9.538817 6.000019 14.49985 1:51:25 AM -29.0057 -15.1729 Right
1:51:26 AM 6.00367 9.555366 6.000019 14.49985 1:51:26 AM -33.5997 -17.3274 Right
1:51:27 AM 6.006526 9.557668 6.000019 14.49997 1:51:27 AM -22.5675 -17.3554 Right
1:51:28 AM 6.012127 9.648153 6.000019 14.49985 1:51:28 AM -19.8175 -15.9363 Right
1:51:29 AM 6.029491 9.987835 6.000019 14.4998
1:51:30 AM 6.046899 10.32726 6.000019 14.49997 1:51:30 AM -21.6316 -18.2782 Right
1:51:31 AM 6.064361 10.66666 6.000019 14.49996 1:51:31 AM -26.7912 -23.9104 Right
1:51:32 AM 6.081869 11.00602 6.000019 14.49996 1:51:32 AM -39.3131 -38.2815 Right
1:51:33 AM -101.506 -108.018 Left
1:51:34 AM 6.073533 11.05243 6.000019 14.49985 1:51:34 AM -68.2009 -153.093 Left
1:51:35 AM 6.064908 11.0612 6.000019 14.4998 1:51:35 AM -99.0617 -176.442 Left
1:51:36 AM 6.058323 11.07219 6.000019 14.49996 1:51:36 AM -107.03 -206.2 Left
1:51:37 AM 6.053562 11.08579 6.000019 14.4998 1:51:37 AM -110.555 -230.716 Left
1:51:38 AM 6.064655 11.10001 6.000019 14.49997 1:51:38 AM -112.048 -231.451 Left
1:51:39 AM 6.1245 11.1245 6.000019 14.49985 1:51:39 AM -120.252 -232.122 Left
1:51:40 AM 6.171119 11.13704 6.000019 14.49997 1:51:40 AM -132.594 -218.977 Left
1:51:41 AM 6.232832 11.13502 6.000019 14.49985
1:51:42 AM 6.28772 11.12419 6.000019 14.49997 1:51:42 AM -144.502 -147.204 Left
1:51:43 AM 6.341294 11.10684 6.000019 14.49997 1:51:43 AM -157.182 -99.5754 Right
1:51:44 AM -165.578 -30.0441 Right
1:51:45 AM 6.390291 11.08646 6.000019 14.49985 1:51:45 AM -166.838 -75.06 Right
1:51:46 AM 6.427298 11.0713 6.000019 14.4998 1:51:46 AM -166.784 -77.3409 Right
1:51:47 AM 6.473382 11.0588 6.000019 14.49985 1:51:47 AM -162.965 -68.358 Right
1:51:48 AM 6.52168 11.03084 6.000019 14.4998 1:51:48 AM 121 Recenter-Right
1:51:49 AM 6.548649 11.01481 6.000019 14.49985 1:51:49 AM 124 Recenter-Right
1:51:50 AM 6.547762 11.0135 6.000019 14.49996 1:51:50 AM -156.974 -71.7713 Right
1:51:51 AM 6.548935 11.00974 6.000019 14.49996 1:51:51 AM -151.843 -81.6761 Right
1:51:52 AM 6.551022 11.0071 6.000019 14.49996 1:51:52 AM -151.541 -84.7626 Right
1:51:53 AM 6.551851 11.00519 6.000019 14.49985 1:51:53 AM -151.526 -90.6611 Right
307
1:51:54 AM 6.554352 11.00197 6.000019 14.49997 1:51:54 AM -146.308 -97.8878 Right
1:51:55 AM 6.55461 10.99957 6.000019 14.49996 1:51:55 AM -140.979 -101.76 Right
1:51:56 AM 6.556025 10.99797 6.000019 14.49997 1:51:56 AM -141.889 -105.246 Right
1:51:57 AM -138.373 -105.58 Right
1:51:58 AM 6.557708 10.99454 6.000019 14.49997
1:51:59 AM 6.557666 10.99328 6.000019 14.49997 1:51:59 AM -131.936 -109.814 Right
1:52:00 AM 6.559336 10.99023 6.000019 14.49985 1:52:00 AM -134.938 -109.825 Right
1:52:01 AM 6.561836 10.98822 6.000019 14.49997 1:52:01 AM -131.356 -109.878 Right
