project #4: simulation and experimental testing of allocation of uavs tim arnett, aerospace...
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Project #4: Simulation and Experimental Testing of
Allocation of UAVs
Tim Arnett, Aerospace Engineering, Junior, University of Cincinnati
Devon Riddle, Aerospace Engineering, Junior University of Cincinnati
ASSISTED BY:
Chelsea Sabo, Graduate Research Assistant
Dr. Kelly Cohen, Faculty Mentor
Outline• Applications of UAVs• Challenges• Project Goals and Objectives• Vehicle Routing Problems• Experimental Testing
– Experimental Setup– Waypoint Navigation Algorithm
• AMASE– Why use AMASE?– Overview– Features
• Results & Analysis• Acknowledgements• Questions
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Why UAVs?3
• Missions that are “dull, dirty, and dangerous”• Cost and performance
– Do not need pilot life support systems– Removal of human survivability constraints
allows better performance
Applications of Surveillance Missions with UAVs
• Search and Rescue• Weather Observation• Forest Fire Monitoring
• Traffic Surveillance• Border Patrol• Military
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Challenges
• Obtaining software and equipment suitable for tests– Systems difficult to obtain and usually
expensive• Verifying solutions on proven systems
– Systems not always well-documented or fully supported
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Project Goals
• Learn to interface equipment for UAV controller development
• Compare two routing solutions for common performance metrics
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Objectives• Objective 1: Interface with cooperative control
development systems– Interface and run algorithms on AR Drones– Interface and run algorithms on AMASE
• Objective 2: Validate task allocation algorithm both in simulation and experimentally
• Objective 3: Test and compare cooperative control strategies for UAVs– Distance travelled– Delivery time for time critical targets
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Vehicle Routing Problems8
DepotTargets
Targets
Targets
• Multiple routing solutions exist depending on desired operational goals
• Which UAV services a target and in what order are the targets visited?
Vehicle Routing Problems:Minimum Distance Route
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Minimum distance solution is useful for minimizing total mission time, fuel consumption, etc.
Vehicle Routing Problems:Minimum Delivery Latency Route
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Often desirable to deliver data to a high-bandwidth connection or “depot”
For this case, the delivery time is often of interest due to missions being time critical
Test Cases• 3 different tests performed
– Differing difficulty and number of targets– Both Minimum Distance and Minimum Delivery
Latency solutions implemented for each test• Tests done both experimentally and in simulation
– Experiments done in IMAGE Lab with AR Drone UAVs– Simulations created in AMASE – an Air Force flight
simulation environment• Compared distance travelled and delivery time for
each test
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Experimental Setup12
AR Drones
IMAGE Lab
Experimental Setup
• AR Drone– Inexpensive, commercially available quadrotor– “Black box” with limited support– Can be controlled by a device using wireless
network adapter
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Experimental Setup14
AR Drones
OptiTrack Cameras
IMAGE Lab
Experimental Setup
• Optitrack System– Cameras provide real time position data– Data can be imported into MatLab
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Experimental Setup16
AR Drones
OptiTrack Cameras
Wireless Router
PC with MatLab and OptiTrack Tracking Tools
IMAGE Lab
Experimental Setup
• Software Interface– PC client with wireless capability, MatLab, and
camera software– Wireless router to connect to multiple drones
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Waypoint Algorithm
• Needed to dictate flight path of UAV• Control Methods
– Proportional-Derivative Control– Fuzzy Logic Control
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Control Diagram19
Waypoint Navigation Controller
• Proportional-Derivative controller– Used for Yaw Rate, Ascent Rate
• Provides good response and settling time• Simple implementation
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Waypoint Navigation Controller
• Fuzzy Logic Controller– Used for Pitch, Roll
• Does not require system model• Robust to stability issues
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AMASE
Automatic Test System Modeling and System Environment
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History of AMASE
• AFRL– Air Force Research Laboratory (Wright Patterson)
• Desktop simulation environment developed for UAV cooperative control studies
• Used to develop and optimize multiple- UAV engagement approaches
• Self contained simulation environment that accelerates iterative development/analysis
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Why AMASE?• Control algorithms can be assessed and compared
effectively • Free for University research• An environment that provides a formal simulation of
the algorithm as a precursor to large scale flight tests.• Proven as a legitimate way to set up realistic flight
simulations.• Provides good visual description of what’s happening
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Challenge: No technical support… Learned through trial and error.
Important Features25
The Map XML Editing
Event EditorCreate
Scenario
Plan Request (CMASI) Validation
Run Scenario
Connect with Client
Record and Analyze data
AMASE Set Up Tool: This is where all of the scenarios are created and the progress is saved.
