1. Aeronautics & Astronautics Autonomous Flight Systems Laboratory All slides and material copyright of University of Washington Autonomous Flight Systems Laboratory

2. Aeronautics & Astronautics Autonomous Flight Systems Laboratory Research and Development at the Autonomous Flight Systems Laboratory University of Washington Seattle, WA Guggenheim 109, AERB 214 (206) 543-7748 http://www.aa.washington.edu/research/afsl

3. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington3 General Information Research Focus Multi-Vehicle Cooperative Control Flight Testing Cooperative Strategies for Teams of Autonomous Air & Surface Vehicles Probability Based Searching/Target Identification Coordinated Underwater Robotics Communications for Heterogeneous Cooperating Autonomous Vehicles To conduct research that advances guidance, navigation, and control technology relevant to Autonomous Vehicles. Mission Statement Dr. Rolf Rysdyk Dr. Juris Vagners Dr. Uy-Loi Ly Dr. Kristi Morgansen Dr. Anawat Pongpunwattana Christopher Lum Craig Husby John Osborne Richard Wise Elizabeth Bykoff People Ben Triplett Dan Klein Jim Colito

4. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington4 Hierarchy of Autonomy Path Planning Task Allocation Search Patterns Human Mission Command Strategic (low bandwidth) Tactical (medium bandwidth) State Stabilization Signal Tracking Inner Loop or “autopilot” Configuration changes Dynamics and Control (high bandwidth) Target Observation Path Following Communication & Cooperation Human Monitor Interaction

5. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington5 Topography of Autonomous Flight

6. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington6 Hardware-in-the-Loop Simulator Avionics Tray HiL Simulator

7. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington7 Hardware-in-the-Loop Simulator GroundstationAircraft

8. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington8 Distributed Real Time Simulator Five computers running REAL TIME simulation software. Used as a high fidelity testing environment to accurately simulate data transfer and communication aspects.

9. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington9 Infrastructure of Flight Tests In addition to simulation, direct access to actual hardware and systems. Partnered with the Insitu Group for ScanEagle UAVs, Northwind Marine for SeaFox Boats. Extensive test infrastructure in place by working with these local companies Includes sea launch & retrieval of UAVs

10. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington10 Aspects of Autonomy Base

11. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington11 Aspects of Autonomy Base STRATEGIC Team Assembly Task Assignment TACTICAL Pattern Hold DYNAMICS & CONTROL Auto Launch/Retrieval

12. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington12 Aspects of Autonomy Base Pattern hold/Team assembly

13. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington13 Aspects of Autonomy Base TransitionPattern hold/Team assembly

14. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington14 Aspects of Autonomy Base TransitionPattern hold/Team assembly STRATEGIC Path Planning Adaptive Task Assignment TACTICAL Obstacle/Threat Avoidance Path Following DYNAMICS & CONTROL State Stabilization

15. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington15 Aspects of Autonomy Base Transition Obstacle/Threat Avoidance Pattern hold/Team assembly

16. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington16 Aspects of Autonomy Base Transition Obstacle/Threat Avoidance Pattern hold/Team assembly STRATEGIC Dynamic Task Allocation Team-Based Cooperation Path Re- planning TACTICAL Obstacle Avoidance Engagement Maneuvers DYNAMICS & CONTROL State stabilization

17. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington17 Aspects of Autonomy Base Transition Obstacle/Threat Avoidance Pattern hold/Team assembly Coordination w/ surface vehicles

18. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington18 Aspects of Autonomy Base Transition Obstacle avoidance Coordination w/ surface vehicles Pattern hold/Team assembly STRATEGIC Provide improved target tasking and routing info to unmanned surface vehicles TACTICAL Orbit Coordination Communication Path Following DYNAMICS & CONTROL Signal Tracking

19. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington19 Aspects of Autonomy Base Transition Obstacle/Threat Avoidance Coordination w/ surface vehicles Pattern hold/Team assembly Searching/Target ID

20. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington20 Aspects of Autonomy Base Transition Obstacle avoidance Coordination w/ ground vehicles Pattern hold/Team assembly Searching/Target ID STRATEGIC Map-Based and Probabilistic Searches TACTICAL Path following DYNAMICS & CONTROL State stabilization

21. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington21 Aspects of Autonomy Base Transition Obstacle/Threat Avoidance Searching/Target ID Coordination w/ surface vehicles Pattern hold/Team assembly

22. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington22 Current Research Projects Real Time Strategic Mission Planning dynamic task and path planning for a team of autonomous vehicles to cooperatively execute a set of assigned tasks. Coordination of Heterogeneous Vehicles developing robust navigation and guidance algorithms to coordinate multiple vehicles to perform a cooperative task. Autonomous Search and Target Identification using total magnetic intensity measurements to search and identify magnetic anomalies in a predetermined area.

23. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington23 Real Time Strategic Mission Planning Base Transition Obstacle/Threat Avoidance Searching/Target ID Coordination w/ surface vehicles Pattern hold/Team assembly

24. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington24 System Overview Previously funded by DARPA & AFOSR

25. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington25 System Block Diagram Solving optimal control problems in real-time

26. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington26 Stochastic Problem Formulation Predicted probability of survival of each vehicle at time t q+1 Predicted probability that a task is not completed at time t q+1 Team utility function

27. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington27 Distributed Architecture for Coordination of Autonomous Vehicles Each vehicle plans its own path and makes task trading decisions to maximize the team utility function There is one active coordinator agent at a time efficiency failure detection local/global information exchanges Computational requirement for running coordinator agent is small compared to planning Coordinator role can be transferred to another vehicle via a voting procedure

28. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington28 Evolution-based Cooperative Planning System (ECoPS) Uses Evolutionary Computation- based techniques in the optimization of trading decision making and path planning Task planner uses price and shared information in addition to predicted states of the world for making trading decisions Task planner interacts with path planner and state predictor to simultaneously search feasible near-optimal task and path plans. We call this system the “Evolution- Based Collaborative Planning System” – ECoPS, combining market based techniques with evolutionary computation (EC).

29. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington29 Evolutionary Computation (EC) Motivated by evolution process found in nature Population-based stochastic optimization technique Metaphor Mapping

30. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington30 Features of Evolution-Based Computation Provides a feasible solution at any time Optimality is a bonus Dynamic replanning Non-linear performance function Collision avoidance Constraints on vehicle capabilities Handling loss of vehicles Operating in uncertain dynamic environments Timing constraints

31. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington31 Market-based Planning for Coordinating Team Tasks Task allocation problem: At trading round n At the end of the trading round: The goal of task trading: Each vehicle proposes which are approved by the auctioneer based on bid price. Distributed Task Planning Algorithm

32. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington32 Dynamic Path Planning Generate feasible paths and planned actions within a specified time limit (ΔTs ) while the vehicles are in motion. Highly dynamic environment requires a high bandwidth planning system (i.e. small ΔT s ). Formulate the problem as a Model-based Predictive Control (MPC) problem

33. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington33 EC-Based Path Planning Mutation Dynamic Planning Path Encoding

34. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington34 Collision Avoidance Model each site in the environment as a uncertainty circular area with radius Probability of intersection: use numerical approximation computationally easier than true solution : possible intersection region : probability density field function : position on the path C i : expected site location v : velocity of the vehicle

35. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington35 Collision Avoidance Example

36. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington36 Simulation Results Simulation on the Boeing Open Experimental Platform

37. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington37 Some Aspects of ECoPS Each vehicle computes its own trajectory and makes decision to trade its tasks with other vehicles. Vehicles may sacrifice themselves if that benefits the team. Each vehicle needs to have periodically updated locations of nearby vehicles only for collision avoidance. Each vehicle needs to know the information about the environment. The accuracy of the information affects the quality of its decision making. The rate of environment information updates should be selected based on how fast objects move in the environment. Assuming vehicles are equipped with on-board sensors, sharing sensed data improves the performance of the team.

38. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington38 Coordination of Heterogeneous Vehicles Base Transition Obstacle/Threat Avoidance Searching/Target ID Coordination w/ surface vehicles Pattern hold/Team assembly

39. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington39 Coordination and Communication with Autonomous Surface Vehicles At strategic level, UAVs can provide improved target tasking and routing information to surface vehicles Autonomous path planning for surface vehicles through non- structured environments enhanced by UAV information At tactical level, UAVs can track evasive targets and update world estimates Currently funded under WTC Phase I Fall/Winter ‘05

40. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington40 Goals and Advantages Goals Use multiple low-cost UAVs to cooperatively track targets Ability to mark targets, report to central database, report to deployed surface vehicles Improve quality and quantity of ISR data and battlefield awareness Advantages Tracking targets with tactical UAVs can require high operator workload Evasive targets could fool a single UAV

41. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington41 Simulation Visualization Autonomous Orbit Coordination for Multiple UAVs

42. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington42 Simulation Results Effects of Radius and Airspeed Manipulation

43. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington43 Simulation Results Effects of Radius and Airspeed Manipulation

44. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington44 Orbit Coordination Maintains relative phase angle between two UAVs in presence of disturbance Nonlinear issues dealing with asymmetry of varying orbits Joint effort between UW, Cornell, U of Calgary, and The Insitu Group Insitu SeaScan tracking moving target

45. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington45 Autonomous Search and Target Identification Base Transition Obstacle/Threat Avoidance Searching/Target ID Coordination w/ surface vehicles Pattern hold/Team assembly

46. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington46 Probabilistic Searching Evaluation of Autonomous Airborne Geomagnetic Surveying Utilize magnetometer to measure local magnetic anomalies for known signature Identify and classify anomalies Search for and track anomalies cooperatively Currently funded under WTC Phase II

47. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington47 General Architecture Obtaining local magnetic map Data from Fugro Airborne Surveys

48. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington48 General Architecture Groundstation Agent 1 Agent 2 Local Magnetic MapOccupancy Map

49. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington49 Occupancy-Based Map Search False Anomalies Target Agents

50. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington50 Occupancy-Based Map Search Score Cell Evaluate possible control population Execute control Basic Algorithm

51. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington51 Occupancy-Based Map Search Score Cell Evaluate possible control population Execute control Basic Algorithm

52. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington52 Occupancy-Based Map Search Score Cell Evaluate possible control population Execute control Basic Algorithm

53. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington53 Occupancy-Based Map Search Score Cell Evaluate possible control population Execute control Basic Algorithm

54. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington54 Occupancy-Based Map Search Score Cell Evaluate possible control population Execute control Basic Algorithm

55. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington55 Occupancy-Based Map Search Score Cell Evaluate possible control population Execute control Basic Algorithm

56. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington56 Occupancy-Based Map Search Score Cell Evaluate possible control population Execute control Basic Algorithm

57. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington57 Occupancy-Based Map Search Score Cell Evaluate possible control population Execute control Basic Algorithm

58. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington58 Occupancy-Based Map Search Score Cell Evaluate possible control population Execute control Basic Algorithm

59. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington59 Occupancy-Based Map Search Score Cell Evaluate possible control population Execute control Basic Algorithm

60. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington60 Occupancy-Based Map Search Score Cell Evaluate possible control population Execute control Basic Algorithm

61. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington61 Occupancy-Based Map Search Score Cell Evaluate possible control population Execute control Basic Algorithm

62. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington62 Anomaly Encounter Aeromagnetic Data from Fugro Airborne Corresponding Line Data Goal: Classify anomaly as target or false signature Anomaly How to score each cell?

63. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington63 Particle Filter How consistent is trace with trajectory over desired target? Classify using Particle Filter Nonparametric Bayes filter. Similar to Unscented Kalman or discrete Bayes filter. Which trajectory (if any) would produce trace?

64. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington64 Particle Filter Fox, D., Thrun, S., Burgard, W. 2005, “Probabilistic Robotics” Samplefrom for m=1:M end sampled from w/probability α Klein, D.J., Klink, J.O., 2005, “Mobile Robot Localization”

65. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington65 True Anomaly Encounter

66. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington66 Different Magnetic Signatures What about for false anomalies?

67. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington67 Confidence Comparison Actual Target Encounter False Encounter Features Use combination of particle filter and neural net to identify target and quantify confidence.

68. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington68 Contact Us Investigators Dr. Rolf Rysdykrysdyk@aa.washington.edu Dr. Uy-Loi Lyly@aa.washington.edu Dr. Juris Vagnersvagners@aa.washington.edu Dr. Kristi Morgansenmorgansen@aa.washington.edu Dr. Anawat Pongpunwattanaanawatp@u.washington.edu Autonomous Flight Systems Laboratory Guggenheim 109 (206) 543-7748 http://www.aa.washington.edu/research/afsl Nonlinear Dynamics and Control Laboratory AERB 120 (206) 685-1530 http://vger.aa.washington.edu