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Collaborative Intent Exchange Based Flight Management System with Airborne Collision
Avoidance for UAS Emre Koyuncu*, Cengiz Pasaoglu**, Prof. Gokhan Inalhan*
*Istanbul Technical University **DHMI, General Directorate Of State Airports Authority
and Navigation Service Provider Turkey
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INSTITUTONAL BACKGROUND
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Aeronautics Research Center
• Central Laboratory for Aeronautics Research (2012-) – +7 Faculty, 15 Research Associates, +20 Ph.D. Level
Researchers • Established to promote advanced, interdisciplinary and
experimental research • Research Focus on wide spectrum of Aeronautics
Technologies • Design of manned and unmanned air vehicles,
spacecraft and spacecraft systems • Air Transportation, ATM • Flight Controls, Simulation and Avionics, • Nanoengineered Composites • Engine technologies and combustion • Aerodynamics, Aeroelasticity
• Strong outreach at both university, national and international level
– Nanotechnologies and Material Sciences – Electronics and Computer Science
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Research Partners and Sponsors
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Controls and Avionics Laboratory • Research Focus
– Advanced flight controls and avionics technologies – Unmanned air vehicles design and autonomy – Air Transport and ATM – Spacecraft Systems Design – Data Analytic Modelling, Estimation, Control and
Learning • Notable Achievements
– Designed the first Turkish indigenous commercial avionics systems 2006-2009
– Designed and built the first Turkish university-level autopilot system for UAVs. 2006-2009
– Designed and built the first Turkish University picosat : ITUpSAT I (TUBITAK) 2006-2009
– Designed and built indigenous bus and ADCS components for nano and micro-satellites ITUpSAT II (TUBITAK 108M523) 2009-2012
– Winner of AIAA/AAS Cansat Picosat Competion 2011
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UAS RESEARCH
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TURAC : Environmental Monitoring UAV
TURAC Configuration
Wingspan 4.2 m
Total Length 1.8 m
Height 1.05 m
Front Propeller Diameter 0.43 m
Empty Weight 39 kg
Maximum Takeoff Weight 47 kg
Havelsan‐ITU Project
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TURAC : Environmental Monitoring UAV
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TURAC : Environmental Monitoring UAV
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4D TRAJECTORY MANAGEMENT
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Flight Deck Automation Support with Dynamic 4D Trajectory Management
Short-Term Collision Avoidance – for midair and terrain collision Probabilistic (multi threat) midair and terrain collision monitoring
Fully automated flight control take-over implementation for delayed pilot response
Certifiable Pseudo-random/probabilistic algorithms with modal maneuver approach
Collaborative Tactical Planning – for dynamically changing environmental/operational conditions and use of airspace
Intent based collaborative decision making with the ATC (high level language – FIDL)
On-board tactical conflict monitoring and resolution via air/air link (low level language – AIDL)
Incorporating all tactical level information through air/ground data link + on-board sense
Automated
Required response time is short
Visual Decision Support Tools Novel touch screen enabling synthetic vision screens increasing situational awareness
Integrated information visualization with trajectory planning modules
Head-Up Displays with augmented reality add-ons
Collaborative
Required response time is long
Human Centered Tools
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Advanced Flight Deck Automation Systems
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ITU B737-800NG Flight Deck and Novel Decision Support Systems
Flight Number
Trajectory ofother AC in traffic
Trajectory of Aircraftin command
Time Slider
Difference ofFlight Level with AIC Airspeed
RTA Informations
Aircraft InCommand
AC in traffic
AC in traffic
NAV Radio Informations COM Radio Informations
Heading
Bank Angle
Pitch Angle & Indicator
Airspeed
Vertical Speed
Altitude
Message Panel
Time Slider
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FUTURE OF UAS
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World Outlook
• 82 Billion Dolar Economy in between 2015-2025 (AUVSI)
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Military and Civilian Market Growth
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UAS Market Composition
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Motivation
• Future intensive use of UAVs for civil applications will require integration into national airspace
• Major challenges; – Lack of UAS interaction links with Air Traffic
Management System – Non-standardised performance/behavioural
characteristics of vehicles • Unable to build a TCAS library based on
performance classification
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Motivation
• Next Generation UAV Flight Management Systems should include;
– Additional data links makes UAVs visible in 4D
• machine-to-machine communication – air to air data links
• machine-to-human(operator, traffic controller etc.) communication – air to ground data links
– Multi-layered safety system for long term and short
term conflict avoidance • Tactical real-time aircraft separation • Short term sense-and-avoid
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UAS Integration into NAS
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Intent Based Nominal Operation And Trajectory Planning
Machine‐to‐machine
Machine‐to‐human
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MICRO UAS INTEGRATION TO NAS : ARCHITECTURES
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KAMPUSIHA – UAS for Campus Security
Quadrotor UAV developed at ITU ARC With onboard camera, flashlight and siren
GUI Integrated into ITU Campus Security System for dispatching UAVs based on emergency calls
