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THE DEVELOPMENT OF A SWARM-BASED
EXPLORATION ALGORITHM WITH THE EXPANDED
SQUARE PATTERN USING QUADCOPTER
BY
MUHAMMAD FUAD RIZA ZUHRI
INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA
2016
THE DEVELOPMENT OF A SWARM-BASED
EXPLORATION ALGORITHM WITH THE EXPANDED
SQUARE PATTERN USING QUADCOPTER
BY
MUHAMMAD FUAD RIZA ZUHRI
A thesis submitted in fulfilment of the requirement for the
degree of Master of Computer Science
Kulliyyah of Information and Communication Technology
International Islamic University Malaysia
SEPTEMBER 2016
ii
ABSTRACT
Exploration algorithm is one of the most important roles in searching mechanism. In
robotics field, exploration algorithm deals with the implementation of the robot to
enlarge the information over a particular environment. In other words, the
implementation of exploration algorithm into the robot is intended to survey the
situation or condition of a specific area. Based on that comprehension, exploration is
applicable to various field such as search and rescue, monitoring conservation,
scientific space exploration, etc. Although the field of exploration algorithm on
robotic has become a major research area and been studied since the 1950s, the
exploration problem has always been an interesting topic for investigation. A variety
of techniques has been developed, even the biological systems have also become an
inspiration to be reckoned. In this thesis, we propose a swarm-based exploration
algorithm with the expanded square pattern using the quadcopter to explore an
unknown area. In this algorithm, the expanded square pattern is conducted by a series
of the distance around a fixed reference point. We simulate the swarm-based
exploration algorithm with the expanded square pattern in the VREP simulator. The
existing exploration algorithms namely, the frontier baseline and the cellular automata
are also simulated to be compared with the proposed algorithm. All algorithms are
simulated with the same setup. In order to analyse and evaluate the performance of all
algorithms, the data of the simulation are documented. Some comparisons are
conducted such as the performance of all algorithms, the performance of a group of
the quadcopter, the covered spaces and the cooperation among groups. According to
the simulation results, the swarm-based exploration algorithm with the expanded
square pattern can explore better and faster compared to the frontier baseline and the
cellular automata as the number of robots increased. This is supported by the
statistical analysis that is conducted at the end of this research.
iii
ملخص البحثC
في آلية البحث. خوارزمية التنقيب تنقيبالفي مجال الروبوتات، خوارزمية تلعب دورا مهما
بعبارة أخرى، فإن خوارزمية التنقيب في. تاتكبير المعلوم تزود الروبوت في بيئة معينة بقابلية
ينطبق على فإن التنقيبعلى ذلك الفهم، بناء . منطقة معينة شروطدراسة حالة أو ب تعنى الروبوت
الفضاء العلمي، وما إلى واستكشاف ،المراقبة والصيانة مختلف المجاالت مثل البحث واإلنقاذ،
خوارزمية التنقيب الروبوتية أصبحت منطقة بحثية رئيسية وتمت على الرغم من أن .ذلك
لتنقيبشكلة افإن م، 1950عام منذ تهادراس موضوع ما تكون دائما . حتى اآلن للبحث ا مثير ا
ت، وحتى النظم البيولوجية أصبحتم تطويرها مجموعة متنوعة من التقنيات مصدر إلهام ال أيضا
مربع توسعي مع نمط يةفي هذه األطروحة، اقترحنا خوارزمية التنقيب السرببه. يستهان
في هذه الخوارزمية، .الستكشاف منطقة مجهولة طوافة رباعية المراوح )كوادكوبتر(استخدام ب
في هذا .حول نقطة مرجعية ثابتة اتمربع من خالل سلسلة من المسافالتوسعي ال نمطيتم تأدية ال
ية مع النمط التوسعي المربع باستخدام برنامج محاكاة خوارزمية التنقيب السربنقوم بالبحث،
تمت خوارزميات التنقيب الحالية التي تم تحديدها . VREPلمحاكاة ا لغرض محاكاتها أيضا
ات. نفس اإلعداد باستخدامتمت محاكات جميع الخوارزميات .مع الخوارزمية المقترحة تهامقارن
ريتأج. ثم توثيق البيانات من المحاكاةتم تحليل وتقييم أداء جميع الخوارزميات، لغرض
المساحات و كوادكوبتر،المثل أداء جميع الخوارزميات، وأداء مجموعة من بينها مقارنات
على التحليل اإلحصائي الذي ا داعتمايمكن أن نخلص .المغطاة، والتعاون بين المجموعات
و جري في نهاية هذا البحثأ نمط المع يسربالخوارزمية التنقيب أن لنتيجة المحاكاة، وفقا
.أن تؤدي بشكل أفضل وأسرع كلما زاد عدد الروبوتات مكنيالتوسعي المربع
iv
APPROVAL PAGE
I certify that I have supervised and read this study and that in my opinion, it conforms
to acceptable standards of scholarly presentation and is fully adequate, in scope and
quality, as a thesis for the degree of Master of Computer Science
…………………………………..
Amelia Ritahani Ismail
Supervisor
I certify that I have read this study and that in my opinion it conforms to acceptable
standards of scholarly presentation and is fully adequate, in scope and quality, as a
thesis for the degree of Master of Computer Science
…………………………………..
Rizal Mohd. Nor
Internal Examiner
…………………………………..
Mohammad Faidzul Nasrudin
External Examiner
This thesis was submitted to the Department of Computer Science and is accepted as a
fulfilment of the requirement for the degree of Master of Computer Science
…………………………………..
Normi Sham Awang Abu Bakar
Head, Department of Computer
Science
This thesis was submitted to the Kulliyyah of Information and Communication
Technology and is accepted as a fulfilment of the requirement for the degree of Master
of Computer Science
…………………………………..
Abdul Wahab Abdul Rahman
Dean, Kulliyyah of Information
and Communication Technology
v
DECLARATION
I hereby declare that this thesis is the result of my own investigations, except where
otherwise stated. I also declare that it has not been previously or concurrently
submitted as a whole for any other degrees at IIUM or other institutions.
Muhammad Fuad Riza Zuhri
Signature ........................................................... Date .........................................
vi
COPYRIGE
INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA
DECLARATION OF COPYRIGHT AND AFFIRMATION OF
FAIR USE OF UNPUBLISHED RESEARCH
THE DEVELOPMENT OF A SWARM-BASED EXPLORATION
ALGORITHM WITH THE EXPANDED SQUARE PATTERN
USING QUADCOPTER
I declare that the copyright holders of this thesis is Muhammad Fuad Riza Zuhri
Copyright © 2016 Muhammad Fuad Riza Zuhri. All rights reserved.
No part of this unpublished research may be reproduced, stored in a retrieval
system, or transmitted, in any form or by any means, electronic, mechanical,
photocopying, recording or otherwise without prior written permission of the
copyright holder except as provided below
1. Any material contained in or derived from this unpublished research
may be used by others in their writing with due acknowledgement.
2. IIUM or its library will have the right to make and transmit copies (print
or electronic) for institutional and academic purposes.
3. The IIUM library will have the right to make, store in a retrieved system
and supply copies of this unpublished research if requested by other
universities and research libraries.
By signing this form, I acknowledged that I have read and understand the IIUM
Intellectual Property Right and Commercialization policy.
Affirmed by Muhammad Fuad Riza Zuhri
……..…………………….. ………………………..
Signature Date
vii
ACKNOWLEDGEMENTS
First and foremost, Alhamdulillah, all praises and thanks to Allah SWT, the Almighty,
for the strengths, the patience and His showers of blessings throughout my research
work to complete my thesis successfully, after all the challenges and difficulties.
I wish to express my appreciation to my supervisor, Dr. Amelia Ritahani
Ismail. I would not the person I am now without her guidance. Thank you for
introducing me to this research world. I could not have imagined having a better
advisor and mentor for my Master study. My appreciation to my fellow AI Group
members, who supported each other to ensure we would accomplish this goal.
Finally, it is my utmost pleasure to dedicate this work to my dear parents and
my family, for supporting me spiritually throughout writing this thesis and for making
me believe in myself and keep going. Thank you for your support and patience.
viii
TABLE OF CONTENTS
Abstract .................................................................................................................... ii
Abstract in Arabic .................................................................................................... iii
Approval Page .......................................................................................................... iv
Declaration ............................................................................................................... v
Copyright Page ......................................................................................................... vi
Acknowledgements .................................................................................................. vii
List of Tables ........................................................................................................... xi
List of Figures .......................................................................................................... xii
List of Algorithms xiv
CHAPTER 1: INTRODUCTION ........................................................................ 1
1.1 Research Background ............................................................................. 1
1.2 Statement of the Problem........................................................................ 4
1.3 Research Hypotheses .............................................................................. 5
1.4 Research Objectives................................................................................ 5
1.5 Research Questions ................................................................................. 5
1.6 Contribution ............................................................................................ 6
1.7 Thesis Structure ...................................................................................... 6
CHAPTER 2: LITERATURE REVIEW ............................................................ 7
2.1 Introduction............................................................................................. 7
2.2 Path Planning .......................................................................................... 8
2.3 Navigation System .................................................................................. 10
2.4 Exploration Algorithm ............................................................................ 13
CHAPTER 3: RESEARCH METHODOLOGY ............................................... 23
3.1 Research Methodology ........................................................................... 23
3.1.1 Studying the Literature Survey ..................................................... 23
3.1.2 Designing the Quadcopter in Simulation ...................................... 23
3.1.3 Simulating Swarm-based Exploration Algorithm to the
Quadcopter .................................................................................... 25
3.1.4 Analysing the Algorithm Performance of the Quadcopter ........... 27
3.1.5 Comparing the Proposed Algorithm with the Other
Algorithms .................................................................................... 27
3.1.6 Documenting the Result of Simulation, Evaluation,
Comparison and Analysis ............................................................. 28
CHAPTER 4: THE SWARM-BASED EXPLORATION ALGORITHM
WITH THE EXPANDED SQUARE PATTERN ................... 29
4.1 The Swarm-based Exploration Algorithm with the Expanded Square
Pattern ..................................................................................................... 29
CHAPTER 5: EXPERIMENTAL SETUP ......................................................... 36
5.1 Simulation Platform ............................................................................... 36
5.2 Experimental Protocol ............................................................................ 37
ix
CHAPTER 6: SIMULATION ............................................................................. 42
6.1 Experiment I: The Swarm-based Exploration Algorithm with
Expanded Square Pattern ........................................................................ 42
6.1.1 Experiment I: Two Quadcopters ................................................... 42
6.1.2 Experiment I: Four Quadcopters ................................................... 43
6.1.3 Experiment I: Eight Quadcopters .................................................. 43
6.2 Experiment II: The Swarm-based Exploration Algorithm based on
Frontier Based Approach ........................................................................ 47
6.2.1 Experiment II: Two Quadcopters .................................................. 47
6.2.2 Experiment II: Four Quadcopters ................................................. 48
6.2.3 Experiment II: Eight Quadcopters ................................................ 50
6.3 Experiment III: The Swarm-based Exploration Algorithm with
Cellular Automata .................................................................................. 53
6.3.1 The Virtual Mapping in the Cellular Automata ............................ 53
6.3.2 Experiment III: Two Quadcopters ................................................ 54
6.3.3 Experiment III: Four Quadcopters ................................................ 55
6.3.4 Experiment III: Eight Quadcopters ............................................... 57
CHAPTER 7: RESULT AND DISCUSSION ..................................................... 60
7.1 The Performance of Exploration Algorithm ........................................... 60
7.1.1 Experiment I: The Swarm-based Exploration Algorithm with
Expanded Square Pattern .............................................................. 60
7.1.1.1 Result and Evaluation ....................................................... 61
7.1.1.2 Analysis: The Vargha-Delaney A Test ............................. 62
7.1.2 Experiment II: The Swarm-based Exploration Algorithm
based on Frontier Based Approach ............................................... 63
7.1.2.1 Result and Evaluation ....................................................... 63
7.1.2.2 Analysis: The Vargha-Delaney A Test ............................. 65
7.1.3 Experiment III: The Swarm-based Exploration Algorithm with
Cellular Automata......................................................................... 66
7.1.3.1 Result and Evaluation ....................................................... 66
7.1.3.2 Analysis: The Vargha-Delaney A Test ............................. 67
7.2 The Performance of Groups of Quadcopters .......................................... 68
7.2.1 The Group of Two Quadcopters ................................................... 68
7.2.1.1 Analysis: The Vargha-Delaney A Test ............................. 69
7.2.2 The Group of Four Quadcopters ................................................... 70
7.2.2.1 Analysis: The Vargha-Delaney A Test ............................. 71
7.2.3 The Group of Eight Quadcopters .................................................. 72
7.2.3.1 Analysis: The Vargha-Delaney A Test ............................. 73
7.3 The Covered Space of All Algorithms ................................................... 74
7.3.1 The Comparison of Different Number of Square Pattern ............. 75
7.3.2 The Comparison of Covered Spaces ............................................. 76
7.4 The Cooperation among Quadcopters .................................................... 78
7.4.1 The Group of Two Quadcopters ................................................... 78
7.4.2 The Group of Four Quadcopters ................................................... 79
7.4.3 The Group of Eight Quadcopters .................................................. 80
7.4.4 The Cooperation of the Quadcopters in terms of
Communication ............................................................................ 81
x
CHAPTER 8: CONCLUSION ............................................................................. 85
REFERENCES ....................................................................................................... 88
xi
LIST OF TABLES
Table 6.1 The Duration of the Expanded Square Pattern Algorithm for
Different Number of Quadcopters 47
Table 6.2 The Duration of the Frontier Baseline Algorithm for Different
Number of Quadcopters 51
Table 6.3 The Duration of the Cellular Automata Algorithm for Different
Number of Quadcopters 58
Table 7.1 The Magnitude of the Effect Size Indicated by A Test Score:
Square Pattern 62
Table 7.2 The Magnitude of the Effect Size Indicated by A Test Score:
