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 Real Time Strategic Mission Planning Base Transition Obstacle/Threat Avoidance Searching/Target ID Coordination w/ surface vehicles Pattern hold/Team assembly

4. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington4 System Overview Previously funded by DARPA & AFOSR

5. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington5 System Block Diagram Solving optimal control problems in real-time

6. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington6 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

7. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington7 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

8. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington8 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).

9. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington9 Evolutionary Computation (EC) Motivated by evolution process found in nature Population-based stochastic optimization technique Metaphor Mapping

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

11. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington11 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

12. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington12 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

13. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington13 EC-Based Path Planning Mutation Dynamic Planning Path Encoding

14. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington14 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

15. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington15 Collision Avoidance Example

16. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington16 Simulation Results Simulation on the Boeing Open Experimental Platform

17. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington17 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.

18. Aeronautics & Astronautics Autonomous Flight Systems Laboratory University of Washington18 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