project #4: simulation and experimental testing of allocation of uavs
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
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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?
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Vehicle Routing Problems:Minimum Distance Route
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Minimum distance solution is useful for minimizing total mission time, fuel consumption, etc.
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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
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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
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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
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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
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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
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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.
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Important Features25
The Map XML Editing
Event EditorCreate
Scenario
Plan Request (CMASI) Validation
Run Scenario
Connect with Client
Record and Analyze data
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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
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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
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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
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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
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Simulation 1(a)
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Simulation Results
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Simulation 1(b)
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Simulation Results
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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%
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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%
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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
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Questions?35
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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
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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
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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
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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
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Command Value Conversion
AR Drone requires commands in text strings with values formatted as a 32-bit signed integer• Command string example
40
CMD = sprintf('AT*PCMD=%d,%d,%d,%d,%d,%d\r',i,1,0,1036831949,0,0);fprintf(ARc, CMD);
Ascent Rate
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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
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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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