Frontier Baseline 65
Table 7.3 The Magnitude of the Effect Size Indicated by A Test Score:
Cellular Automata 68
Table 7.4 The Magnitude of the Effect Size Indicated by A Test Score:
Two Quadcopters 70
Table 7.5 The Magnitude of the Effect Size Indicated by A Test Score:
Four Quadcopters 72
Table 7.6 The Magnitude of the Effect Size Indicated by A Test Score:
Eight Quadcopters 73
Table 7.7 The Number of Communication and the Covered Space
Comparison of the Expanded Square Pattern 82
Table 7.8 The Number of Communication and the Covered Space
Comparison of the Cellular Automata 82
Table 7.9 The Measurement of the Effectiveness Communication 83
xii
LIST OF FIGURES
Figure 3.1 The Flowchart of the Research Methodology 24
Figure 4.1 General Concept of Exploration Activity 30
Figure 4.2 Cardinal Directions (Cardinal Points) 31
Figure 4.3 The Expanded Square Pattern 32
Figure 5.1 The Design of the Quadcopter in the VREP Simulator 38
Figure 5.2 The Design of the Environment in the VREP Simulator 39
Figure 5.3 The Staring Point of the Expanded Square Pattern Quadcopters 39
Figure 5.4 The Starting Point of the Frontier Baseline Quadcopters 40
Figure 5.5 The Starting Point of the Cellular Automata Quadcopters 40
Figure 6.1 Two Quadcopter Simulation on Experiment I 44
Figure 6.2 Four Quadcopter Simulation on Experiment I 45
Figure 6.3 Eight Quadcopter Simulation on Experiment I 46
Figure 6.4 Two Quadcopter Simulation on Experiment II 49
Figure 6.5 Four Quadcopter Simulation on Experiment II 50
Figure 6.6 Eight Quadcopter Simulation on Experiment II 52
Figure 6.7 The Virtual Map in the Cellular Automata 53
Figure 6.8 The Result of the Cellular Automata in the Virtual Map
Viewpoint 54
Figure 6.9 Two Quadcopter Simulation on Experiment III 56
Figure 6.10 Four Quadcopter Simulation on Experiment III 57
Figure 6.11 Eight Quadcopter Simulation on Experiment III 59
Figure 7.1 The Comparison of the Different Number of Quadcopter’s
Performance in the Exploration Algorithm with the Expanded
Square Pattern 61
xiii
Figure 7.2 The Comparison of the Different Number of Quadcopter’s
Performance in the Exploration Algorithm based on the
Frontier Based Approach 64
Figure 7.3 The Comparison of the Different Number of Quadcopter’s
Performance in the Exploration Algorithm based on the
Cellular Automata 67
Figure 7.4 The Comparison of the Algorithm’s Performance for the
Group of Two Quadcopters 69
Figure 7.5 The Comparison of the Algorithm’s Performance for the
Group of Four Quadcopters 71
Figure 7.6 The Comparison of the Algorithm’s Performance for the
Group of Eight Quadcopters 73
Figure 7.7 The Comparison of Each Scenario in terms of the Number of
Square Pattern Performed for Each Quadcopter 75
Figure 7.8 The Space Comparison for Two Quadcopters 77
Figure 7.9 The Space Comparison for Four Quadcopters 77
Figure 7.10 The Space Comparison for Eight Quadcopters 77
Figure 7.11 The Uncovered Space (Red Colour) 78
Figure 7.12 The Comparison of Duration among Group of Two
Quadcopters 79
Figure 7.13 The Comparison of Duration among Group of Four
Quadcopters 80
Figure 7.14 The Comparison of Duration among Group of Eight
Quadcopters 81
xiv
LIST OF ALGORITHMS
Algorithm 1 Exploration 30
Algorithm 2 Expanded Square Pattern 32
Algorithm 3 Covered Area 33
Algorithm 4 Next Location Planning 34
1
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF THE STUDY
Exploration is one of the most important utilities as a searching activity to obtain much
information in an unknown environment. It is the basic role for a searching activity
because exploration serves as the main contribution in collecting information. The best
result which can be achieved only if exploration can be completed. Without
completing the exploration, the result of a searching activity cannot be affirmed. In
ensuring a good exploration, two properties must be realized; completeness and
effectiveness in terms of space and time respectively. Completeness requires the
explorer to cover most of the area while effectiveness emphasizes the explorer’s
efficiency to complete the exploration in minimum time.
Nowadays, the exploration activity has been studied to make major
contributions in various fields. The researchers give more attention to this field in
order to support Search and Rescue (SAR) team on the job. It is because, sometimes,
the effort and result of the exploration are not good enough. Therefore, many
researches such as Alvissalim et al. (2012), Di Felice et al. (n.d.), Apvrille et al. (2014)
and Ma’sum et al. (2013) have been conducted to support the search and rescue team.
Those projects are trying to make some contribution that can help the SAR team do
their jobs. Various aspects of technology have been developed to reduce the risk in the
search and rescue mission that can endanger people or animals such as horses and
dogs usually involved in searching. The application of robots can retrieve information
much more easily and safely compared to people or animal.
2
However, the use of an autonomous robot has recently become a priority to
support the search mechanism. The unmanned aerial vehicle (UAV) is a kind of
vehicle that operate without a human pilot aboard. As an example, Cantelli et al.
(2013), Tanner (2007), Grocholsky et al. (2006) and Phan and Liu (2008) have proved
UAV can be used to cover large areas in the search for targets, moving rapidly or
seeing through such obstacles as buildings or fences that cannot be done by the
unmanned ground vehicle (UGV). Apvrille et al. (2014) and Ma’sum et al. (2013) give
an example of exploration as something that develops the autonomous drones that can
fly autonomously to cover certain areas and identify groups of people. Most of the
implementation applies the searching mechanism for finding people and the
exploration mechanism has a main role to support search and rescue.
In exploration, there are two elements that must be completed; path planning to
determine the area for the next exploration and the navigation system to manage the
movement of the quadcopter. These two elements should be completed appropriately
because of their main roles to discover an unknown area.
The concept of the swarm robot and the quadcopter as a kind of flying robot
has become two interesting ideas combined and developed in the exploration activity.
Some advantages of the multi-robot approaches described by Arkin (1998) that are a
group of robots can perform more efficiently and actuate at different places
simultaneously, a group of robots has a wider range of sensing than a single robot and
can accomplish certain goals which are impossible for a single robot.
A quadcopter is also a helicopter that is lifted and propelled by four rotors. The
use of quadcopter has advantages as mentioned by Bouabdallah et al. (2004), Patel et
al. (2012), Xu and Ozguner (2006), Zul Azfar and Hazry (2011), Gupte et al. (2012),
Lee et al. (2009) and Bou-Ammar et al. (2010): this kind of UAV offers the payload
3
augmentation, quadcopter as a simple unmanned vehicle to manufacture and control,
the navigation of quadcopter allows simple take-off/landing if it is compared to a fixed
wing aircraft. It is a hybrid of a fixed wing aircraft because of its manoeuvrability that
becomes its inherent dynamic nature and a kind of unmanned aerial vehicle, its flight
time depending on the fuel/battery life.
After looking at those implementations, it can be seen that a multi-flying robot
contributes an impact to a search mission. Thus, the use of robots, usually called
swarm robots, can be considered as a good option to develop an application to support
a search mission. In this context, the swarm-robot system is often suggested to have
obvious advantages over the single-robot system: faster, robust, fault-tolerant and
compensation of sensors uncertainty. To minimize the time to complete the
exploration task in the context of a swarm-robot exploration, efficient exploration
techniques should consider strategies to distribute the robots in the environment to
reduce an overlap among the explored areas of each robot. This is the global
coordination issue defined as allocating appropriately exploration goals for the
individual robots so that they simultaneously explore the different zones of the area.
The flying robot can also be selected as a type of robot which is more reliable in this
research, a type of flying robot, the quadcopter which is selected based on its
advantages.
In this research, we propose the development of a swarm-based exploration
algorithm with the expanded square pattern for an outdoor environment using the
quadcopter in large areas. The expanded square pattern adopted from the National
Search and Rescue Manual, Australia by Authority (2014). The basic idea of the
expanded square pattern is to start the pattern from a fixed central point and to expand
4
outwards in concentric squares. Some quadcopters will be designed in mass in order to
achieve a great exploration performance.
1.2 STATEMENT OF THE PROBLEM
Exploration is one of the important elements in a search mechanism. The search
mechanism needs the exploration technique to discover unknown areas. However,
some problems may occur e.g., the path to be discovered cannot be estimated by the
robot (Al Redwan Newaz et al., 2013). Hence, robots cannot estimate whether they
have covered the whole area or not. Therefore, the exploration should be conducted in
such a way where it can cover a large area. It becomes an important factor to be
considered so the completeness of an exploration can be predicted.
Moreover, other problems may also occur when the area to be covered is too
large. If the robots explore in the same direction, they will need more time to explore
another direction and avoid colliding with each other (Doniec et al., 2009; Zelenka and
Kasanicky, 2014). In this kind of environment, a method for the deployment of robots
must be considered because it will affect the effectiveness of the exploration. Each
robot should know its own area to which it belongs.
Furthermore, in a search mission conducted by a team, coordination among
members is very important for the division of tasks. It means that if each member does
not know the status of the other members regarding their positions and conditions,
they may end up exploring the same area that had already been covered. In other
words, the repetition can be a waste of time. On the other hand, space limitation in the
environment can force multiple robots to move together and they can also interfere
with each other (Julia et al., 2010; Zelenka and Kasanicky, 2014). The more robots
5
used to accomplish the goal, the more time needed on detours in order to avoid a
collision. Therefore, coordination among all robots must be managed properly.
1.3 RESEARCH HYPOTHESES
The following are some hypotheses relating to this research:
H1 The swarm-based exploration algorithm can be successfully applied for
exploring a large area.
H2 The proposed swarm-based exploration algorithm can be implemented to a
swarm of quadcopters for their coordinated movement based on different
directions.
1.4 RESEARCH OBJECTIVES
This study embarks on the following objectives:
1- To study the existing exploration algorithms.
2- To develop a swarm-based exploration algorithm for the quadcopter.
3- To simulate the exploration algorithm in a large area.
4- To compare the performance of the swarm-based exploration algorithms
with the other swarm-based algorithms.
1.5 RESEARCH QUESTIONS
In this research, the focus is to answer the following important questions:
1. What are the exploration algorithms that have been developed?
2. What kind of exploration algorithm can be implemented to a swarm of the
quadcopters?
3. How effective is the proposed swarm-based exploration algorithm for
covering a large area compared to the other swarm-based exploration
algorithms?
6
1.6 CONTRIBUTION
The major contribution of this research is the development of a swarm-based
exploration algorithm with the expanded square patterns. This algorithm will be
simulated using the quadcopters. This algorithm is intended for outdoor environments
in a large area. This algorithm is developed based on the coordinated movement of
swarm robots.
1.7 THESIS STRUCTURE
This thesis is divided into 8 chapters. In chapter 2, we provide a critical review of the
related works and literature on the exploration algorithm, path planning and navigation
system. This chapter will clarify the common problem of the existing exploration
algorithm and identify the proper navigation system and path planning to be utilized.
In chapter 3, we provide the research methodology that describes our attempts to
create the swarm-based exploration algorithm with the expanded square pattern. In
chapter 4, we provide an explanation of the swarm-based exploration algorithm with
the expanded square pattern. In chapter 5, we describe the experiment that will be
organized for the proposed algorithm and the other two existing exploration
algorithms. In chapter 6, the simulation of exploration algorithm that is implemented
to a different number of robots in different scenarios is illustrated. In chapter 7, we
provide our result and discussion about the performance of the swarm-based
exploration algorithm compared to other swarm-based exploration algorithms. Finally,
Chapter 8 will be dedicated to the conclusion of the development of the swarm-based
exploration algorithm with expanded square patterns.
7
CHAPTER TWO
LITERATURE REVIEW
2.1 INTRODUCTION
Environment exploration is a domain in robotic research that studies the robot
behavior in discovering an area. In real life, robot explorations are used in the
dangerous environmental investigation and the search and rescue field which do not
allow the participation of human beings. Some research has been conducted and
expanded into the other subfields such as coordinated deployment and distribution,
searching mechanism, monitoring system and other.
The exploration algorithm consists of two major elements: mapping and path
planning as mentioned by Stachniss (2009). These two elements cannot be applied
independently. Mapping is an element that is needed by the robot to gather
information of the environment. It is used to answer the question “What does the
environment look like?” Some researchers use camera vision sensors to gather this
information while others use the sonar sensor or laser findings. Since this research
does not use any kind of sensor and camera, the role of mapping the environment will
be replaced by the navigation system. Hence, the robots in this research use the
navigation system to gather information about the environment. Path planning answers
the question “How can I reach a target location?” In this aspect, it needs the
information that is collected by mapping. These two elements can be combined to do
the exploration.
In this chapter, three main aspects of exploration algorithms will be explained
clearly: path planning in Section 2.2, the navigation system in Section 2.3 and Section
2.4 will explain the existing problems of the exploration algorithm.
8
2.2 PATH PLANNING
In Tisdale et al. (2009), path planning approach was developed to deal with the
myopic which can be a problem for the UAV in certain situations. This approach is to
choose a planning horizon to ensure that every plan has a value above some threshold
and it is called the increasing horizon planner strategy. The good thing about this
technique is that this system can be categorized as a unique for it allows the searching
and localization step in the same framework so it can reduce the complexity of its
process. However, this technique could not be applied in detecting more than one
moving target.
Khuswendi et al. (2011) explore the composition of the path planning
algorithm and claim it as the most appropriate algorithm for UAV path planning. The
algorithms proposed by Khuswendi et al. (2011) are based on the potential field
method and A* algorithm because these two methods have similarities. Because the
2D A* algorithm and A* 3D hierarchical methods had problems about being time
consuming and short path problem respectively, eventually, the A* 3D receding
horizon method is applied by dividing the environment into 10×10×20. The advantage
of this algorithm is that it optimizes the constraint that occurs in other methods in
terms of safety, time and energy cost. However, this algorithm can be applied if the
obstacle has been determined.