1:52:02 AM 6.563061 10.98617 6.000019 14.49996 1:52:02 AM -127.084 -114.562 Right
1:52:03 AM 6.564486 10.98434 6.000019 14.49997 1:52:03 AM -125.99 -110.029 Right
1:52:04 AM 6.566859 10.98199 6.000019 14.4998 1:52:04 AM -129.545 -113.996 Right
1:52:05 AM 6.569016 10.98002 6.000019 14.49997 1:52:05 AM -125.631 -114.956 Right
1:52:06 AM 6.570813 10.97804 6.000019 14.49996 1:52:06 AM -123.292 -116.024 Right
1:52:07 AM 6.572488 10.9769 6.000019 14.4998 1:52:07 AM -125.25 -113.466 Right
1:52:08 AM 6.574524 10.97521 6.000019 14.49996 1:52:08 AM -122.625 -115.393 Right
1:52:09 AM -118.959 -112.457 Right
1:52:10 AM 6.576846 10.97412 6.000019 14.49985 1:52:10 AM -117.667 -114.244 Right
1:52:11 AM 6.579556 10.97285 6.000019 14.49985 1:52:11 AM -118.379 -110.556 Right
1:52:12 AM 6.58041 10.97136 6.000019 14.49997 1:52:12 AM -114.753 -111.195 Right
1:52:13 AM 6.583171 10.97096 6.000019 14.49996 1:52:13 AM -113.136 -109.027 Right
1:52:14 AM 6.585874 10.96978 6.000019 14.49985
1:52:15 AM 6.589977 10.96939 6.000019 14.4998 1:52:15 AM -113.693 -106.551 Right
1:52:16 AM 6.5924 10.96931 6.000019 14.4998 1:52:16 AM -106.56 -104.324 Right
1:52:17 AM 6.596512 10.96926 6.000019 14.49985 1:52:17 AM -102.795 -102.229 Right
1:52:18 AM 6.598096 10.96913 6.000019 14.4998 1:52:18 AM -103.265 -99.5557 Right
1:52:19 AM 6.598648 10.96869 6.000019 14.49997 1:52:19 AM -96.7862 -99.5568 Left
1:52:20 AM 6.600674 10.96803 6.000019 14.49985 1:52:20 AM -96.794 -99.5349 Left
1:52:21 AM -98.1552 -98.1942 Left
1:52:22 AM 6.602701 10.96889 6.000019 14.4998 1:52:22 AM -94.9518 -95.8136 Left
1:52:23 AM 6.60552 10.96992 6.000019 14.49996 1:52:23 AM -93.0473 -93.5202 Left
1:52:24 AM 6.607229 10.96921 6.000019 14.49996 1:52:24 AM -95.7424 -89.3737 Right
1:52:25 AM 6.609035 10.96995 6.000019 14.49996 1:52:25 AM -91.5863 -87.4561 Right
1:52:26 AM 6.610603 10.97059 6.000019 14.49996 1:52:26 AM -94.3097 -85.8247 Right
1:52:27 AM 6.613195 10.97157 6.000019 14.4998 1:52:27 AM -93.4186 -85.3743 Right
1:52:28 AM 6.616239 10.97251 6.000019 14.49985 1:52:28 AM -90.5772 -80.9013 Right
1:52:29 AM 6.618249 10.9736 6.000019 14.49985 1:52:29 AM -94.829 -77.6625 Right
1:52:30 AM 6.620165 10.97572 6.000019 14.4998
1:52:31 AM 6.621376 10.97638 6.000019 14.49985 1:52:31 AM -95.8926 -74.8453 Right
1:52:32 AM 6.62289 10.97761 6.000019 14.4998 1:52:32 AM -91.158 -73.7209 Right
1:52:33 AM 6.624775 10.97787 6.000019 14.4998 1:52:33 AM -94.1889 -71.2993 Right
1:52:34 AM -95.6216 -69.0873 Right
308
1:52:35 AM 6.626234 10.97903 6.000019 14.49997 1:52:35 AM -93.9236 -67.5178 Right
1:52:36 AM 6.63363 10.98716 6.000019 14.49996 1:52:36 AM -111.039 -47.8742 Right
1:52:37 AM 6.643041 10.9958 6.000019 14.49985 1:52:37 AM -123.153 -66.4864 Right
