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 2

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 4

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 5

Project Goals Learn to interface equipment for UAV controller development Compare two routing solutions for common performance metrics 6

Objectives Objective 1:Objective 1: Interface with cooperative control development systems –Interface and run algorithms on AR Drones –Interface and run algorithms on AMASE Objective 2:Objective 2: Validate task allocation algorithm both in simulation and experimentally Objective 3:Objective 3: Test and compare cooperative control strategies for UAVs –Distance travelled –Delivery time for time critical targets 7

Vehicle Routing Problems 8 Depot 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 9 Minimum distance solution is useful for minimizing total mission time, fuel consumption, etc.

Vehicle Routing Problems: Minimum Delivery Latency Route 10 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 11

Experimental Setup 12 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 13

Experimental Setup 14 AR Drones OptiTrack Cameras IMAGE Lab

Experimental Setup Optitrack System –Cameras provide real time position data –Data can be imported into MatLab 15

Experimental Setup 16 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 17

Waypoint Algorithm Needed to dictate flight path of UAV Control Methods –Proportional-Derivative Control –Fuzzy Logic Control 18

Control Diagram 19

Waypoint Navigation Controller Proportional-Derivative controller –Used for Yaw Rate, Ascent Rate Provides good response and settling time Simple implementation 20

Waypoint Navigation Controller Fuzzy Logic Controller –Used for Pitch, Roll Does not require system model Robust to stability issues 21

AMASE Automatic Test System Modeling and System Environment 22

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 23

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 24 Challenge: No technical support… Learned through trial and error.

Important Features 25 The MapXML EditingEvent Editor Create 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. 26 The Map Event Editor Toolbar Error Box

Simulation of test data on a world wide scale 27 What runs the simulation Characteristics of the aircraft The Map Aircraft Path line

Experimental Results 28 Minimum Delivery Latency Route Minimum Distance Route

Analysis 29 Total Time CostTotal Distance Travelled D = Delivery Time

Simulation 1(a) 30 Simulation Results

Simulation 1(b) 31 Simulation Results

Analysis 32 Total Time Cost D = Delivery Time

Comparison 33 % 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

Acknowledgements 34 NSF Grant # DUE 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 36 CMD = sprintf('AT*PCMD=%d,%d,%d,%d,%d,%d\r',i,1,0, ,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 37 CMD = sprintf('AT*PCMD=%d,%d,%d,%d,%d,%d\r',i,1,0, ,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 38 CMD = sprintf('AT*PCMD=%d,%d,%d,%d,%d,%d\r',i,1,0, ,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 39 CMD = sprintf('AT*PCMD=%d,%d,%d,%d,%d,%d\r',i,1,0, ,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 40 CMD = sprintf('AT*PCMD=%d,%d,%d,%d,%d,%d\r',i,1,0, ,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 41 CMD = sprintf('AT*PCMD=%d,%d,%d,%d,%d,%d\r',i,1,0, ,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 42

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. 43