The biological system can also inspire the path planning technique. Galvez et
al. (2014) adopt the genetic algorithm for path planning. The process starts with
initializing the start point and goal point in 3D coordinates. The genetic algorithm will
evaluate its fitness. The obstacle is then identified in the chromosome. The next step is
the selection process where the first half of high fitness chromosome is used to
generate offspring and as a result, the fittest chromosome is the lowest value of
9
distance traveled. Here, it alters each gene with a small probability and applies a
random search to make sure all points have been examined as described by Mitchell
(1998).
In addition, according to Faigl et al. (2010), a simple straight line segment path
between two goals increases the error in the segment (longitudinal) direction because
of imprecise odometry. In order to solve this problem, the robot can traverse to any
point close to the goal point. So, the proposed algorithm by Faigl et al. (2010) would
use the SOM adaptation schema for the TSP modified using equation (4): Ai+1 =
RTiMiRiAiR
TiMi
TRi + RTiSiRi mentioned by Faigl et al. (2010) in his paper that created
the ring of points. This technique can be applied in a situation where there is more
than one goal point but the localization error and not recognizing the goal and position
can be a serious problem.
A two-level path planning algorithm is proposed by Ok et al. (2013) and called
Voronoi Uncertainty Fields (VUF). The higher level planner modifies the generalized
Voronoi diagrams introduced by Lee and Drysdale (1981) for a collision-free path
exists. The lower level planner considers the observed obstacle in the environment
using the potential field method introduced by Khatib (1986). The top-level planner
can be used as the global planner to create and update a list of Voronoi nodes that
make the shortest path. The bottom-level can be used as the local planner that uses the
list as a local way-point. However, Ok et al. (2013) combine these two methods with
the SLAM system. The advantages of this method are that it is designed for an
uncertain environment, it moves robots out of local minima and it provides a forest-
like environment. However, it does not deal with map uncertainties in a deterministic
and complete way.
10
In summary, after looking at those path planners and considering the
advantages and disadvantages of each algorithm, Ok et al., (2013) gives some ideas to
this research i.e., to divide path planning into two levels: the higher level deals with
the global planner and the lower level deals with the local planner. Therefore, in the
development of the path planning algorithm, the concept of the local and global path
planning will be adopted by adjusting them with the swarm-based exploration
algorithm. The path taken should be recorded or memorized by the quadcopter so it
will not take the same path that can be time-wasting for exploration.
2.3 NAVIGATION SYSTEM
Navigation is a field of study that focuses on the process of monitoring and controlling
the movement of a craft or vehicle from one place to another. The field of navigation
includes four general categories: land navigation, marine navigation, aeronautic
navigation, and space navigation. The quadcopter navigation system will be
categorized as aeronautic navigation. The following are some work that has been done.
Krajnik et al. (2012) present a simple visual navigation system for an
autonomous quadcopter extended from a ground robot navigation system in Krajnıket
al. (2010). The method is based on the “record and replay” technique. So, the record
technique means that the UAV will traverse along the (poly-line shape) path and
during this movement, it will track or “record” the salient features in an image from an
on-board camera. The quadcopter could then perform autonomous flight towards the
first recorded path segment by applying the replay technique. Here, the quadcopter
compares the mapped landmarks to the features in its field of view. The use of the
simple histogram voting scheme that makes methods swift and robust is the
advantages besides it does not require radio beacons and artificial landmarks.
11
However, the disadvantage is that it operates only along paths that have been travelled
before, only for the straight line and has the limitation in length.
Although this algorithm does not need to use a landmark, the method from
Krajnik et al. (2012) is similar to what has been done by Apvrille et al. (2013) and
Selby et al. (2011). All depend on the condition or situation of the environment. It
means that the environment has to provide them with some information. It can be seen
from the Apvrille et al. (2013) project.
For the indoor environment, Apvrille et al. (2013) introduce the autonomous
navigation using landmark and 3D perception with a bottom and a front camera and
on-board sensors. In this project, there are three recognitions applied which follow a
coloured line on the floor, to identify landmark and to capture the environment in 3D
without a 3D sensor. Two techniques are described here, proposed by Ranft et al.
(2013). The use of the landmark and the prediction of the moving object are two
disadvantage of this system as well as it is only for the indoor environment.
Moreover, in a project, visual control for the quadcopter navigation system is
developed by Selby et al. (2011). An independent and on-board vision-based control
system to autonomously identify and track a moving target. The motion captures
feedback and is replaced by an estimated state measurement from an Extended
Kalman Filter (EKF) from Bachrach et al. (2009) and Bachrach et al. (2011).
However, Selby et al. (2011) also provide the GPS system. The GPS plays an
important role in this technique when the system is in an autonomous mode. However,
it has not been proven in a real ocean. The controller also depends on the visual
navigation and there is a limitation of intended target.
To prevent from being dependent on environmental information, a project by
Engel et al. (2012) creates a system where a ground-based laptop can navigate
12
autonomously in an unknown and GPS-denied environment. So, this approach utilizes
three components running on a laptop and connected via wireless LAN to the
quadcopter. The first is the monocular SLAM based on PTAM as described by Klein
and Murray (2007). The second is EKF to fuse all available data. One advantage is it
does not need an artificial landmark and knowledge about the environment.
Unfortunately, it uses off-board processing, in other words, it depends on the laptop.
Thus, the limitation of distance also becomes a problem.
Krokowicz et al. (2010) also introduce another solution to prevent from
problems of Krajnik et al. (2012), Apvrille et al. (2013) and Selby et al. (2011). For
the indoor navigation system, Krokowicz et al. (2010) use schematic environment
maps and sensor information from ultrasonic sensors. In this algorithm, the robot has
to identify the characteristic points and based on these points, the robot position can be
identified. The schematic map and ultrasonic sensor give benefit to this system
because it is easy and cheap. But it is only for the indoor environment and the absence
of camera and sensor reading is its flaw.
For this matter, Rengarajan and Anitha (2013) has a good solution in using the
GPS for the navigation system. They also develop an algorithm for an autonomous
way-point navigation using the GPS and Atmega-328P. In this project, the GPS is
used as an input sensor to get the latitude and longitude of the quadcopter’s current
position. There are two good things about this system: the use of the GPS and that it
flows along predefined tracks. However, the system depends on its base station
(laptop) for the whole mechanism so the limitation of distance is also a short-coming.
It can be summarized that for the navigation system, the use of the GPS
becomes a main factor for the effective movement of the quadcopter as shown by
Selby et al. (2011) and Rengarajan and Anitha (2013). It is because the quadcopter can
13
find the exact point coordinated in term of (x, y) for latitude and longitude. Some
research has been conducted relying on the camera to guide the movement of the
quadcopter. However, in this research, the use of the camera is not a proper way
because it is used to detect objects and in a certain condition, it cannot give an accurate
guidance to the quadcopter.
2.4 EXPLORATION ALGORITHM
Exploration is the act of searching for the purpose of the discovery of information or
resources. Exploration of unknown environments has become one of the interesting
problems in robotics. This work requires a robot to explore and at the same time to
learn about the covered area so that the area can be identified and recognized. A multi-
robot system has made the contribution to this research field. In this section, we want
to look for various methods that have been done and to get some ideas of the existing
exploration methods. Most divide the exploration method into several stages that will
be executed depending on the robot’s situation and some have developed exploration
algorithm inspired by the biological system.
Many researchers have published the exploration algorithm that falls into the
algorithm of the frontier-based exploration such as Yamauchi (1997), Yamauchi et al.
(1998), Makarenko et al. (2002) and Gonzalez-Banos and Latombe (2002). They
create a strategy based on the idea that robot(s) attempt to obtain as much new
information as possible from the environment explored by going to the boundary
between the area that had been explored and unexplored (Pravitra et al., 2011). The
random selection technique has always become an option in this problem study.
However, when we implement it in real life, the random selection may be an
inefficient technique.
14
Yamauchi (1997) says the basic idea of a frontier based approach is:
”To gain the most new information about the world, move to the
boundary between open space and uncharted territory.”
By constantly moving to new frontiers, the robot can gain more information
about the new territory and extend the map. In this algorithm, when the robot can
navigate to a certain position, it means that that area is considered accessible for
exploration. Many researchers have worked such as Fraundorfer et al., (2012), Freda
and Oriolo, (2005) and Simmons et al., (2000) that relate to this algorithm and some
has improvised it.
Franchi et al. (2009) and Cesare et al. (2015) have shown experiments about
the exploration algorithm. In Franchi et al. (2009), a decentralized strategy for
cooperative robot exploration has been developed. A simple and decentralized
cooperation mechanism becomes the basic idea of this method. Each robot moves
towards areas that appear to be unexplored by the rest of the team on the basis of the
available information. However, Cesare et al. (2015) offer a new method inspired by
Franchi et al. (2009) that would be explained later.
A similar idea is also described by De Hoog et al. (2009) where the robots
would cooperate to store information of the covered areas. Role-based autonomous
exploration algorithm is proposed by De Hoog et al. (2009). In this method, there will
be two mobile robots and each is assigned one of two roles at the beginning of
exploration and do not change. The two roles are explorer which means to explore the
farthest reaches of the environment and relay which helps to connect explorers to the
command centre. Thus, firstly, a mobile robot will explore an area as far as it can and
then periodically returns to a previous rendezvous point to pass its knowledge to a
relay mobile robot. After that, the relay mobile robot communicates the findings of the
15
explorer mobile robot to the command centre. If it finds an information of the
environment while relaying, this information will be added to the findings of the
explorer mobile robot. However, De Hoog et al. (2009)’s method has a limitation of
communication and it is limited by a static team hierarchy. Maintaining them could
lead to a long travel to the rendezvous point.
In 2009, works about the exploration method under communication constraint.
Doniec et al. (2009) make a proposal that is claimed as the original way to formalize
and solve the issue that relies on the distributed constraint satisfaction problems
(disCSP) which are an extension of the classical constraint satisfaction problem (CSP)
by Kumar (1992). There are five states implemented on this algorithm. The first is to
update maps and connectivity tables for each robot. The second is to construct the
disCSP based on the connectivity table and the robot’s current position. The next state
is to order the value of each domain taking the distance to the frontier into account.
The fourth is to solve the disCSP to obtain the next direction and finally, to operate the
movement of each robot during a fixed time period. All these five states will be
repeated until there are no more unexplored areas. Each robot must exchange its local
map with each other to build a global view of the environment in order to detect those
five states. It guarantees the connectivity among all members, it is easier for deadlock
detection and it decreases the duration when adding the number of robots. But, when
the robot is added, the team spends more time avoiding each other than exploring. The
size of the disCSP also increases requiring more messages. The algorithm is
asynchronous so the delay occurs when exchanging messages.
So far, it can be seen that those algorithms have problems, such as leading to
long travel to tryst point or getting stuck at a certain point which can result in
inefficient exploration and the distribution of the robot that can waste time. Therefore,
16
Julia et al. (2010) and Cepeda et al. (2012) have tried to solve this kind of problem.
These two works will be explained below.
Julia et al. (2010) introduce a hybrid/deliberative approach to the multi-robot
for exploration problem. One exploration problem is the negative effect of local
minima that has been presented by Lau and NSW (2003) and Julia et al. (2008) where
it wastes time in the process to escape from that point. Basically, this approach relies
on the concept of an expected safe zone that inspired the concept of safe zone
implemented by Gonzalez-Banos and Latombe (2002) and Franchi et al. (2007) and
the gateway cell. There are two layers for the movements of the robot: the reactive
layer (state: go to frontier, avoid obstacle or go to gateway) for the expected safe zone
and the deliberative layer used to switch or combine several states. Moreover, the
hybrid/deliberative approach has advantages such as the avoidance of local minima,
the reactive process runs in a delimited period of time caused by the expected safe
zone concept and all these show the robustness of the algorithm. However, usually, if
more robots are implemented and the result should be better. However, in this case, it
increases the error because too many robots have to cover a small area travel in a
shorter path more time. Therefore it is needed in the changed zone state for
coordination.
Cepeda et al. (2012) have introduced a simple exploration algorithm that
combines a behaviour-based navigation with an efficient data structure to store path
taken. The proposed algorithm implements four different behaviours and a resultant
emergent behavior. The first is to avoid obstacles which considers three conditions:
the first condition is the possible corner to avoid getting stuck, the second is to keep
distance from obstacle and the last is to avoid team-mates. The second behaviour is to
avoid past introduced by Balch (1993) which is used to gather the newest location by
17
using a hash table to store the path taken before. The third behaviour is to locate an
open area, used for locating the largest open area. This behavior represents the
wandering factor of the exploration technique. The four behaviour is dispersion as
introduced by Mataric (1995) that implement the coordination mechanism to spread
the robots and avoiding team-mates. The last behaviour is a resultant emergent
behaviour that uses a Finite State Automata (FSA) to decide which state should be
activated. This constitutes an important part of this exploration algorithm. The good
things are it can cover a large open space and not get stuck nor spend unnecessary time
because of the use of the hash table. Unfortunately, it depends on the communication
with the other robots so there is a limitation in the distance and the quality of the map
is also not good.
Furthermore, for the multi-robot team, the communication range may be a case
so the coordination that was under limited communication has to be brought into
account. However, rendezvous strategy has been implemented to solve the limited
communication problem. It can be seen from Ko et al. (2003), Roy and Dudek (2001)
and Zhou and Roumeliotis (2006). Mosteo et al. (2008) also address the same
problem. Yuan et al. (2010) introduce a cooperative approach for multi-robot
exploration that adopts the frontier-based algorithm introduced firstly by Yamauchi
(1997). The optimal frontier will be selected by evaluating information gain and
navigation cost and consider the communication range. Thus, the robot team calculates
the set of frontier cells and select the candidates from this set. The evaluating index
includes information gain and navigation cost as mentioned before. When the robot
explores the unknown area, the number of frontier cells will also increase.