1:52:38 AM 6.648644 11.00886 6.000019 14.49996 1:52:38 AM -137.975 -83.6072 Right
1:52:39 AM 6.651331 11.02271 6.000019 14.4998 1:52:39 AM -131.944 -79.5927 Right
1:52:40 AM 6.61236 11.04557 6.000019 14.49985 1:52:40 AM -146.173 -98.9936 Right
1:52:41 AM 6.572775 11.05748 6.000019 14.4998 1:52:41 AM -138.816 -105.585 Right
1:52:42 AM 6.526691 11.06667 6.000019 14.4998 1:52:42 AM -126.642 -112.235 Right
1:52:43 AM 6.478302 11.06718 6.000019 14.49997 1:52:43 AM -97.7547 -123.433 Left
1:52:44 AM 6.417551 11.0624 6.000019 14.4998 1:52:44 AM -68.2804 -135.3 Left
1:52:45 AM 6.349144 11.03582 6.000019 14.49997
1:52:46 AM -56.2662 -147.393 Left
1:52:47 AM 6.306427 11.01443 6.000019 14.49985 1:52:47 AM -52.1288 -149.342 Left
1:52:48 AM 6.260409 11.0026 6.000019 14.49985 1:52:48 AM -56.1821 -143.883 Left
1:52:49 AM 6.201631 10.97643 6.000019 14.4998 1:52:49 AM -50.8526 -143.024 Left
1:52:50 AM 6.168406 10.96397 6.000019 14.49996 1:52:50 AM -55.2349 -147.046 Left
1:52:51 AM 6.11854 10.95276 6.000019 14.4998 1:52:51 AM -54.1744 -139.75 Left
1:52:52 AM 6.076892 10.94891 6.000019 14.49985 1:52:52 AM -51.6418 -138.903 Left
1:52:53 AM 6.0286 10.94015 6.000019 14.4998 1:52:53 AM -57.3512 -139.283 Left
1:52:54 AM 5.958687 10.92706 6.000019 14.49985 1:52:54 AM -56.1437 -139.51 Left
1:52:55 AM 5.903365 10.91479 6.000019 14.4998 1:52:55 AM -58.0218 -140.735 Left
1:52:56 AM 5.850225 10.91505 6.000019 14.49997 1:52:56 AM -54.0321 -136.25 Left
1:52:57 AM 5.780518 10.9062 6.000019 14.49996
1:52:58 AM -57.0427 -137.634 Left
1:52:59 AM 5.732518 10.90266 6.000019 14.49985 1:52:59 AM -54.6161 -137.817 Left
1:53:00 AM 5.685184 10.90153 6.000019 14.49985 1:53:00 AM -53.7877 -138.206 Left
1:53:01 AM 5.636422 10.89761 6.000019 14.4998 1:53:01 AM -55.3123 -141.307 Left
1:53:02 AM 5.585737 10.89337 6.000019 14.4998 1:53:02 AM -55.7246 -139.886 Left
1:53:03 AM 5.52403 10.8875 6.000019 14.49997 1:53:03 AM -60.6025 -140.763 Left
1:53:04 AM 5.478683 10.88959 6.000019 14.4998 1:53:04 AM -54.0615 -140.834 Left
1:53:05 AM 5.432499 10.88104 6.000019 14.49996 1:53:05 AM -60.4018 -148.573 Left
1:53:06 AM 5.382638 10.88239 6.000019 14.49985
1:53:07 AM 5.334692 10.88439 6.000019 14.4998 1:53:07 AM -62.5412 -144.644 Left
1:53:08 AM 5.287015 10.88934 6.000019 14.49985 1:53:08 AM -61.6562 -145.279 Left
1:53:09 AM 5.219532 10.89126 6.000019 14.4998 1:53:09 AM -60.454 -146.228 Left
1:53:10 AM 5.15249 10.88888 6.000019 14.49997 1:53:10 AM -62.9963 -153.653 Left
1:53:11 AM -65.9307 -155.404 Left
1:53:12 AM 5.093613 10.8905 6.000019 14.49996 1:53:12 AM -74.3723 -156.662 Left
1:53:13 AM 5.04456 10.89211 6.000019 14.49985 1:53:13 AM -87.4061 -162.053 Left
1:53:14 AM 4.996026 10.88948 6.000019 14.4998 1:53:14 AM -126 Recenter-Left
