Application of DDDAS Principles to Command, Control and Mission Planning for UAV Swarms

Transcription

1 Application of DDDAS Principles to Command, Control and Mission Planning for UAV Swarms Greg Madey & R. Ryan McCune Department of Computer Science and Engineering University of Notre Dame DDDAS PI Meeting Dec 1-3, 2014 IBM T. J. Watson Research Center

2 Project History Goal: Apply DDDAS Concept to Command & Control of UAV Swarms Two year design, development, prototypes, demonstration, & evaluation Test bed Synthetic (simulated) UAV Swarms Demo with basic UAVs Parrot AR.DRONE 2.0 Proof-of-concept => DDDAS concept can be applied! Current: DURIP > UAV hardware with improved payload, communication, performance, open development features Continuation of development, demonstration, & evaluation with more capable hardware (CrazyFli, Hummingbird, Iris, Dr. Robot, DragonFly) Supplemental support from GAANN and NSF Applying new computational and networking paradigms, e.g., emergent computing, swarm intelligence, vertex-oriented computing 1

3 M. Brian Blake Software Service Discovery and Composition Greg Madey Agent-Based Simulation DDDAS Applications Chris Poellabauer Wireless Networks, Sensor Networks, Mobile Computing, Real-Time Systems David Wei CSE PhD Student R. Ryan McCune & Rachael Purta CSE PhD Students Mikolaj Dobski & Artur Jaworski CSE REU Students Alexander Madey High School Research Intern Hongsheng Lu CSE PhD Student Andrew Gnott Hardware Evaluation & Recommendations R. Ryan McCune GAANN Fellow Research Course Instructor Sean Gleason UAV assembly, configuration, flight Edwin Onattu UAV assembly, configuration, flight Travis Patterson UAV assembly, configuration, flight

4 Unmanned Aerial Vehicles (UAVs) Trends Numbers increasing Costs decreasing Small to full sized Capability increasing Swarms

5 Unmanned Aerial Vehicles (UAVs) Challenges Operator overload Training costs Flying the swarm Emergent behavior in swarm, how to control?

6 Operator Overload Unmanned Aerial Vehicles (UAVs) No on-board pilot Intelligence, Surveillance, and Reconnaissance (ISR) missions Becoming smaller and cheaper with increased capabilities How to efficiently operate many UAVs? 5

7 DDDAS Concept Simulations of the Swarms for Ground-based Operators Mission Planning Dynamic Mission Re-planning Command & Control Agent-based Simulations Dynamically Updated by UAV Sensors What-if Predictive Modeling to Support Re-planning and Command & Control (Fly the Swarm)

8 Sequence 1 Research Test-bed Virtual UAV Swarms Simulation-to-Simulation Modeling Investigate DDDAS Research Questions Proof-of-concept 2 DURIP Physical UAV Swarms Evaluate, Calibrate, Demonstrate and Validate

9 Ground Station Operator Team Mission Planning & Re-Planning Command & Control TestBed Virtual Swarms Virtual UAV Swarms Applica' on*of*dddas*principles*to*command,*control*and*mission*planning*for*uav*swarms Research*Test?Bed Ground Station Operator Team Mission Planning & Re-Planning Command & Control DURIP Physical Swarms Physical UAV Swarms

10 Results A DDDAS test-bed was developed utilizing web-services middleware to communicate between a real-world UAV swarms and agent-based simulations. Six Parrot AR.Drones 2.0 quadrocopters were demo ed as a real world UAV swarm communicating over the test-bed to a ground-based command & control application. G.R. Madey, M.B. Blake, C. Poellabauer, H. Lu, R.R. McCune, Y. Wei, Applying DDDAS Principles to Command, Control and Mission Planning for UAV Swarms, Procedia Computer Science, Volume 9,

11 Testbed System Console & UI Virtual UAV Swarm Console DDDAS Application System View

12 UAVs in Flight 2

13 Results Another investigation examined dynamic mission scheduling for swarms by incorporating DDDAS principles into a globallocal hybrid-planning scheme. A global agent utilized simulation to determine optimal task assignment, while UAVs locally determine the execution order for assigned tasks. Wei, Y., Madey, G.R, and Blake, M.B. Blake. "An Operation-time Simulation Framework for UAV Swarm Configuration and Mission Planning, ICCS 2013, Barcelona, Spain. June 5-7,

14 Mission Planning Example Mission T1 T2 T3 Swarm V2 V1 V4 V3

15 Two Missions, Multiple Tasks Each, Global Task Assignment, and Scheduling by UAVs Locally 5

16 Results Another investigation applied the DDDAS paradigm to two swarm command and control scenarios. In both scenarios, UAV swarms were augmented with mission-specific sensors, providing real-time measurements to a ground operator. Based on realtime measurements, the operator could adjust a single, global swarm parameter to achieve mission objectives. Madey, A.G., and Madey, G.R "Design and Evaluation of UAV Swarm Command and Control Strategies, ADS'13/SpringSim2013. SCS, San Diego, CA

17 DDDAS Inspired Control of UAV Swarm DDDAS Control/Sensor Data Local Swarm Behavior Two Types of UAVs Swarm Performance Improved with One Control Input the Cohere Value 7