Unfortunately, because of that, the computation complexity would be over. Therefore,
the frontier cells can be partitioned into different groups and each group evaluated by
18
using the subtractive clustering algorithm introduced by (Chiu, 1994) to estimate the
initial centre and the number of groups. The result of clustering will be as some
candidate cells and these will be integrated by each robot. So, the selected destination
of the robot will be the region enveloped by the frontier and finally, it is known.
Eventually, the exploration of the unknown environment can be converted into a
problem of multi-stage trajectory planning in the known environment which consists
of two sub-processes including avoiding to disperse the robot team and tracking to
synchronous rendezvous for the multi-robot. The efficient and distributed exploration
give some benefit so it can minimize the navigation cost. However, since it calculates
local destinations without enough information, it needs more exploration step. It does
not consider the motion and measurement uncertainty and has limited
communications.
After observing the exploration for the mobile robot, the following paragraph
will lead us to see the exploration especially for the flying robot such as MAV or the
quadcopter. Some researchers adopt the idea from the mobile robot’s exploration
algorithm and others create their own algorithm. However, so far, many researchers
still implement the idea introduced by Yamauchi (1997) as their basic exploration
movement combined with other techniques.
An exploration algorithm has been presented by Shen et al. (2012a) and Shen
et al. (2012b). It is called the SDE-based exploration algorithm that enables MAV to
explore in 3D indoor environments. So, the exploration algorithm is used to explore
the free space. Here, the free space is represented by a set of virtual particles that is
resampled based on its density to identify the representation of the environment. It
means that the dense of the particle is for the known space while the sparse is for the
unknown space. There are three states described by Shen et al. (2012a) and Shen et al.
19
(2012b): initializing and re-sampling, simulating particle expansion and extracting
frontiers as the Stochastic Differential Equation-based Exploration algorithm (SDEE).
After initializing which is to generate and emit the particle at the known free space
location, the next step is re-sampling by detecting the regions of the greatest expansion
of particle. After the free space is identified, the frontiers are selected and the robot
navigates to that location and does full exploration. Unfortunately, some flaws can be
seen from this project. The first is that it is only for the indoor environment and a
single robot. Then, there is no information about the number of particles required in
expanding particle for the certain environment. Based on the experimental setup, if it
is compared to the frontier-based, it needs more path and time and the percentage of
covered area is less than the frontier based.
Additionally, Sang et al. (2013) describe an exploration and an obstacle
avoidance method design to be implemented on indoor MAV running in cluttered and
confined indoor environment. Since Sang et al. (2013)’s work aims to three problems,
it uses the frontier based strategy from (Yamauchi, 1997) for the basic exploration idea
of the MAV. For getting waypoint, they use the safe corridor method and the SLAM
navigation system is implemented for estimating position. They use the safe corridor
and the waypoint tree where the child node is selected corresponding to the longest
frontier as the next waypoint. If no child node is feasible, the MAV returns to the last
node and removes the current node. It can be seen that there is a chance where the
robot returns to the explored area and if it is implemented in multi-robot, there is a
chance where two or more robots will cover the same area. In other words, the
efficiency of this algorithm is not as expected. There is also a possibility where the
MAV does not explore the area completely.
20
Al Redwan Newaz et al. (2013) proposes a heuristic algorithm for exploration
priority. In this algorithm, the UAV is used to test their algorithm in V-REP simulator.
They say that their algorithm envisions a new direction for online path planning based
on the fact that the obstacle does not always hinder from reaching a goal position,
rather, sometime, it helps to find a goal position easily. In other words, they try to use
the obstacle to reach the goal position and invokes the obstacle to be the guidance. In
this algorithm, Al Redwan Newaz et al. (2013) introduces four steps: grid making to
find the nearest next set position, cost estimation to restrict the movement options of
UAV, obstacle search to avoid from any obstacle and moving to minimum cost point
to navigate its position to the new location. Moreover, Al Redwan Newaz et al. (2013)
claims their algorithm for having less search although their path is not the shortest.
Besides that, this algorithm does not ensure the UAV can cover the whole area. The
use of a heuristic algorithm in path planning also does not always guarantee to find the
goal position even if it can set the next movement and the indoor environment become
the characteristic of this algorithm’s implementation.
It can be seen that problems appear with the exploration by a single robot. The
exploration cannot be achieved for all kinds of environment. The range of exploration
by a single robot is not as large as multiple robots and there is no assurance that the
robot can cover the area as expected. However, limitations can be solved by using
multiple robots when they can cooperate. Some works have been done to overcome
these problems that will be explained in the following paragraph.
Another work about swarm robot that implements the exploration algorithm is
introduced by Cesare et al. (2015). They utilize four states: explore, meet, sacrifice and
relay. This method is compared to a baseline frontier-based exploration approach by
Franchi et al. (2009) where the baseline frontier-based approach is the same as the
21
state explore. The comparison shows that the improvement of using the states of meet,
sacrifice and relay can explore a greater percentage of the map (5% to 18%). This
method maximizes the efficiency of exploring with unreliable communication and
limited energy battery that can be predicted as well. However, it is just for a small
scale environment, not for a large environment and there is a possibility that the robot
cannot complete together. In other words, the completeness of an exploration cannot
be affirmed for all cases. Furthermore, some constraint of Cesare et al. (2015)’s work
cannot be considered such as this algorithm is only for the indoor environment and this
work is intended only for certain conditions which are unreliable communication and
limited energy battery.
Zelenka and Kasanicky (2014) is inspired to develop the exploration algorithm
based on cellular automata for the swarm robot. They investigate this algorithm in the
real outdoor environment using two quadcopters. In this approach, every robot has to
have its own map which is a cellular grid. Every robot creates its own map based on its
sensing and they will share information to gain the information of the environment.
Every change that is made is also updated. Coordination among the robots is done by
using the evaporated mark and robot behaviours. Once a single robot has visited an
area, it will put a mark on this area with virtual pheromones. For the exploration part,
the robot will divide the area into regular square cells and it can perform one step or
stay in position in every iteration movement. When a robot visits a grid cell, it gives a
mark such as virtual pheromones onto its map and direction and sends it to the other
robots. By doing that, it can share information. However, after a robot visits that area,
the other robots can visit the same area again since the first robot has moved to the
other grid cells and shared its own virtual pheromones. In other words, there is a
chance where two robots or more can discover the same area even if the time they visit
22
is different. It can be seen in Figure 3 in Zelenka and Kasanicky (2014). The
distribution of the swarm robot is also not managed. It means that the swarm robot can
move in the same direction at the same time.
After observing those exploration algorithms, the final problem leads us to
Zelenka and Kasanicky (2014)’s problem which is the possibility of robots to explore
the same area which can be a waste of time and the efficiency of an exploration in
relation to the number of robots is not proven statistically. The distribution of the robot
is also not evaluated to support the performance of the exploration. It is because the
distribution or deployment of robots can give impact to the efficiency and
effectiveness of the exploration activity.
It can be summarized that the use of multi robots in exploration can give better
results. In other words, a large environment can be covered by multiple robots with
less duration in finishing it. Moreover, the cooperation among robots in terms of their
deployment becomes an effective approach to increase the efficiency and effectiveness
of the exploration.
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CHAPTER THREE
RESEARCH METHODOLOGY
This part of the thesis describes the way in which the work on simulating the searching
algorithm has been carried out. This chapter is related to the readings and findings
from Chapter 2. In Section 3.1, the research methodology is explained. The research
methodology is summarized in the flowchart in Figure 3.1.
3.1 INTRODUCTION
3.1.1 Studying the Literature Survey
In the first step, the literature survey is conducted to identify the basic idea of the
exploration algorithm. Those publications are analysed to study about the path
planning algorithm, the navigation system and the exploration algorithm. The
advantages and disadvantages of each research are identified to get a clear and deep
understanding of problems of the exploration algorithm that are going to be solved.
3.1.2 Designing the Quadcopter in Simulation
In the second step, the component to build a quadcopter is identified and determined
such as motor, body, camera, some sensors and navigation module. And then, start
constructing the body of quadcopter by designing it in the simulation. Since this
research only creates the quadcopter in simulation, some simulators have already
provided the quadcopter as a complete unit. However, some modules must be added to
support the performance of the quadcopter in completing the task such as GPS for the
navigation system and the wireless module for communication among units.
24
Figure 3.1 The Flowchart of the Research Methodology
GPS The Global Positioning System (GPS) is a space-based satellite navigation
system that provides location and time information in all weather conditions,
anywhere on or near the earth where there is an unobstructed line of sight to four or
more GPS satellites. The GPS system concept is based on time. The satellites carry
very stable atomic clocks that are synchronized to each other and to ground clocks.
GPS satellites continuously transmit their current time and position. A GPS receiver
monitors multiple satellites and solves equations to determine the exact position of the
receiver and its deviation from true time. The receiver’s earth-centered solution
25
location is usually converted to latitude, longitude and height relative to an ellipsoidal
earth model.
Wireless Communication V-REP allows simulating wireless communications in a
very flexible way: data can be emitted into a specific direction, and over a specific
distance. Emitted data can then be received if the receiver is located within the
specified emission area. Refer to the corresponding functions in the regular API for
more details. Wireless emission/reception activities can be visualized by enabling the
Visualize wireless emissions and Visualize wireless receptions items in the
environment dialogue.
After the quadcopter has been built, the simple navigation system is applied to
move from one point to another as simple as it is. If the quadcopter can move properly,
it means that the quadcopter can be used for this research and the other quadcopters
are also built by following the design of the first quadcopter. After the quadcopters are
ready, the communication among quadcopters are connected so later, those
quadcopters can communicate to each other in completing their tasks.
3.1.3 Simulating the Swarm-based Exploration Algorithm to the Quadcopter
After studying and analysing algorithms related to the exploration algorithm as
mentioned in Section 3.1.1, the best technique is adopted for the path planning and
navigation system. For example, the use of GPS can be an alternative as the
quadcopter can move from and to a specific point coordinate as used by (Selby et al.,
2011; Rengarajan and Anitha, 2013). The use of the GPS can be categorized as a
proper implementation of the navigation system compared to the other methods
mentioned in Chapter 2 Section 2.3. Furthermore, for the exploration algorithm, a new
simple algorithm will be designed and developed based on the national search and
26
rescue manual technique described in Authority (2014). There are adjustments which
must be done for this combination to get a complete exploration algorithm. It means
that there must be uncorrelated systems among those techniques and the adjustment
must be applied to fuse those techniques.
According to Authority (2014) mentioned in National Search and Rescue
Manual, Australia:
“In an expanding square search, the searching activity begins at a
position that is reported or most likely location. It will expand outwards
in concentric squares. It is an appropriate pattern to be implemented but
it requires accurate navigation to do this pattern. Usually, the first leg of
this pattern will be directed into the wind. It is applied to minimize and
prevent the navigational error. As a kind of search pattern in search and
rescue manual procedure, the square search pattern is always used when
the location of target is recognized to be in a small area relatively. It
means that the location of target is no more then 15−20 NM where 1
NM = 1.852 km, from the start point of searching.”
The first two legs are held to a distance that is equal to the spacing between the
track legs. After the same two legs are done, it will be increased by another track
spacing. The direction can be to the right or left. It depends on the position of the
observer’s point of view. After one searching is completed, the direction of the
searching should be changed by 45◦. However, the position of a final track should be
the same as the initial search track at the start point. The number of search legs can
vary, for instance, it may be 5 and it will be increased by an increment of 4, 9, 13, 17
and etc.
27
3.1.4 Analysing the Algorithm Performance of the Quadcopter
After implementing the new algorithm, analyses are conducted:
1. The movement of the quadcopter. It should be reviewed to see whether
the quadcopter moves properly from one point to another. It shows that if
there is an error in the movement, the exploration activity cannot be
completed as expected.
2. The cooperation among those quadcopters. It should be reviewed to see
whether those quadcopters can communicate and cooperate through that
communication network line. The network communication takes an
important role because it establishes the communication among those
quadcopters.
3. The exploration activity of each quadcopter. It must be analysed to see
whether those quadcopters have covered a determined area as expected.
The developed exploration algorithm must be applied properly to achieve
the main task of a quadcopter team.
4. The completion task. Finally, the performance must be analysed whether
those quadcopters can finish the whole task from start point until the goal
with the expected performance and result.
3.1.5 Comparing the Proposed Algorithm with the Other Algorithms
If the quadcopters have successfully completed their tasks based on the previous
experiment, the comparison is conducted to contrast with another method. The swarm-
based exploration algorithm implemented with the expanded square pattern will be
compared to another exploration algorithm introduced by Yamauchi (1997) to prove
that this swarm-based exploration algorithm has better results because of the expanded
28
square pattern compared to the random selection. It will also be compared to another
swarm-based exploration algorithm described by Zelenka and Kasanicky (2014) to
prove the deployment direction of this swarm-based approach shows improvements.
Some factors related to the performance of the quadcopter as a team is identified such
as time taken, path taken, search pattern and the expansion of the quadcopter’s covered
area during the exploration activity.
3.1.6 Documenting the Results of Simulation, Evaluation, Comparison and
Analysis
After previous sections (Section 3.1.1 until 3.1.5) have been done, the results of the
testing, the analysing and the comparison are documented. The chart line is created to
see the result of the comparisons specifically. The performance and achievement of the
new method are discussed in detail.
29
CHAPTER FOUR
THE SWARM-BASED EXPLORATION ALGORITHM WITH THE
EXPANDED SQUARE PATTERN
In this chapter, we will discuss the swarm-based exploration algorithm with the
expanded square pattern. In exploration, there are three algorithms that are used to
complete the task. The first is Algorithm 1 that describes the main idea of the
exploration activity. Inside Algorithm 1, there are three algorithms that will be
executed: Algorithm 2 for the expanded square pattern, Algorithm 4 for determining
the next location and Algorithm 3 for calculating the covered area.