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IHATAR – UAS for Crop Monitoring
Flight Management Architecture Processed Images of the crop obtained from the onboard multispectral camera
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Integration of Micro UAS’ into the NAS
• High power/high weight manned aircraft type integration is not realistics
• Ever growing interest in Civilian Usages of UAS (Amazon, Google, and even Apple…) – Part of G type airspace usage (1200 ft and below)
• FAA 400ft proposition (500ft mandate for small UAVs)
• 3 Critical (and Main) Factors in real integration – Sense-Avoid Technologies (including geo fencing) – Tracking Technologies – UAS Recovery System : Minimal 3rd part damage/harm in
UAS failures
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UAS Integration via GSM
• Command control via GSM data link
• Sense and Avoid via GSM data link (ADS-B, ADS-C applications)
• Reservation basis restricted areas and broadcasting via GSM network
– Urban areas – Digital Elevation
Maps – Buildings – No fly zones – …
• Positioning via GSM Network
– Differential GSM positioning applications
• Traffic data cloud binding – Track UAS traffic
trough GSM Network
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UAS Integration via GSM
• Command control via GSM data link
• Sense and Avoid via GSM data link (ADS-B, ADS-C applications)
• Reservation basis restricted areas and broadcasting via GSM network
– Urban areas – Digital Elevation
Maps – Buildings – No fly zones – …
• Positioning via GSM Network
– Differential GSM positioning applications
• Traffic data cloud binding – Track UAS traffic
trough GSM Network • Redundancy through LOS
Data Link for C2
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UAS Integration via GSM
• Command control via GSM data link
• Sense and Avoid via GSM data link (ADS-B, ADS-C applications)
• Reservation basis restricted areas and broadcasting via GSM network
– Urban areas – Digital Elevation
Maps – Buildings – No fly zones – …
• Positioning via GSM Network
– Differential GSM positioning applications
• Traffic data cloud binding – Track UAS traffic
trough GSM Network • Redundancy through LOS
Data Link for C2 • Redundancy through
transponder for ADS-B
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EXPERIMENTAL DEMONSTRATION
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ITU Multirotor : Experimental Platform for Advanced Flight Controls and Autonomy Research
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Onboard Flight Management System
General Architecture of the FMS
Air‐to‐air data link (ADS‐B emulator for air‐to‐air)
Air‐to‐ground Data Link
Flight Control Computer
Flight Management Computer
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Experimental Implementation
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Autonomous Sense And Avoid
The Sense and Avoid module • independent safety assurance system from the ground for short
term collisions with – the aircraft in the surrounding traffic and – terrain objects
• Do not use intent sharing or time-consuming negotiation processes,
and immediately intervenes when the midterm separation assurance process fails.
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Collision Avoidance Approaches
• Nominal models – only consider current behavior and updates
advisory upon each information availability – do not require detailed performance models – i.e. TCAS
• Probabilistic models – robust due to accounting for likelihood of all
possible future trajectories – generate high rate deviation maneuvers – require detailed performance model for all type
of aircraft • Worst case models
– consider worst case maneuvers minimizing collision time, then maximize first potential collision times
– computationally complex, therefore generally utilize finite maneuver libraries
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Autonomous Sense And Avoid
The sense and avoid module of the UAV uses two types of information:
– Surrounding traffic information • obtained from ADS-B (Automatic
Dependent Surveillance-Broadcast ) transponders of surrounding aircraft.
– Terrain database
• spatial model of the earth objects in certain resolution.
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Autonomous Sense And Avoid
ADS-B hardware emulator • Xtend 900 MHz RF Module • Pre-programmed to work in broadcast
mode • Communicate with all transponders in the
field • Enables both ADS-B In and ADS-B Out
applications For simplification, the experimental ADS-B transponder always use a single mode.
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Autonomous Sense And Avoid
ADS-B emulator message structure • An simplified ADS-B data structure • Covers most essential flight information with 56 bytes data
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Collision Detected
These are sent to the RRT*
b. Collision is detected at time t. c. RRT* algorithm is initiated. a. Pre‐loaded tasks are in progress.
Cost Efficient Route
d. Cost efficient route is calculated by RRT* e. New route is transferred to UAV f. Task progress continues.
Search Area
New Route
Estimated Route Of Intruder
Conflict Detection and Resolution
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Real Time Video & Telemetry Window
Speed
Altitude
Vertical Speed
Artificial
Horizon
User Inputs
Home Position Mission
Control & Sense And
Avoid Window
Intruder Uncertanity
Balls
Altitude Window
Extra Information Window
Current Waypoint UA
V
Estimated Route Of Intruder
Vehicle Selector
Route
ID, Altitude Difference, Heading
Altitude[m] Of Mission Step
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Intruder
Experimental Demonstrations
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Predicted Intruder Path
Updated Reference Trajectory
Intruder Uncertainty
RRT* Search Graph
t=0 sec
Position Error
Potential Collision Point
Reference Trajectory
Target Waypoint
Altitude History Target Altitude
Current Altitude
t=20 sec t=60 sec
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Thank you.