1:53:15 AM 4.991217 10.88767 6.000019 14.49996 1:53:15 AM -130 Recenter-Left
309
1:53:16 AM 4.990352 10.88381 6.000019 14.49996 1:53:16 AM -124 Recenter-Left
1:53:17 AM 4.99016 10.88246 6.000019 14.4998 1:53:17 AM -121 Recenter-Left
1:53:18 AM 4.989205 10.88002 6.000019 14.49996 1:53:18 AM -102.304 -177.617 Left
1:53:19 AM 4.988693 10.87982 6.000019 14.49996 1:53:19 AM -102.17 -31.9602 Right
1:53:20 AM 4.980302 10.88846 6.000019 14.49997 1:53:20 AM -105.96 -28.6357 Right
1:53:21 AM 4.974521 10.8948 6.000019 14.49985 1:53:21 AM -99.0141 -15.0242 Right
1:53:22 AM 4.985075 10.88896 6.000019 14.49985
1:53:23 AM -104.962 -27.5926 Right
1:53:24 AM 4.990876 10.87805 6.000019 14.49997 1:53:24 AM -99.4879 -33.9593 Right
1:53:25 AM 4.992637 10.86631 6.000019 14.49997 1:53:25 AM -92.1063 -36.4425 Right
1:53:26 AM 4.99206 10.85928 6.000019 14.4998 1:53:26 AM -82.3122 -56.3929 Right
1:53:27 AM 4.987722 10.84992 6.000019 14.49997 1:53:27 AM -78.8108 -71.4661 Right
1:53:28 AM 4.980475 10.84083 6.000019 14.49996 1:53:28 AM -73.5009 -83.6646 Left
1:53:29 AM 4.975312 10.82903 6.000019 14.4998 1:53:29 AM -68.0154 -90.709 Left
1:53:30 AM 4.968103 10.81765 6.000019 14.49996 1:53:30 AM -55.5609 -93.1641 Left
1:53:31 AM 4.95587 10.81778 6.000019 14.49996 1:53:31 AM -43.5889 -97.0433 Left
1:53:32 AM 4.959801 10.85633 6.000019 14.4998 1:53:32 AM -43.7389 -106.511 Left
1:53:33 AM 4.976748 10.92349 6.000019 14.49996 1:53:33 AM -47.392 -112.999 Left
1:53:34 AM 4.982579 10.94744 6.000019 14.4998 1:53:34 AM -39.3973 -119.776 Left
1:53:35 AM -51.5808 -104.772 Left
1:53:36 AM 5.0058 10.98978 6.000019 14.49997 1:53:36 AM -69.5573 -117.347 Left
1:53:37 AM 5.014694 11.00246 6.000019 14.49985 1:53:37 AM -78.1664 -152.557 Left
1:53:38 AM 5.059988 11.04233 6.000019 14.49985
1:53:39 AM 5.100826 11.07482 6.000019 14.4998 1:53:39 AM -84.2212 -194.002 Left
1:53:40 AM 5.149418 11.10312 6.000019 14.4998 1:53:40 AM -81.3581 -200.172 Left
1:53:41 AM 5.20497 11.11898 6.000019 14.49997 1:53:41 AM -95.2547 -200.254 Left
1:53:42 AM 5.255413 11.12372 6.000019 14.49997 1:53:42 AM -112.726 -187.776 Left
1:53:43 AM 5.306905 11.12421 6.000019 14.49985 1:53:43 AM -119.38 -128.332 Left
1:53:44 AM 5.371315 11.10587 6.000019 14.4998 1:53:44 AM -137.001 -15.3879 Right
1:53:45 AM 5.439966 11.08478 6.000019 14.49996 1:53:45 AM -141.609 -73.4359 Right
1:53:46 AM 5.493051 11.06112 6.000019 14.49996 1:53:46 AM -143.465 -13.569 Right
1:53:47 AM 5.538978 11.04218 6.000019 14.4998 1:53:47 AM -143.472 -13.8734 Right
1:53:48 AM -143.501 -14.3689 Right
1:53:49 AM 5.597509 11.01698 6.000019 14.49996 1:53:49 AM -144.594 -52.0097 Right
1:53:50 AM 5.649308 10.99419 6.000019 14.4998 1:53:50 AM -143.828 -51.0071 Right