4.1 THE SWARM-BASED EXPLORATION ALGORITHM WITH THE
EXPANDED SQUARE PATTERN
In this section, the main concept of the swarm-based exploration is introduced. Firstly,
the swarm robotic is a field of study that is concerned with controlling and
coordinating multiple robots and is defined by Dorigo et al. (2014) as:
“the study of how to design groups of robots that operate without
relying on any external infrastructure or on any form of centralized
control and in a robot swarm, the collective behavior of the robots
results from local interactions between the robots and between the
robots and the environment in which they act.”
Therefore, in this research, some quadcopters that can communicate with each
other to perform exploration activity will be used. The concept of this exploration
activity is shown in Figure 4.1.
30
Figure 4.1 General Concept of Exploration Activity
The main exploration algorithm is presented in Algorithm 1. The exploration
activity begins with initializing the point coordinate to start an exploration activity.
This point is derived from the user’s information. The point coordinate should consist
of two points: latitude and longitude that can be identified by the GPS as a guide to
these quadcopters. These points then are transferred to the quadcopters and calculated
by each of them.
Algorithm 1 Exploration
Require: finding location coordinate
initializing (x,y)currentlocation
initializing length of area
initializing width of area
if (x,y)currentlocation == true then
Ddirection ← (north|| east||south||west)
turning to Ddirection
endif
31
for i := (x,y)currentlocation → (x,y)centersquare do
moving forward
updating i
endfor
repeat
determining (x,y)targetsquare
turning right
executing Algorithm 2
executing Algorithm 3
checking status
if (x,y)coveredarea == false then
executing Algorithm 4
else
STOP
endif
until maximum of expansion radius
They will determine the direction of the exploration; one will go to north,
south, east or west respectively. It is decided based on the four cardinal directions
(cardinal points) as shown in Figure 4.2. After determining the direction, each
quadcopter turns to the respective directions. Afterward, every quadcopter will go to a
point to perform the expanded square pattern that is viewed in Algorithm 2. On
arriving there, each quadcopter will perform the expanded square pattern adopted from
the National Search and Rescue Manual, Australia by Authority (2014) as shown in
Figure 4.3.
Figure 4.2 Cardinal Directions (Cardinal Points)
32
Algorithm 2 Expanded Square Pattern
Require: finding location coordinate
while (x,y)currentlocation ≠ (x,y)targetsquare do
moving to (x,y)targetsquare
turning 90○ to left
updating (x,y)currentlocation
(x,y)targetsquare ← (x,y)targetsquare + 0.5 meters
endwhile
Figure 4.3 The Expanded Square Pattern
When they go across the maximum local radius point, they will stop and generate
communication lines to each other. They will communicate to determine the next area
to be explored. Prior to that, they will calculate the covered area of the expanded
square pattern. It is done by applying Algorithm 3. As shown in algorithm 3, at the
beginning, the robot must initialize its current location coordinate. Then, it does the
nested loop to store x and y coordinate to the array size[x][y]. The (x)diagonal and
(y)diagonal in algorithm 3 is derived from the equation 4.4. In the equation 4.4, the point
coordinate of the target square is subtracted by the coordinate of the expanded square
pattern centre and subtracted again by 0.5. After that, this result is used to subtracted
the coordinate of centre square to obtain the (x)diagonal and (y)diagonal.
33
Algorithm 3 Covered Area
if (x)currentlocation == true ˄ (y)currentlocation == true then
for i := (x)diagonal → (x)currentlocation do
for j := (y)diagonal → (y)currentlocation do
size[x][y] = (i,j)
endfor
endfor
endif
After they store the point coordinate of the covered area, they will determine
the next location described in Algorithm 4. The algorithm 4 covers the path planning
algorithm. In determining the next location, each quadcopter will send its current
location coordinate and covered area to the others. On receiving the coordinate point,
they will calculate it with their own coordinate point to find the middle point between
these two points. In this case, the calculation is controlled by the following rules:
1) the north quadcopter will calculate its coordinate with the east
quadcopter’s coordinate;
2) the east quadcopter will calculate its coordinate with the south
quadcopter’s coordinate;
3) the south quadcopter will calculate its coordinate with the west
quadcopter’s coordinate;
4) the west quadcopter will calculate its coordinate with the north
quadcopter’s coordinate.
To obtain the middle point, the equation 4.1 is implemented in Algorithm 4.
After receiving the partner location, the robot subtracted its point coordinate with its
partner point coordinate and divide it by two and plus the partner coordinate again. In
the next step, they will go to the middle point and turn right. After that, they will check
whether the next centre point of the expanded square pattern has been covered or not.
34
If it is no, the robot can continue to perform the next expanded square pattern.
However, if it is yes, they need to determine the location of the next expanded square
pattern by using the equation 4.3. In the equation 4.3, the value of range explore is
obtained from the equation 4.2. The value of length of area is divided by two and its
result is divided by four. After obtaining the value of range explore, in the equation
4.3, this value is multiplied by 2 and added by the current location of the robot. Then,
the robot can do the exploration again by using the same expanded square pattern.
These processes are repeated until they reach the maximum global radius point.
Algorithm 4 Next Location Planning
obtaining (x,y)currentlocation; (x,y)centersquare; (x,y)targetsquare
sending (x,y)currentlocation; (x,y)coveredarea
receiving (x,y)partnerlocation; (x,y)partnercoveredarea
(x,y)middlepoint ← equation 4.1
while (x,y)currentlocation ≠ (x,y)middlepoint do
moving to (x,y)middlepoint
updating (x,y)currentlocation
endwhile
if (x,y)currentlocation == (x,y)middlepoint then
turning right
if (x,y)centersquare == (x,y)coveredarea then
(x,y)centersquare ← (x,y)targetsquare + 1.5
(x,y)targetsquare ← equation 4.2
for i := (x,y)currentlocation → (x,y)centersquare do
moving forward
updating (x,y)currentlocation
endfor
elseif (x,y)centersquare ≠ (x,y)coveredarea then
for i := (x,y)currentlocation → (x,y)centersquare do
moving forward
updating (x,y)currentlocation
endfor
endif
endif
35
Equations
(𝑥, 𝑦)𝑚𝑖𝑑𝑑𝑙𝑒𝑝𝑜𝑖𝑛𝑡
= (
(𝑥, 𝑦) 𝑐𝑢𝑟𝑟𝑒𝑛𝑡𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛
− (𝑥, 𝑦)𝑝𝑎𝑟𝑡𝑛𝑒𝑟𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛
2) + (𝑥, 𝑦)𝑝𝑎𝑟𝑡𝑛𝑒𝑟
𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛
, 𝑤ℎ𝑒𝑟𝑒 ∀𝑥 ∈ [0, ∞) (4.1)
𝑟𝑎𝑛𝑔𝑒 𝑒𝑥𝑝𝑙𝑜𝑟𝑒 =
𝑙𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑎𝑟𝑒𝑎2⁄
4 (4.2)
(𝑥, 𝑦)𝑡𝑎𝑟𝑔𝑒𝑡
𝑠𝑞𝑢𝑎𝑟𝑒 = (𝑟𝑎𝑛𝑔𝑒 𝑒𝑥𝑝𝑙𝑜𝑟𝑒 ∗ 2) + (𝑥, 𝑦)𝑐𝑢𝑟𝑟𝑒𝑛𝑡
𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 (4.3)
(𝑥, 𝑦)𝑑𝑖𝑎𝑔𝑜𝑛𝑎𝑙 = (𝑥, 𝑦)𝑐𝑒𝑛𝑡𝑒𝑟𝑠𝑞𝑢𝑎𝑟𝑒 − (((𝑥, 𝑦)𝑡𝑎𝑟𝑔𝑒𝑡𝑠𝑞𝑢𝑎𝑟𝑒 − (𝑥, 𝑦)𝑐𝑒𝑛𝑡𝑒𝑟𝑠𝑞𝑢𝑎𝑟𝑒) − 0.5) (4.4)
36
CHAPTER FIVE
EXPERIMENTAL SETUP
In this section, a quadcopter has been designed using V-REP (Virtual Robot
Experimentation Platform) by E. Rohmer (2013) that has become a model standard in
robotics research facilitating other researchers to conduct their works. The simulation
platform is discussed in Section 5.1. Here, the advantage of the V-REP is described.
Moreover, the experiment protocol is presented in Section 5.2. In this section, the
information of the experiment is explained.
5.1 SIMULATION PLATFORM
A robotic simulator is used to create embedded applications for the robot
without depending physically on the actual machine. The simulator used in the
experiment is V-REP. V-REP is the Swiss army knife among robots simulators. The
robot simulator V-REP, integrated development environment, is based on a distributed
control architecture: each object/model can be individually controlled via an
embedded script, a plugin, an ROS node, a remote API client, or a custom solution.
This makes V-REP very versatile and ideal for multi-robot applications. Controllers
can be written in C/C++, Python, Java, Lua, Matlab, Octave or Urbi. V-REP is used
for fast algorithm development, factory automation simulation, fast prototyping,
verification, robotics-related education, remote monitoring, safety double-checking,
etc.
In addition, V-REP is highly customizable simulator: every aspect of a
simulation can be customized. Moreover, the simulator itself can be customized and
tailored so as to behave exactly as desired. This is allowed through an elaborate
37
Application Programming Interface (API). Six different programming or coding
approaches are supported, each having particular advantages over the others but all six
are compatible.
Nogueira and Lucas (n.d.) compared V-REP and Gazebo robotic simulators
using a basic experiment in robot control using fuzzy logic and evolutionary robotics.
They conclude that V-REP is a more intuitive and user-friendly simulator, and packs
more features. Gazebo is more integrated into ROS framework and is an open source
solution which means it allows for complete control over the simulator. However, it
needs a number of external tools to match up with V-REP functionalities. Also,
Gazebo is more hardware-demanding than V-REP. So, the cognitive scientist should
have a better chance of implementing and validating their cognitive theories using V-
REP than Gazebo.
5.2 EXPERIMENTAL PROTOCOL
The use of the quadcopter has advantages. One of them is regarding the
simplicity of its propulsion and navigation system that consists of four independent
motors and propellers with a fixed pitch. Each pair of opposite propellers rotates in the
same direction to avoid yaw torque during the roll and pitch movement. The system
dynamics are controlled by thrust and torque triggered by every motor unit. The design
of the quadcopter used in this simulation can be seen in Figure 5.1. The additional
module which should be used are the GPS (Global Positioning System) for the
navigation system of the quadcopter, and the wireless module for communication
among the quadcopters as explained in Section 3.1.2.
38
Figure 5.1 The Design of the Quadcopter in the VREP Simulator
In this research, we conduct three experiments divided into three scenarios.
The first scenario, we create two quadcopters, the second scenario has four
quadcopters and for the third scenario, we have eight quadcopters. For every scenario,
the wide of the area is the same which is 24𝑚 × 24𝑚 as shown in Figure 5.2. The area
is composed of the grid cells and the size of each cell is 1𝑚 × 1𝑚. The quadcopter
moves in the middle of the cell. The velocity of all quadcopters is 0.5 𝑚𝑠⁄ . All
quadcopters fly at the altitude of five meters. In this simulation, the effect of wind
disturbance is ignored. At the beginning of the simulation, all quadcopters are given
the information of the environment about the length and the width and the coordinate
of starting point. Thus, the quadcopter uses the GPS to traverse along in the
determined space.
One more property is also equipped to all quadcopters. This property is
provided in the V-REP, named graph. So when they move, they will leave a yellow
mark. This is aimed to see more clearly the final result of the quadcopter’s movement.
39
Figure 5.2 The Design of the Environment in the VREP Simulator
Figure 5.3 shows the starting point of all quadcopters in every scenario for the
expanded square pattern. The starting point of the quadcopters of the expanded square
pattern algorithm is fixed and determined by the user. For two quadcopters and four
quadcopters, the starting point is at the centre of the environment as shown in Figure
5.3[a] and 5.3[b] respectively and for the eight quadcopters, the first four quadcopters
are placed at the centre while the other four is placed between the centre and the
corner of the environment as shown in Figure 5.3[c].
Figure 5.3 The Staring Point of the Expanded Square Pattern Quadcopters: [a] Two
Quadcopters, [b] Four Quadcopters and [c] Eight Quadcopters
40
Figure 5.4 and 5.5 shows the starting point of all quadcopters in every scenario
for the frontier baseline from Yamauchi (1997) and cellular automata from Zelenka
and Kasanicky (2014) respectively. The starting point of both is placed at the edge of
the environment because there is no specific provision in these algorithms according to
Yamauchi (1997) and Zelenka and Kasanicky (2014). It is different from the expanded
square pattern because the quadcopter of the expanded square pattern implements their
exploration algorithm based on cardinal point as explained in Section 4.1 and shown in
Figure 4.2. The placement of the quadcopters is determined by the user. It is applied
for all scenarios.
Figure 5.4 The Starting Point of the Frontier Baseline Quadcopters: [a] Two
Quadcopters, [b] Four Quadcopters and [c] Eight Quadcopters
Figure 5.5 The Starting Point of the Cellular Automata Quadcopters: [a] Two
Quadcopters, [b] Four Quadcopters and [c] Eight Quadcopters
41
All scenarios are captured as the result of the experiments. In all experiments,
the simulation is run until the quadcopters finish their exploration. The completion of
the exploration can be defined as a situation where a group of the quadcopter has
covered the determined space. Then, the covered space is calculated based on every
grid cell in the area. The simulation will be terminated when all quadcopters stop the
exploration.
The cooperation of the swarm quadcopters is analysed by observing two
things: the intensity of communication and the exploration and the finishing time
among all members in a group. Therefore, when the quadcopters explore the area, the
act of communication and the finishing time is recorded.
