UNMANNED SYSTEMS RESEARCH Aeronautics & Astronautics University of Washington

1. UNMANNED SYSTEMS RESEARCH Aeronautics & AstronauticsUniversity of Washington Dr. Juris Vagners Professor Emeritus February 26, 2010 AUVSI Cascade Chapter Meeting Seattle, Washington

2. PRESENTATION OUTLINE • Faculty Research Labs • A brief history • Faculty laboratory activity summaries and selected research projects

3. Controls & Systems Faculty Research Labs Mehran MesbahiAssociate Professorhttp://dssl.aa.washington.edu/ Kristi A. MorgansenAssociate Professorhttp://www.aa.washington.edu/research/ndcl Juris VagnersProfessor Emeritus http://www.aa.washington.edu/research/afsl

4. WIND TUNNEL TESTING, UWALAerosonde, the first UAV across the Atlantic

5. The launch: St John’s, Newfoundland

6. North Atlantic Crossing: The route and weather

7. LAIMA in the Museum of Flight

8. Nonlinear Dynamics and Control Lab http://vger.aa.washington.edu Kristi A. Morgansen Modeling Estimation Control Heterogeneous coordinated control with limited communication Modeling and control of shape-actuated immersed mechanical systems Coordinated control with communication for UUVs Bioinspired system modeling for coordinated control Cognitive dynamics models for human-in-the-loop systems Integrated communication and control

9. Modeling and control of fin-actuated underwater vehicles Tail locomotion and pectoral fin maneuverability Goals • Agile maneuverability • Analytical control theoretic models of immersed shape-actuated devices • Underwater localization • Nonlinear control • Coordinated control Challenges • Small size • Coriolis effects • Unmodeled or approximated fluid dynamics elements • Communication and sensing limitations NSF CAREER UW RRF NSF BE (with J. Parrish and D. Grunbaum, UW)

10. UW Fin-Actuated UUV - Control • Results extendable to many fluid-body models • Rigorous mathematics with simple implementation • Experimental stabilization robust • Incorporate vortex dynamics and unsteady effects into model • Optimal motion generation • Extension to flexible actuators

11. Coordinated Control with Limited Communication Goals • Control in the presence of communication and sensing constraints • Control over networks • Deconfliction • Schooling/swarming group behavior Challenges • Managing time delays in local control • Definition of attention • Allocation of resources • Construction of stabilizing controllers • Modeling NSF CAREER AFOSR (with Prof. Tara Javidi, UCSD) AFOSR (with The Insitu Group, Inc.) The Boeing Company

12. Hierarchical Integrated Communication and Control Goals • Coordinated tracking of objects or boundaries • Non-separated design of communication and control algorithms • Data quantization • Cooperative task management • Control over networks Challenges • Managing time delays in local control • Allocation of resources • Construction of stabilizing controllers • Modeling for both communication and control NSF CAREER AFOSR (with Prof. Tara Javidi, UCSD) AFOSR (with The Insitu Group, Inc.)

13. Bioinspired Coordinated Control Models of social aggregations Effects of heterogeneity (levels of hunger, familiarity) Relation to engineered systems Application to fishery management, population modeling Goals Challenges • Tracking of objects • Data fusion • Model representation NSF BE (with J. Parrish and D. Grunbaum, UW) Murdock Trust

14. Cognitive Dynamics for Human-in-the-Loop Goals • Coordinated control for heterogeneous multivehicle system with human interaction • Cognitive models and social psychology • Dynamics and control Challenges • Model representation • Heterogeneity • Information flow • Levels of autonomy AFOSR MURI (with J. Baillieul (BU), F. Bullo (UCSB), D. Castanon (BU), J. Cohen (Princeton), P. Holmes (Princeton), N. Leonard (Princeton), D. Prentice (Prentice), J. Vagners (UW))

15. Identification and Influence in Networks Distributed Space Systems Lab http://dssl.aa.washington.edu Mehran Mesbahi Informed design for controllability and security of networks Coordination over randomly evolved networks Decentralized computation and estimation Adaptable swarms Network identification Autonomous networks with foreign inputs