18 Target UAV - Active Pursuer UAV - Inactive Pursuer UAV - Search 8

19 Results Another investigation quantified swarm performance with agent-based modeling. The modeling demonstrated the utility of an explanatory model in the DDDAS framework, and provided an interesting juxtaposition of a bottom-up model analyzed by a topdown clustering algorithm, where both calculated results based on the distance of agent neighbors. Example: UAV Swarm for disaster management McCune, R. R. and G. R. Madey. Decentralized K-Means Clustering with MANET Swarms, ADS'14/SpringSim 2014, SCS, Tampa, FL. April 13-16, 2014 McCune, R. Ryan, and Greg R. Madey. "Control of Artificial Swarms with DDDAS." Procedia Computer Science 29 (2014):

20 Swarm Clustering Tanker Moves 10

21 Simulation Snap-Shots Swarm clustering simulation Voronoi diagram overlay Tankers as seed points UAV Swarm for disaster support 11

22 More Results in Publications McCune, R. R. and G. R. Madey. Decentralized K-Means Clustering with MANET Swarms, ADS'14/SpringSim 2014, SCS, Tampa, FL. April 13-16, Madey, A.G., and Madey, G.R "Design and Evaluation of UAV Swarm Command and Control Strategies, ADS'13/SpringSim2013. SCS, San Diego, CA McCune, R., Y. Wei, R. Purta, A. Madey, M. B. Blake, and G. Madey, Investigations of DDAS for Command and Control of UAV Swarms with Agent-Based Modeling. WSC Washington D.C. December 8-11, McCune, R. R., and G. Madey. "Swarm Control of UAVs for Cooperative Hunting with DDDAS, ICCS 2013, Barcelona, Spain. June 5-7, McCune, R. R., and G. R. Madey. "Agent-Based Simulation of Cooperative Hunting with UAVs. ADS'13/SpringSim2013, SCS, San Diego, CA Purta, R., M. Dobski, A. Jaworski, and G. Madey. "A Testbed for Investigating the UAV Swarm Command and Control Problem Using DDDAS, ICCS 2013, Spain. June 5-7, Purta, R., Saurabh N., and G. Madey. "Multi-hop Communications in a Swarm of UAVs. ADS'13/SpringSim 2013, SCS, San Diego, CA Wei, Y., Madey, G.R, and Blake, M.B. Blake. "An Operation-time Simulation Framework for UAV Swarm Configuration and Mission Planning, ICCS 2013, Barcelona, Spain. June 5-7, Wei, Y., G. Madey, and M. B. Blake. "Agent-based Simulation for UAV Swarm Mission Planning and Execution. ADS 13/SpringSim 2013, SCS, Y. Wei and M.B. Blake, An Agent based Services Framework with Adaptive Monitoring in Cloud Environments, WETICE 2012, IEEE Press, Toulousse, France, June Best Student Paper Award. Wei, Y., and M. B. Blake. "Adaptive Web Services Monitoring in Cloud Environments." International Journal on Web Portals (2013). G.R. Madey, M.B. Blake, C. Poellabauer, H. Lu, R.R. McCune, Y. Wei, Applying DDDAS Principles to Command, Control and Mission Planning for UAV Swarms, Procedia Computer Science, Volume 9, 2012 McCune, R. Ryan, and Greg R. Madey. "Control of Artificial Swarms with DDDAS." Procedia Computer Science 29 (2014): Madey, A., Unmanned Aerial Vehicle Swarms: The Design and Evaluation of Command and Control Strategies using Agent-based Modeling, International Journal of Agent Technologies and Systems (IJATS), Vol. 5, Issue 3, 2013, P. Mitra and C. Poellabauer, Opportunistic Routing in Mobile Ad-Hoc Networks, Routing in Opportunistic Networks, Isaac Woungang Ed., Springer, McCune, R. R., A. Madey and G. R. Madey. UAV Swarm Command and Control With Agent-Based Modeling, Book Chapter (under review) 12

23 UAVs (DURIP) Parrot AR.Drone 2.0 CrazyFlie Hummingbird Iris Dr. Robot Dragonfly

24 UAV Research: FAA Challenges FAA Campus in flight path for SBN airport Purchasing Office Office of Risk Management Campus Security University Legal OK if indoors!

25 Indoors Facilities 21,000 square feet 614 feet by 210 feet = 128,940 square feet

26 Movie Removed to Reduce File Size can be viewed at:

27 Acknowledgements This research was supported in part under grants from the Air Force Office of Scientific Research: AFOSR FA (testbed, virtual SWARMS of UAVs) AFOSR FA (DURIP physical SWARM of UAVs) The National Science Foundation Award No REU Site Award No REU Supplement GAANN Fellowship (Graduate Assistance in Areas of National Need) from the Department of Education Center for Research Computing, University of Notre Dame Department of Computer Science & Engineering, Notre Dame 17

28 Vertex-Oriented Computing Application to DDDAS

29 Swarm Application Architecture 19

30 Large-Scale Graph Processing Graphs capture relationships Networks of nodes and edges Graph analytics are valuable Node or graph quantities Big Data driving volume Millions of nodes, billions of edges Can t centrally process Random access impractical

31 Vertex-Oriented Frameworks Distributed graph processing framework Abstract away details like MPI User-defined vertex program Iterative execution Vertices communicate through messages Equivalent result to sharedmemory algorithms Machine

32 Min Value STEP 0 Active Halt

33 DDDAS and Vertex Computing DDDAS principles for bottom-up approach Lots of Applications Power grids Decentralized control algorithms Disturbance monitoring Framework properties Synchronicity Streaming partitioning

34 THANK YOU! Questions? Project URL: 24