42
CHAPTER SIX
SIMULATION
In this chapter, we will be simulating the exploration algorithm with the expanded
square pattern, the cellular automata from Zelenka and Kasanicky (2014) and the
frontier based approach introduced by Yamauchi (1997). In order to evaluate the
performance of all exploration algorithms, the data from every simulation on the
different number of quadcopters are collected and documented. These data will be
compared, evaluated and analysed in Chapter 7. The various number of swarm robots
is implemented in this simulation which are two, four and eight quadcopters. The data
that will be collected and compared are the simulation time, the spaces covered by the
quadcopters and the time needed for each quadcopter.
6.1 EXPERIMENT I: THE SWARM-BASED EXPLORATION ALGORITHM
WITH THE EXPANDED SQUARE PATTERN
The snapshots of the simulation are shown in Figure 6.1, 6.2 and 6.3 for the different
number of quadcopters which are two, four and eight respectively.
6.1.1 Experiment I: Two Quadcopters
In Figure 6.1, it can be seen that the number of quadcopters that is applied are two and
Figure 6.1[a] shows two quadcopters performing their first expanded square patterns.
After completing the first expanded square pattern, the quadcopters determine their
next location to be explored. Figure 6.1[b] shows that they start to perform their
second expanded square patterns. The interesting part is presented in Figure 6.1[c]
when the quadcopters determine the next location after the second expanded square
43
pattern. It is shown in Figure 6.1[c], since their next locations have been completed at
the first expanded square pattern, both increase their next point coordinate to perform
the third expanded square pattern. The same situation occurs for the next expanded
square pattern as shown in Figure 6.1[d]. In Figure 6.1[e], the quadcopters are going to
do the next exploration algorithm followed by Figure 6.1[f] that shows that the
quadcopters can complete all their exploration tasks. In this scenario, each quadcopter
has to perform six square patterns to cover the whole area.
6.1.2 Experiment I: Four Quadcopters
Figure 6.2 shows the performance of four quadcopters in completing their tasks. In
Figure 6.2[a], they are in the middle of their work for the first expanded square
pattern. Figure 6.2[b] presents four quadcopters that almost finish the first expanded
square pattern. In Figure 6.2[c], the situation is different from the first scenario for the
two quadcopters. The next location for the third expanded square pattern is not
through the area that has been completed. Hence, the quadcopter does not need to
extend its next point coordinate. Figures 6.2[d] and [e] show the situation where the
quadcopters perform their last expanded square pattern. Finally, in Figure 6.2[f], all
quadcopters finish their jobs. In this scenario, each quadcopter has to perform three
expanded square patterns to cover the whole area.
6.1.3 Experiment I: Eight Quadcopters
Figure 6.3 presents the work of eight quadcopters. Figure 6.3[a] gives us a picture of
eight quadcopters starting their exploration. They are dispersed all over the place. In
Figure 6.3[b] and [c], all quadcopters are in the middle of completing their first
expanded square pattern. Figure 6.3[d] describes all quadcopters are going to their
44
next location of expanded square pattern. In Figure 6.3[e], all almost finish their work
and in Figure 6.3[f], all quadcopters have completed their tasks. In this scenario, every
quadcopter must perform only two expanded square patterns to explore the whole area.
Figure 6.1 Two Quadcopter Simulation on Experiment I. The time format is described
in mm:ss. [a] at 01:27, [b] at 02:53, [c] at 05:46, [d] at 11:32, [e] at 17:18 and [f] at
23:03
45
Figure 6.2 Four Quadcopter Simulation on Experiment I. The time format is described
in mm:ss. [a] at 00:44, [b] at 01:28, [c] at 02:55, [d] at 05:51, [e] at 08:46 and [f] at
11:42
46
Figure 6.3: Eight Quadcopter Simulation on Experiment I. The time format is
described in mm:ss. [a] at 00:20, [b] at 00:40, [c] at 01:20, [d] at 02:40, [e] at 04:00
and [f] at 05:21
47
In order to evaluate the performance of the simulations in Chapter 7, the data
from three different scenarios on the same environment are collected as shown in
Table 6.1.
Table 6.1 The Duration of the Expanded Square Pattern Algorithm for Different
Number of Quadcopters
Space
(meter2)
Two
Quadcopters
(mm:ss)
Four
Quadcopters
(mm:ss)
Eight
Quadcopters
(mm:ss)
36 01:27 00:44 00:20
72 02:53 01:28 00:40
144 05:46 02:55 01:20
288 11:32 05:51 02:40
432 17:18 08:46 04:00
576 23:03 11:42 05:21
6.2 EXPERIMENT II: THE SWARM-BASED EXPLORATION ALGORITHM
BASED ON THE FRONTIER BASED APPROACH
In this experiment, the exploration based on the frontier based approach introduced by
Yamauchi (1997) is implemented. The snapshots of the simulation are shown in
Figure 6.4, 6.5 and 6.6 for the different number of robots.
6.2.1 Experiment II: Two Quadcopters
First of all, in Figure 6.4[a], it can be seen two quadcopters move to two different
areas for exploration. The first quadcopter goes up and the second quadcopter goes to
the right side of the environment. However, Figure 6.4[b] shows that the first
quadcopter goes down towards the direction of its initial position. And then, in Figure
48
6.4[c], the second quadcopter which went to the right side, moves to the areas
explored by the first quadcopter. After a few minutes, both quadcopters explore the
same area which is in the bottom area. And again, a few minute later, both move to the
same area but now they are in the upper area. It can be seen in Figure 6.4[d] and [e].
Finally, both can finish their exploration and end it in the upper area. In this scenario,
the second quadcopter finishes earlier than the first quadcopter.
6.2.2 Experiment II: Four Quadcopters
Furthermore, Figure 6.5 presents four quadcopters that implement the frontier baseline
algorithm. From Figure 6.5[a], it can be seen that the deployment of four quadcopters
is not spread evenly. Three quadcopters go up and one quadcopter goes right. Figure
6.5[b] also does not show a different situation from the first one. But, in Figure 6.5[c],
one of three quadcopters starts to separate from the other two. Then, four quadcopters
are separated evenly: two and two as seen in Figure 6.5[d]. However, an uneven
distribution happens again. Three quadcopters explore the upper environment and only
one quadcopter explores the bottom area. Finally, Figure 6.5[f] views the end of four
quadcopters’ work.
49
Figure 6.4 Two Quadcopter Simulation on Experiment II. The time format is described
in mm:ss. [a] at 01:44, [b] at 03:28, [c] at 06:55, [d] at 13:50, [e] at 20:45 and [f] at
27:39
50
Figure 6.5 Four Quadcopter Simulation on Experiment II. The time format is
described in mm:ss. [a] at 00:50, [b] at 01:39, [c] at 03:18, [d] at 06:36, [e] at 09:54
and [f] at 13:12
6.2.3 Experiment II: Eight Quadcopters
Figure 6.6 presents the simulation of eight quadcopters. In Figure 6.6[a], eight
quadcopters are distributed evenly and it is different from the previous simulation
51
(four quadcopters). Figure 6.6[b] also shows a good deployment of all quadcopters.
However, from Figure 6.6[c], one quadcopter starts to separate from its group. That
quadcopter explores the right top of the environment alone. Even if this situation
happens, the normal condition of distribution can be conducted again. But, some odd
situation is shown in Figure 6.6[d] where there are two quadcopters that go back to the
explored area. These two quadcopters have wasted time because they explore the same
area again. Figure 6.6[e] shows that there is one quadcopter which still explores while
the others have done their jobs. Lastly, in Figure 6.6[f], it can be seen all quadcopters
get their jobs done.
In order to evaluate the performance of those simulations in Chapter 7, the data
from three different scenarios on the same environment are collected as shown in
Table 6.2.
Table 6.2 The Duration of the Frontier Baseline Algorithm for Different Number of
Quadcopters
Space
(meter2)
Two
Quadcopters
(mm:ss)
Four
Quadcopters
(mm:ss)
Eight
Quadcopters
(mm:ss)
36 01:44 00:50 00:36
72 03:28 01:39 01:11
144 06:55 03:18 02:22
288 13:50 06:36 04:44
432 20:45 09:54 07:06
576 27:39 13:12 09:28
52
Figure 6.6 Eight Quadcopter Simulation on Experiment II. The time format is
described in mm:ss. [a] at 00:36, [b] at 01:11, [c] at 02:22, [d] at 04:44, [e] at 07:06
and [f] at 09:28
53
6.3 EXPERIMENT III: THE SWARM-BASED EXPLORATION ALGORITHM
WITH THE CELLULAR AUTOMATA
In this experiment, the exploration algorithm with the cellular automata introduced by
Zelenka and Kasanicky (2014) is implemented. The snapshots of the simulation are
shown in Figure 6.9, 6.10 and 6.11 for the different number of robots. For more
information about the cellular automata, in Section 6.3.1, the concept of virtual
mapping in cellular automata is explained.
6.3.1 The Virtual Mapping in the Cellular Automata
In cellular automata, each quadcopter has own world representation (map). World map
of the quadcopter is the cellular grid. The quadcopter uses this grid for the navigation
system and as a memory. Each situation which can be recognized by the quadcopter is
entered into its own virtual map. The initial settings of the virtual map are illustrated in
Figure 6.7.
Figure 6.7 The Virtual Map in the Cellular Automata. The determined space consists
of 5 × 5 cells and the size of the cell is set to 5𝑚 × 5𝑚
In cellular automata, the quadcopter’s method to calculate the covered space is
different from the expanded square pattern and frontier baseline. Here, the use
of 5𝑚 × 5𝑚 grid cell becomes the indicator to say whether the quadcopter has visited
54
the defined area or not. Therefore, if the quadcopter has visited one grid cell, it
assumes that the quadcopter has covered every pixel in the 5𝑚 × 5𝑚 grid cell. Thus, it
can be said that the quadcopter has explored the area based on the virtual map but it is
not accepted based on the covered space explained in Section 5.2. Figure 6.8 shows
the quadcopter in every group that discovered all 5𝑚 × 5𝑚 grid cells.
In Section 6.3.2 to 6.3.4, the result of the quadcopter of cellular automata is
explained. Although the method of this algorithm in calculating the covered space has
different point view, the deployment and cooperation among the quadcopters can still
be examined to see the effect of coordinated movement and the quadcopter’s
effectiveness compared to the other algorithms since it has become problems
mentioned in Section 1.2.
Figure 6.8 The Result of the Cellular Automata in the Virtual Map Viewpoint: [a] Two
Quadcopters, [b] Four Quadcopters and [c] Eight Quadcopters
6.3.2 Experiment III: Two Quadcopters
Figure 6.9[a] indicates the even deployment of two quadcopters. The first quadcopter
goes up and the second one goes to the right side. But, in Figure 6.9[b], the first
quadcopter goes back to its track. Then, two quadcopters start to move to the upper
direction simultaneously on the different side of the environment. It can be seen in
55
Figure 6.9[c]. The first quadcopter then reaches the upper area earlier than the second
robot and goes back to the lower direction. The second quadcopter also moves to the
center direction of the environment and comes back to the track to go to the upper
environment. All situations are viewed in Figure 6.9[d]. Based on Figure 6.9[e], the
first quadcopter moves to the center of the environment while the second quadcopter
moves to an area that has been explored by the first quadcopter. Here, the repetition of
the exploration is done by the second quadcopter. Finally, Figure 6.9[f] shows two
quadcopters end their exploration. Based on Figure 6.9, the path taken by two
quadcopters have not covered completely the environment compared to the previous
two experiments.
6.3.3 Experiment III: Four Quadcopters
For a different scenario on this simulation, all activities are captured in Figure 6.10.
This simulation starts with the quadcopters that move separately as shown in Figure
6.10[a]. The same situation also happens in Figure 6.10[b]. There is no significant
occurrence until this time. However, after that, the second quadcopter moves back
after it goes up while the other three still maintain their directions. It is shown in
Figure 6.10[c]. Furthermore, the different situation occurs in Figure 6.10[d] when the
fourth quadcopter visits the area covered by the third quadcopter. In Figure 6.10[e],
four quadcopters are seen to explore the same area while there is an area which is still
not covered. Here, the deployment of all quadcopters is not maintained properly. In the
end, Figure 6.10[f] observes that the quadcopter stops after completing its exploration.
56
Figure 6.9 Two Quadcopter Simulation on Experiment III. The time format is
described in mm:ss. [a] at 00:13, [b] at 00:25, [c] at 00:50, [d] at 01:39, [e] at 02:29
and [f] at 03:18
57
Figure 6.10 Four Quadcopter Simulation on Experiment III. The time format is
described in mm:ss. [a] at 00:15, [b] at 00:30, [c] at 01:05, [d] at 02:01, [e] at 03:06
and [f] at 04:02
6.3.4 Experiment III: Eight Quadcopters
In addition, for the scenario of the eight quadcopters, at the beginning, the movements
of the quadcopters are so crowded. It is shown in Figure 6.11[a]. The same situation
also appears in Figure 6.11[b] and an uneven separation of quadcopters occurs here.
58
Six quadcopters move to the right direction and only two quadcopters which go up.
However, in Figure 6.11[c], the distribution of the quadcopters have become more
evenly spread. Although until this time the situation is conducted well, Figure 6.11[d]
displays that there are four quadcopters’ paths that interfere with each other. But, after
a few minutes, the situation looks better in Figure 6.11[e] when more quadcopters
reach the top of the environment and the distribution is even. At the end of the
simulation, eight quadcopters stop at different places that are far from each other as
shown in Figure 6.11[f].
In order to evaluate the performance of these simulations in Chapter 7, the data
from the three different scenarios on the same environment are collected as shown in
Table 6.3.