16. Spacecraft Formation Flying Distributed Space Systems Lab http://dssl.aa.washington.edu Mehran Mesbahi Spacecraft Attitude Control Formation Initialization of Microsatellites Space Interferometry Mission Reorientation in multiple attitude constraints

17. Decentralized UAV De-confliction Distributed Space Systems Lab http://dssl.aa.washington.edu Mehran Mesbahi Planar Collective UAV Coordination UAV path planning & Collision Avoidance Formation flying Can guarantee collision free and reach destination Can perform under turn-rate constraints and limit sensing capability Limited communication Leader-Followers on Unicycle model UAV Using navigation function

18. Autonomous Flight Systems Laboratory http://www.aa.washington.edu/research/afsl Juris Vagners To conduct research that advances technologies relevant to unmanned systems. General USV Work Dynamic Mission Management Human in the Loop Architectures General UAV GN&C Work Path Planning and Collision Avoidance

19. Coordinated Searching Using Autonomous Agents Goals • Increase autonomy of group of agents involved in a search mission. • Guarantee detection of target in search domain. • Develop control laws so agents act in coordinated fashion. Challenges • Heterogeneous team with different capabilities and constraints. • Environment may be complex and/or dynamic. • Algorithm scalability and inter-vehicle communication. Washington Technology Center Washington Space Grant Consortium Air Force Office of Scientific Research Boeing/Insitu Northwind Marine

20. Coordinated Searching Using Autonomous Agents • Target locations probabilistically modeled using occupancy based maps. • Search strategy based on non-linear optimization and Voronoi partitioning. Environment Single agent patrolling a New York harbor Occupancy based map

21. Coordinated Searching Using Autonomous Agents • Validate algorithms in simulation, in Boeing Vehicle Swarm Technology (VSTL) lab, and in flight test. Flight test using quadrotor UAVs in Boeing VSTL Flight test in single engine aircraft over Puget Sound

22. Human-in-the-Loop Control Architectures Goals Challenges • Develop a system for rapid verification and validation of strategic, autonomous algorithms. • Investigate interactions between human and automated algorithms. • Logistics and high overhead for simple tests. • Rules and regulations. • Non-deterministic human behavior. Washington Technology Center AFOSR

23. Dynamic Mission Management and Path Planning Goals • Perform dynamic task assignment for large number of autonomous agents. • Provide feasible paths which allow agents to accomplish tasks. • Replan according to rapidly changing environment and/or conditions. Challenges • Heterogeneous agents means varying capabilities and constraints. • Actions which benefit individual agents may not benefit team. • Environmental constraints. DARPA AFOSR Northwind Marine Wash. Technology Center

24. Dynamic Mission Management and Path Planning • Distributed control of multiple, heterogeneous vehicles • Provides a solution at any time, based on evolutionary computation techniques • Continuous task/path replanning based on market strategies • Operates in uncertain dynamic environments (weather, pop-ups, damage, new objectives) • Complex performance trade-offs • Collision avoidance • Vehicle capabilities can be explicit • Handles loss of vehicles • Timing constraints can be explicit • Seamless integration of operator inputs

25. Dynamic Mission Management and Path Planning Elliot Bay mission Agents adapt plan to accommodate changing environment Evolution-Based Cooperative Planning Systems (ECoPS)

26. Risk Assessment Tool for UAS Operations “Acceptable system safety studies must include a hazard analysis, risk assessment, and other appropriate documentation,” -FAA Goals • User-friendly tool for modeling the risk of UAS team operations • Direct users where to find needed info • Wed-based & downloadable versions • Promote risk-based approach to UAS regulation & policy Challenges • Wide variety of UAS operations • Diverse areas overflown (disparate population profiles) • Accurately model air traffic  create tool to predict traffic in specific area • Limited data for validation

27. The next demonstration http://www.aerovelco.com/

28. THANK YOU! QUESTIONS?