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The Map Event Editor
Toolbar
Error Box
Simulation of test data on a world wide scale
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What runs the simulation
Characteristics of the aircraftThe Map
Aircraft
Path line
Experimental Results28
-0.5 0 0.5 10
0.5
1
1.5
2
1
2
3
4 56
7
8
1
-0.5 0 0.5 10
0.5
1
1.5
2
1
234
5
67
8
1
Minimum Delivery Latency Route Minimum Distance Route
Analysis29
𝐽𝑇=∑𝑖=1
𝑁
𝐷 𝑖 𝑅𝑡𝑜𝑡𝑎𝑙=√ (𝑥1−𝑥𝑑𝑒𝑝𝑜𝑡 )2+( 𝑦1− 𝑦 𝑑𝑒𝑝𝑜𝑡 )
2+∑𝑖=2
𝑁
√ (𝑥𝑖−𝑥 𝑖−1 )2+ (𝑦 𝑖− 𝑦 𝑖− 1)2
Total Time Cost Total Distance Travelled
Minimum Delivery Latency
Minimum Distance Difference
Total Time Cost
751.96 1134.92 -33.74%
Total Distance Travelled
25.11 18.04 -28.14%
D = Delivery Time
Simulation 1(a)
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Simulation Results
Simulation 1(b)
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Simulation Results
Analysis32
𝐽𝑇=∑𝑖=1
𝑁
𝐷 𝑖
Total Time Cost
D = Delivery Time
Minimum Delivery Latency
Minimum Distance Difference
Total Time Cost
31915 38807 -17.76%
Comparison33
Ideal Experimental AMASE Test 1 -21.89% -44.35% -21.45% Test 2 -18.65% -16.81% -11.98% Test 3 -24.90% -32.92% -11.50%
% Improvement of Total Time Cost for the Minimum Delivery Latency route compared to the Minimum Distance route
% Improvement of Total Distance Travelled for the Minimum Distance route compared to the Minimum Delivery Latency route
Ideal Experimental Test 1 -21.91% -8.88% Test 2 -28.75% -39.93% Test 3 -29.94% -32.55%
Acknowledgements34
• NSF Grant # DUE-0756921 for Type 1 Science, Technology, Engineering, and Mathematics Talent Expansion Program (STEP) Project
• Kelly Cohen, Ph.D, Faculty Mentor, University of Cincinnati, Cincinnati, OH• Chelsea Sabo, Ph.D, GRA, University of Cincinnati, Cincinnati, OH• Stephanie Lee, AFRL, Wright-Patterson Air Force Base, Dayton, OH• Manish Kumar, Ph.D, University of Toledo, Toledo, OH• Balaji Sharma, MS, University of Toledo, Toledo, OH• Ruoyu Tan, MS, University of Toledo, Toledo, OH
• Task Allocation Algorithm sourced from work done by Dr. Chelsea Sabo
Questions?35
Command Value Conversion
AR Drone requires commands in text strings with values formatted as a 32-bit signed integer• Command string example
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CMD = sprintf('AT*PCMD=%d,%d,%d,%d,%d,%d\r',i,1,0,1036831949,0,0);fprintf(ARc, CMD);
Sequence
Command Value Conversion
AR Drone requires commands in text strings with values formatted as a 32-bit signed integer• Command string example
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CMD = sprintf('AT*PCMD=%d,%d,%d,%d,%d,%d\r',i,1,0,1036831949,0,0);fprintf(ARc, CMD);
Flag
Command Value Conversion
AR Drone requires commands in text strings with values formatted as a 32-bit signed integer• Command string example
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CMD = sprintf('AT*PCMD=%d,%d,%d,%d,%d,%d\r',i,1,0,1036831949,0,0);fprintf(ARc, CMD);
Roll
Command Value Conversion
AR Drone requires commands in text strings with values formatted as a 32-bit signed integer• Command string example
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CMD = sprintf('AT*PCMD=%d,%d,%d,%d,%d,%d\r',i,1,0,1036831949,0,0);fprintf(ARc, CMD);
Pitch
• Value corresponds to a command value of 0.1• Values are a ratio to the full value allowable by the drone
Command Value Conversion
AR Drone requires commands in text strings with values formatted as a 32-bit signed integer• Command string example
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CMD = sprintf('AT*PCMD=%d,%d,%d,%d,%d,%d\r',i,1,0,1036831949,0,0);fprintf(ARc, CMD);
Ascent Rate
Command Value Conversion
AR Drone requires commands in text strings with values formatted as a 32-bit signed integer• Command string example
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CMD = sprintf('AT*PCMD=%d,%d,%d,%d,%d,%d\r',i,1,0,1036831949,0,0);fprintf(ARc, CMD);
Yaw Rate
The Event Editor
• AirVehicleConfiguration– Characteristics of the UAV – Given
• AirVehicleEntity– Characteristics of where the UAV starts in a
scenario and where it will go first• MissionCommand
– Tells the UAV where to go from homebase
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CMASI
• Common Mission Automation Services Interface– A system of interactive objects that pertain to
the command and control of a UAV system.– Where the MissionCommand is used. – Example of two scenarios to show why
CMASI is important.
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