Table 6.3 The Duration of the Cellular Automata Algorithm for Different Number of
Quadcopters
Space
(meter2)
Two
Quadcopters
(mm:ss)
Space
(meter2)
Four
Quadcopters
(mm:ss)
Space
(meter2)
Eight
Quadcopters
(mm:ss)
15 00:13 25 00:15 30 00:10
25 00:25 50 00:30 60 00:21
50 00:50 95 01:05 120 00:40
65 01:39 130 02:01 160 01:23
85 02:29 170 03:06 200 02:05
100 03:18 190 04:02 240 02:46
59
Figure 6.11 Eight Quadcopter Simulation on Experiment III. The time format is
described in mm:ss. [a] at 00:10, [b] at 00:21, [c] at 00:40, [d] at 01:23, [e] at 02:05
and [f] at 02:46
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CHAPTER SEVEN
RESULT AND DISCUSSION
The simulation development has taken a period of time which starts from the robot’s
simple navigation system that can only move from one point to another. Finally, the
research reaches the objective which is the development of the exploration algorithm
for a swarm of quadcopters that can implement the expanded square pattern to do the
exploration. In this chapter, the results of experiments that have been carried out in
Chapter 6 are discussed. The data from the different exploration algorithms are
compared. The results of the comparison are analysed and evaluated. All data that
have been collected is transformed into line graph in order to be observed clearly and
easily.
7.1 THE PERFORMANCE OF THE EXPLORATION ALGORITHMS
The performance of the different exploration algorithms is presented in this section.
The statistical test using the Vargha-Delaney A test (Vargha and Delaney, 2000) will
be conducted to observe and quantify the performance of each algorithm.
7.1.1 Experiment I: The Swarm-based Exploration Algorithm with the Expanded
Square Pattern
Three hypotheses for the first experiment are:
H10: The group of four quadcopters explores the environment faster than the
group of two quadcopters.
H20: The group of eight quadcopters explores the environment faster than the
group of four quadcopters.
61
H30: The group of eight quadcopters explores the environment faster than the
group of two quadcopters.
7.1.1.1 Result and Evaluation
As the proposed algorithm in this research, the result of the exploration algorithm with
the expanded square pattern shown in Figure 7.1 gives a satisfactory result. It is
because the number of swarm robots that is implemented in this algorithm affects its
performance. Figure 7.1 shows the first scenario that uses two quadcopters spend more
time compared to the second and third scenario. The second scenario that implements
the four quadcopters spend more time compared to the third scenario. In other words,
it is clear that as the number of quadcopters increase, the time needed to cover the
whole area is decreased.
Figure 7.1 The Comparison of the Different Number of Quadcopter’s Performance in
the Exploration Algorithm with the Expanded Square Pattern
62
For example, in scenario 1 (two quadcopters), to cover the area of 36 m2, the
quadcopters spend 1 minute and 27 seconds. If it is compared to scenario 2 (four
quadcopters), for 1 minute 27 seconds, the quadcopters can cover the area of 72 m2
and to scenario three (eight quadcopters), the quadcopters can cover the area of more
than 144 m2. The other example can be seen in the minute 05:46. Here, in scenario 2,
the quadcopters can only cover the area of 144 m2 while in the scenarios 2 and 3, the
quadcopters can cover a larger area which are 288 m2 and 576 m2 respectively. From
this point of view, it can be seen that a group of eight quadcopters is able to cover four
times faster than a group of two quadcopters and a group of four quadcopters is able to
cover twice as fast than a group of two quadcopters.
7.1.1.2 Analysis: The Vargha-Delaney A Test
The result of the Vargha-Delaney A test is shown in Table 7.1.
Table 7.1 The Magnitude of the Effect Size Indicated by A Test Score: Square Pattern
Simulation Square Pattern Annotation
A test score
(2 and 4 Quads) 0.64 Medium
A test score
(4 and 8 Quads) 0.75 Large
A test score
(2 and 8 Quads) 0.86 Large
Analysing the data on simulation time of the proposed algorithm with the
expanded square pattern from two quadcopters compared with four quadcopters using
the A test returned a value of 0.64. Since 0.64 is in the range of up to 64, the A test
indicates a medium difference between the two data sets. The different result is
63
retrieved from the comparison of four quadcopters and eight quadcopters which have a
value of 0.75 that indicates a large difference. Moreover, the result of the comparison
between two quadcopters and eight quadcopters gives a bigger value which is 0.86.
Based on these results, we can conclude that H10 is accepted with a medium difference
and H20 and H30 are accepted with large differences.
From the analysis described in this section, we can summarize that the
magnitude of the difference between the swarm of robots 2, 4 and 8 in terms of time
are large which means that the number of robots to explore an area affects the
effectiveness of the exploration performance.
7.1.2 Experiment II: The Swarm-based Exploration Algorithm based on the
Frontier Based Approach
The three hypotheses for the second experiment are:
H40: The group of four quadcopters explores the environment faster than the
group of two quadcopters.
H50: The group of eight quadcopters explores the environment faster than the
group of four quadcopters.
H60: The group of eight quadcopters explores the environment faster than the
group of two quadcopters.
7.1.2.1 Result and Evaluation
In experiment II, the similar graph pattern is shown in Figure 7.2 for the frontier
baseline algorithm. It can also be highlighted that the number of swarm robots
influences their performance. From Figure 7.2, it is obvious that two quadcopters need
64
more time than four quadcopters and eight quadcopters and four quadcopters need
more time than eight quadcopters to cover an area with the same size.
Figure 7.2 The Comparison of the Different Number of Quadcopter’s Performance in
the Exploration Algorithm based on the Frontier Based Approach
For instance, two quadcopters explore the area of 72 m2 and spend 3 minutes
and 28 seconds. But in 3 minutes and 28 seconds, four and eight quadcopters may
cover more than 144 m2. Another instance can be viewed in the thirteenth minute. At
that time, two quadcopters may cover about 288 m2 while four quadcopters almost
finish their exploration and eight quadcopters have already finished their exploration.
Based on this data, it can be proven that eight quadcopters can explore faster than the
four and two quadcopters which are the same result as the proposed algorithm.
However, in the frontier baseline, eight quadcopters can explore only three times faster
65
than two quadcopters where in the expanded square pattern algorithm, eight
quadcopters can explore four times faster than two quadcopters.
7.1.2.2 Analysis: The Vargha-Delaney A Test
The result of the Vargha-Delaney A test is shown in Table 7.2.
Table 7.2 The Magnitude of the Effect Size Indicated by A Test Score: Frontier
Baseline
Simulation Frontier
Baseline Annotation
A test score
(2 and 4 Quads) 0.75 Large
A test score
(4 and 8 Quads) 0.61 Medium
A test score
(2 and 8 Quads) 0.75 Large
Furthermore, analysing the simulation time data of the frontier baseline
algorithm from two quadcopters compared with four quadcopters using the A test
returned a value of 0.75. Since 0.75 is above 0.71, the A test indicates a large
difference between the two data sets. The different result is retrieved from the
comparison of four quadcopters and eight quadcopters which are a value of 0.61 that
indicates a medium difference. Moreover, the result of the comparison between two
quadcopters and eight quadcopters gives the same value as the first which is 0.75. That
indicates a large difference. Based on these results, we can conclude that H40 and H60
are accepted with a large difference and H50 is accepted with a medium difference.
66
7.1.3 Experiment III: The Swarm-based Exploration Algorithm with the Cellular
Automata
Three hypotheses for the third experiment are:
H70: The group of four quadcopters explores the environment faster than the
group of two quadcopters.
H80: The group of eight quadcopters explores the environment faster than the
group of four quadcopters.
H90: The group of eight quadcopters explores the environment faster than the
group of two quadcopters.
7.1.3.1 Result and Evaluation
If it is compared to the other algorithms, a different arrangement is presented in Figure
7.3. In this line graph, a group of four quadcopters has the longest time to complete the
exploration. It is followed by a group of two quadcopters and lastly a group of eight
quadcopters.
As shown in all algorithms, eight quadcopters can always be the fastest
group. It happens also in this experiment. They only need 2 minutes and 46 seconds to
cover the area of 240 m2. However, in Figure 7.3, the eight quadcopters can only be
two times faster than the four quadcopters which are the slowest group. Unfortunately,
if the eight quadcopter’s performance of the cellular automata is compared to the
expanded square pattern which can be four times faster and the frontier baseline which
can be three times faster, the eight quadcopters of cellular automata has the lowest
efficiency in its performance. Besides that, if it is seen from the swarm perspective, the
number of quadcopters in this algorithm as shown in Figure 7.3 does not affect its
performance. It is proven by looking at the line graph where, although a group of four
67
quadcopters has more quadcopters than a group of two quadcopters, yet it spends a
longer time than a group of two quadcopters to complete the exploration.
Figure 7.3 The Comparison of the Different Number of Quadcopter’s Performance in
the Exploration Algorithm based on the Cellular Automata
7.1.3.2 Analysis: The Vargha-Delaney A Test
The result of the Vargha-Delaney A test is shown in Table 7.3. Moreover, various
results are recognized from the cellular automata simulation. Analysing the simulation
time data of the cellular automata algorithm from two quadcopters compared with four
quadcopters using the A test returned a value of 0.42. Since 0.42 is below 0.56, the A
test indicates a small difference between the two data sets. The different result is
retrieved from the comparison of four quadcopters and eight quadcopters at a value of
0.61 that indicates a medium difference. Moreover, the result of the comparison
between two quadcopters and eight quadcopters give the value 0.58 that indicates a
68
medium difference. Based on these results, we can say that H70 is not accepted
because of a small difference and H80 and H90 are accepted with medium differences.
Table 7.3 The Magnitude of the Effect Size Indicated by A Test Score: Cellular
Automata
Simulation Cellular
Automata Annotation
A test score
(2 and 4 Quads) 0.42 Small
A test score
(4 and 8 Quads) 0.61 Medium
A test score
(2 and 8 Quads) 0.58 Medium
7.2 THE PERFORMANCE OF GROUPS OF QUADCOPTERS
The performance of algorithms on the different number of quadcopters are presented
in this section. Three hypothesis for the comparison among the group of quadcopters:
H100: The square pattern can explore the environment faster than the frontier
baseline.
H110: The square pattern can explore the environment faster than the cellular
automata.
H120: The frontier baseline can explore the environment faster than the
cellular automata.
7.2.1 The Group of Two Quadcopters
As the first example, from Figure 7.4, it can be noticed that two quadcopters of the
cellular automata are 1.8 times faster than the two quadcopters of the frontier baseline
and 1.4 times faster than the two quadcopters of the expanded square pattern. But the
69
two quadcopters of the expanded square pattern are 1.2 times faster than the two
quadcopters of the frontier baseline. The different time between two algorithms which
are the expanded square pattern and the frontier baseline and the cellular automata is
not too big.
Figure 7.4 The Comparison of the Algorithm’s Performance for the Group of Two
Quadcopters
7.2.1.1 Analysis: The Vargha-Delaney A Test
The result of the Vargha-Delaney A test for two quadcopters is shown in Table 7.4.
The result of the expanded square pattern compared with the frontier baseline using
the A test returned a value of 0.42. Since 0.42 is below 0.56, the A test indicates a
small difference between the two data sets. The different result is gotten from the
comparison of expanded square pattern and cellular automata which are a value of
0.89 that indicates a large difference. Moreover, the result of the comparison between
70
the frontier baseline and the cellular automata gives a bigger value which is 0.94.
Based on these result, we can conclude that H100 is not accepted because of a small
difference. H110 and H120 is not accepted since the cellular automata is faster than the
others with a large difference although the cellular automata cannot cover the
determined area completely.
Table 7.4 The Magnitude of the Effect Size Indicated by A Test Score: Two
Quadcopters
Simulation Two
Quadcopters Annotation
A test score
(Square Pattern and Frontier Baseline) 0.42 Small
A test score
(Square Pattern and Cellular Automata) 0.89 Large
A test score
(Frontier Baseline and Cellular Automata) 0.94 Large
7.2.2 The Group of Four Quadcopters
The same result is also shown in Figure 7.5 for the comparison of four quadcopters.
The expanded square pattern and the frontier baseline have the similar duration for
their exploration times but the cellular automata has the shortest duration compared to
the other algorithms. Nevertheless, according to Figure 7.5, the cellular automata is
only 1.2 times faster than the expanded square pattern and 1.3 times faster than
frontier baseline. This value is reduced from the comparison of two quadcopters even
if the number of quadcopters is increased (from two to four quadcopters).
71
Figure 7.5 The Comparison of the Algorithm’s Performance for the Group of Four
Quadcopters
7.2.2.1 Analysis: The Vargha-Delaney A Test
The result of the Vargha-Delaney A test for two quadcopters is shown in Table 7.5.
The result of the expanded square pattern compared with the frontier baseline using
the A test returned a value of 0.42. Since 0.42 is below 0.56, the A test indicates a
small difference between the two data sets. The different result is retrieved from the
comparison of the expanded square pattern and the cellular automata which is a value
of 0.75 that indicates a large difference. Moreover, the result of the comparison
between the frontier baseline and the cellular automata gives a bigger value which is
0.75. Based on all these results, we can conclude that H100 is not accepted because of
a small difference. H110 and H120 are not accepted since the cellular automata is faster
than the others with a large difference although the cellular automata cannot cover the
72
determined area completely. However, the value of the four quadcopters is reduced
from two quadcopters.
Table 7.5 The Magnitude of the Effect Size Indicated by A Test Score: Four
Quadcopters
Simulation Two
Quadcopters Annotation
A test score
(Square Pattern and Frontier Baseline) 0.42 Small
A test score
(Square Pattern and Cellular Automata) 0.75 Large
A test score
(Frontier Baseline and Cellular Automata) 0.78 Large
7.2.3 The Group of Eight Quadcopters
The final comparison is displayed for the comparison of eight quadcopters. The result
of this comparison is quite interesting because the line pattern that appears is different
from Figure 7.4 and 7.5. Usually, the line of the expanded square pattern and the
frontier baseline is far above the line of the cellular automata but in Figure 7.6, it is
not.