1:53:51 AM 5.70221 10.97442 6.000019 14.49996 1:53:51 AM -142.183 -54.9048 Right
1:53:52 AM 5.744753 10.96664 6.000019 14.49997 1:53:52 AM -138.585 -53.8142 Right
1:53:53 AM 5.807028 10.94724 6.000019 14.4998
1:53:54 AM 5.846809 10.93905 6.000019 14.49996 1:53:54 AM -138.739 -52.7229 Right
1:53:55 AM 5.924026 10.91969 6.000019 14.49985 1:53:55 AM -136.974 -50.4903 Right
1:53:56 AM 5.978089 10.91452 6.000019 14.49997 1:53:56 AM -134.698 -56.2263 Right
310
1:53:57 AM 6.043155 10.90553 6.000019 14.49985 1:53:57 AM -133.75 -51.0146 Right
1:53:58 AM 6.087174 10.90328 6.000019 14.4998 1:53:58 AM -134.019 -55.0196 Right
1:53:59 AM 6.14807 10.89994 6.000019 14.49985 1:53:59 AM -134.2 -54.074 Right
1:54:00 AM -134.435 -51.9361 Right
1:54:01 AM 6.194035 10.88963 6.000019 14.4998 1:54:01 AM -136.688 -57.2412 Right
1:54:02 AM 6.249125 10.89418 6.000019 14.49996 1:54:02 AM -135.816 -54.8457 Right
1:54:03 AM 6.309861 10.88464 6.000019 14.49996 1:54:03 AM -136.301 -56.5503 Right
1:54:04 AM 6.355663 10.88891 6.000019 14.4998 1:54:04 AM -136.455 -53.5466 Right
1:54:05 AM 6.402854 10.88107 6.000019 14.49997 1:54:05 AM -139.855 -58.6116 Right
1:54:06 AM 6.464009 10.88349 6.000019 14.49985 1:54:06 AM -140.498 -59.3407 Right
1:54:07 AM 6.511384 10.8805 6.000019 14.49996 1:54:07 AM -140.995 -58.4003 Right
1:54:08 AM 6.564715 10.88709 6.000019 14.4998 1:54:08 AM -142.051 -53.5457 Right
1:54:09 AM 6.607385 10.88495 6.000019 14.49997 1:54:09 AM -145.704 -59.4671 Right
1:54:10 AM 6.657025 10.8846 6.000019 14.49996 1:54:10 AM -146.667 -60.2929 Right
1:54:11 AM 6.702734 10.88588 6.000019 14.49996
1:54:12 AM -145.24 -59.9032 Right
1:54:13 AM 6.763561 10.88866 6.000019 14.4998 1:54:13 AM -149.631 -62.8854 Right
1:54:14 AM 6.806781 10.88923 6.000019 14.49997 1:54:14 AM -154.306 -62.5465 Right
1:54:15 AM 6.859155 10.88654 6.000019 14.49985 1:54:15 AM -156.043 -65.5184 Right
1:54:16 AM 6.907557 10.88963 6.000019 14.49997 1:54:16 AM -158.798 -73.7818 Right
1:54:17 AM 6.955798 10.88258 6.000019 14.49996 1:54:17 AM -169.409 -82.7007 Right
1:54:18 AM 6.997734 10.87064 6.000019 14.49985 1:54:18 AM 126 Recenter-Right
1:54:19 AM 6.997715 10.86932 6.000019 14.49996 1:54:19 AM 127 Recenter-Right
1:54:20 AM 6.996756 10.86789 6.000019 14.49997 1:54:20 AM 122 Recenter-Right
311
APPENDIX F – BENCHMARK SCENARIOS SIMULATION RESULTS
Benchmark 1 - Sample 1
312
Benchmark 1 - Sample 2
313
Benchmark 1 - Sample 3
314
Benchmark 1 - Sample 4
315
Benchmark 1 - Sample 5
316
Benchmark 2 - Sample 1
317
Benchmark 2 - Sample 2
318
Benchmark 2 - Sample 3
319
Benchmark 2 - Sample 4
320
Benchmark 2 - Sample 5
321
Benchmark 3 - Sample 1
322
Benchmark 3 - Sample 2
323
324
Benchmark 3 - Sample 3
325
Benchmark 3 - Sample 4
326
Benchmark 3 - Sample 5