For a group of eight quadcopters, the expanded square pattern is 1.2 times
faster than the cellular automata and 1.7 times faster than the frontier baseline. The
value of this number is reduced again although the number of quadcopters is also
increased. This fact becomes clearer that the effectiveness of the cellular automata’s
performance is not good although it has the shortest duration compared to the others.
73
Figure 7.6 The Comparison of the Algorithm’s Performance for the Group of Eight
Quadcopters
7.2.3.1 Analysis: The Vargha-Delaney A Test
Table 7.6 The Magnitude of the Effect Size Indicated by A Test Score: Eight
Quadcopters
Simulation Two
Quadcopters Annotation
A test score
(Square Pattern and Frontier Baseline) 0.36 Small
A test score
(Square Pattern and Cellular Automata) 0.65 Large
A test score
(Frontier Baseline and Cellular Automata) 0.78 Large
The result of the Vargha-Delaney A test for two quadcopters is shown in Table 7.6.
The result of the expanded square pattern compared with the frontier baseline using
74
the A test returned a value of 0.36. Since 0.36 is below 0.56, the A test indicates a
small difference between the two data sets. The different result is retrieved from the
comparison of the expanded square pattern and the cellular automata which is a value
of 0.65 that indicates a large difference. Moreover, the result of the comparison
between the frontier baseline and the cellular automata gives a bigger value which is
0.78. Based on all these results, we can conclude that H100 is not accepted because of
a small difference. H110 and H120 are not accepted since the cellular automata is faster
than others with a large difference although the cellular automata cannot cover the
determined area completely. However, the value of the four quadcopters is also
reduced from two and four quadcopters.
Therefore, from the comparison in terms of the different number of
quadcopters, it can be summarized that although the cellular automata has the shortest
time compared to the others generally but the acceleration value of the quadcopters in
terms of the number of units does not increase significantly compared to the expanded
square pattern and the frontier baseline. Besides that, the shortest time of the cellular
automata in exploration is caused by the covered space that is performed. The
explanation about this matter will be presented in Section 7.3.2.
7.3 THE COVERED SPACE OF ALL ALGORITHMS
This section is divided into two subsections: the comparison of the number of
expanded square pattern that is performed by each quadcopter (algorithm: expanded
square pattern) and the comparison of covered space from all exploration algorithms.
75
7.3.1 The Comparison of the Different Number of Square Pattern
The covered space is one of the results that must be notified in this research.
Especially, for the swarm-based exploration algorithm with the expanded square
pattern, there is one thing that can be recognized as we look at the simulation captures
which is the expanded square pattern itself.
Figure 7.7 shows that for each quadcopter, the group of two quadcopters
performs more expanded square patterns compared to the group of four and eight
quadcopters. For each quadcopter, the group of four quadcopters performs more
expanded square patterns compared to the group of eight quadcopters. It can be said
that as the number of quadcopters is increased, the number of expanded square pattern
that is performed to cover the whole area is less for each quadcopter.
Figure 7.7 The Comparison of Each Scenario in terms of the Number of Square
Pattern Performed for Each Quadcopter
76
For example, in the group of two quadcopters, each quadcopter performs six
expanded square patterns to complete the task while for the group of four and eight
quadcopters, each quadcopter only performs three and two expanded square patterns
respectively. It means that, as the number of the quadcopter is increased, the task for
each quadcopter is reduced. In other words, the cooperation among the quadcopters in
this experiment is proven i.e. the number of quadcopters will affect the performance of
every single one of them. Hence, as the number of quadcopters is increased, the tasks
for each quadcopter decrease.
7.3.2 The Comparison of the Covered Spaces
In this section, the comparisons of the covered space from all algorithms are exposed.
The covered space is related to the time taken for completing exploration. From this
comparison, it can be noticed clearly the reason of the cellular automata has a short
time for its exploration.
If looking at the first comparison i.e. for two quadcopters (see Figure 7.8), it is
visible that the cellular automata does not cover the whole area as it is done by the
frontier baseline and the expanded square pattern. The same thing happens for four
and eight quadcopters that can be seen in Figure 7.9 and 7.10. The frontier baseline
and the expanded square pattern cover the whole area by exploring every pixel of the
area. However, the cellular automata just go around one space and move along to
explore the other side of the environment. By looking at these figures, it is
recognizable that the cellular automata has less explored space compared to the others
although they can finish the exploration earlier than the others according to its
assumption of the covered space as explained in Section 6.3.1. However, it is
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considered to be not complete according to the expanded square pattern. In order to
observe it more clearly, Figure 7.11 shows the uncovered space marked in red.
Figure 7.8 The Space Comparison for Two Quadcopters: [a] Cellular Automata, [b]
Frontier Baseline and [c] Square Pattern
Figure 7.9 The Space Comparison for Four Quadcopters: [a] Cellular Automata, [b]
Frontier Baseline and [c] Square Pattern
Figure 7.10 The Space Comparison for Eight Quadcopters: [a] Cellular Automata, [b]
Frontier Baseline and [c] Square Pattern
78
Figure 7.11 The Uncovered Space (Red Colour): [a] Two Quadcopters, [b] Four
Quadcopters and [c] Eight Quadcopters
7.4 THE COOPERATION AMONG THE QUADCOPTERS
After looking at the performance of all algorithms, evaluating the different numbers of
quadcopters’ performance and the covered space of algorithms, the finishing time of
members in every group should be observed. As far as the swarm-based algorithms is
concerned, those three algorithms should consider the finishing time as an important
element in the swarm-based system. It means that since the swarm robot is described
as having a collective behaviour (Dorigo et al., 2014), the cooperation among
quadcopters becomes necessary to be noticed in this context. Therefore, the
cooperation among quadcopters is related to the time that quadcopters start and end
their cooperation with each other.
7.4.1 The Group of Two Quadcopters
The first observation is aimed at the group of two quadcopters. Here, as shown in
Figure 7.12, the expanded square pattern has the smallest time gap compared to the
others which is only 5 seconds. It is followed by cellular automata with 19 seconds
and the frontier baseline with 1 minute and 49 seconds.
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Figure 7.12 The Comparison of Duration among Group of Two Quadcopters
7.4.2 The Group of Four Quadcopters
The other result is recognized from the group of four quadcopters from Figure 7.13. In
this scenario, the expanded square pattern has a satisfied finishing time. All of its
quadcopters finish their explorations at the same time which is at 11:42. Nonetheless,
the frontier baseline and the cellular automata, have the different result for each of
their quadcopters. For example, in the frontier baseline, the first quadcopter takes the
longest time to finish which is at 13:12 followed by the second, fourth and third
quadcopters. The same result is also shown in the cellular automata where the first
quadcopter takes the longest time which is at 04:02 and is followed by the fourth,
second and third quadcopter.
80
Figure 7.13 The Comparison of Duration among Group of Four Quadcopters
7.4.3 The Group of Eight Quadcopters
Lastly, the result of the group of eight quadcopters is described in Figure 7.14. Here,
the expanded square pattern’s quadcopters have two different finishing time divided
into two groups of time which are at 04:52 and at 05:21 respectively. The first four
quadcopters are at 04:52 and the second four quadcopters are at 05:21. The time gap is
29 seconds. In the frontier baseline, each quadcopter has a different finishing time.
The longest time gap is 4 minutes and 28 seconds. The same thing happens for the
cellular automata where each quadcopter has a different finishing time. The longest
time is 1 minutes and 44 seconds.
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Figure 7.14 The Comparison of Duration among Group of Eight Quadcopters
7.4.4 The Cooperation of the Quadcopters in terms of Communication
The cooperation of the quadcopters can be seen by observing the communication
generated among members while exploring the determined area. It means that the
number of communication among the quadcopters give an impact on how large the
determined area has been discovered by the quadcopters.
From Table 7.7, it can be seen that the group of two quadcopters generates five
times communication line in order to complete the task. However, as the number of
the quadcopter is increased, the communication among members is reduced. It is
viewed as the group of four quadcopters and eight quadcopters that generate
communication line two and one time(s) respectively.
82
Table 7.7 The Number of Communication and the Covered Space Comparison of the
Expanded Square Pattern
Number of
Communication
The Covered
Space for Two
Quadcopters
The Covered
Space for Four
Quadcopters
The Covered
Space for
Eight
Quadcopters
0 64 m2 128 m2 288 m2
1 128 m2 256 m2 576 m2
2 272 m2 576 m2 -
3 416 m2 - -
4 480 m2 - -
5 576 m2 - -
From Table 7.8, it can be seen that the group of two quadcopters generates
eight times communication line in order to cover 100 m2. However, it can be seen
from Table 7.8, the group of four quadcopters increases the intensity of
communication among the quadcopters up to 21 times to cover 190 m2. However, the
number of the communication line in the group of eight quadcopters is reduced
although the space covered is expanded. It is viewed as the group of eight quadcopters
that generate communication line up to 15 times.
Table 7.8 The Number of Communication and the Covered Space Comparison of the
Cellular Automata
Number of
Communication
The Covered
Space for Two
Quadcopters
The Covered
Space for Four
Quadcopters
The Covered
Space for
Eight
Quadcopters
0 – 4 50 m2 30 m2 60 m2
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5 – 8 100 m2 60 m2 120 m2
9 – 11 - 90 m2 180 m2
12 – 15 - 120 m2 240 m2
16 – 18 - 150 m2 -
19 - 21 - 190 m2 -
One way to measure the cooperation among the quadcopters is to create a
metric that calculate the effectiveness of the quadcopters in a group using what we call
the effectiveness cooperation (EC). The effectiveness cooperation is an estimate of the
cooperation among the quadcopters to accomplish the task. The EC is defined as a
relationship between the communication lines (CL) generated during the exploration
and the covered space (CS) as the final result retrieved. The communication line is an
important factor of the quadcopters’ cooperation in exploration. A simplistic view of
CL is the number of communication required to interact with the other robots. The
effectiveness cooperation metric is defined as:
𝐸𝐶 = 1 − 𝐶𝐿
𝐶𝐿 + 𝐶𝑆.
Table 7.9 The Measurement of Effectiveness Communication
The Number of
Communication
The Group of
Two
Quadcopters
The Group of
Four
Quadcopters
The Group of
Eight
Quadcopters
The Expanded
Square Pattern 0.991 0.997 0.998
The Cellular
Automata 0.990 0.900 0.940
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Table 7.9 describes the measurement value of the effectiveness cooperation in
the expanded square pattern and the cellular automata algorithm. In this table, it shows
that the value of the expanded square pattern is bigger than the value of the cellular
automata. Besides that, the increment value of the expanded square pattern is
comparable to the increment of the number of the quadcopters. However, it has a
different pattern in the cellular automata. Here, the biggest value belongs to the group
of two quadcopters and it is followed by the group of eight quadcopters and four
quadcopters. Therefore, from this point of view, the expanded square pattern has the
best result for the quadcopter’s cooperation.
85
CHAPTER EIGHT
CONCLUSION
Exploration is one of the most important elements in the searching activity as a
technique that can be implemented to gather information in an unknown environment.
To collect information, the best result can be achieved if the exploration can be
completed properly. To ensure good exploration, there are two properties that must be
realized: completeness and effectiveness in terms of space and time respectively.
By implementing the expanded square pattern introduced by the National
Search and Rescue Manual, Australia in Authority (2014), we propose a swarm-based
exploration algorithm using the quadcopter for the outdoor environment that can be
applied in a large area. We present the algorithm of exploration with the expanded
square pattern. In addition, we have successfully simulated the proposed algorithm in
VREP simulator and also the swarm-based exploration algorithm with the cellular
automata (Zelenka and Kasanicky, 2014) and the frontier baseline (Yamauchi, 1997).
Therefore, it can be said that two hypotheses in Section 1.3 are accepted. Additionally,
it also answers the first and second question from Section 1.4 about the existing
exploration algorithm and kind of exploration algorithm that can be implemented to
the swarm of quadcopters. Based on the simulation, three algorithms are analysed,
compared and evaluated.
In this research, we look closely into the issue of completeness and
effectiveness. Completeness requires the exploration to cover most of the area and
effectiveness emphasizes the efficiency of the explorer which is the quadcopter to
finish the exploration in minimum time. In the context of the swarm robotic, efficiency
also considers the efficient number of quadcopter for exploration.
86
In regard to the time factor, the cellular automata has the shortest time to finish
the exploration and is followed by the expanded square pattern and the frontier
baseline. However, it happens because the completeness of the covered area of this
algorithm is not as much as the other exploration algorithms. Besides that, based on
the Vargha-Delaney A test, the expanded square pattern has the best result compared
to the others. According to this result, it can be said that as the number of the
quadcopter is increased, the performance of the quadcopter is better. In other words,
the more the quadcopters are added, the faster they can complete the exploration. In
addition, we compare the performance of each group. It shows that the effectiveness of
the expanded square pattern’s performance is the best compare to the other algorithms.
Therefore, based on this result, it can answer the third question in Section 1.4 that is
the expanded square pattern is the most effective swarm-based exploration algorithm
compared to the frontier baseline and the cellular automata.
The completeness factor is also evaluated by comparing the space covered by
each group of quadcopters. The result is derived from captured simulation and shows
that the cellular automata covers the least area compared to the expanded square
pattern and the frontier baseline. Hence, in term of completeness of space, the
expanded square pattern has the better result than the cellular automata.
Finally, as a part of cooperation concerned, the expanded square pattern has the
best result since most of its quadcopters start and end their exploration at the same
time. And then, as the number of the quadcopter is increased, the number of
communication among the quadcopters is decreased. It means that the number of the
quadcopters affects the performance of the swarm quadcopters.
For future work, we expect an add-on and other improvements to the proposed
swarm-based exploration algorithm with the expanded square patterns to be simulated,
87
compared and analysed. The implementation can be done in a real environment, the
altitude of the quadcopter in exploration can be evaluated to look for the best altitude
for exploration and the object identification technique can be applied to this algorithm
to capture the object.
88
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