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

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

Controls & Systems Faculty Research Labs 3 Kristi A. Morgansen Associate Professor Mehran Mesbahi Associate Professor Juris Vagners Professor Emeritus

WIND TUNNEL TESTING, UWAL Aerosonde, the first UAV across the Atlantic

The launch: St John’s, Newfoundland

North Atlantic Crossing: The route and weather

LAIMA in the Museum of Flight

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

9 Modeling and control of fin-actuated underwater vehicles Tail locomotion and pectoral fin maneuverability NSF CAREER UW RRF NSF BE (with J. Parrish and D. Grunbaum, UW) 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

10 UW Fin-Actuated UUV - Control Results extendable to many fluid-body models Rigorous mathematics with simple implementation Experimental stabilization robust º Incorporate vortex dynamics and unsteady effects into model º Optimal motion generation º 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 NSF CAREER AFOSR (with Prof. Tara Javidi, UCSD) AFOSR (with The Insitu Group, Inc.) 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

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 NSF BE (with J. Parrish and D. Grunbaum, UW) Murdock Trust Goals Challenges Tracking of objects Data fusion Model representation

14 Cognitive Dynamics for Human-in-the-Loop Challenges Model representation Heterogeneity Information flow Levels of autonomy Goals Coordinated control for heterogeneous multivehicle system with human interaction Cognitive models and social psychology Dynamics and control 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 Coordination over randomly evolved networks Decentralized computation and estimation Autonomous networks with foreign inputs Informed design for controllability and security of networks Distributed Space Systems Lab Mehran Mesbahi Adaptable swarms Network identification

16 Spacecraft Formation Flying Distributed Space Systems Lab 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 Mehran Mesbahi Planar Collective UAV Coordination UAV path planning & Collision Avoidance Limited communication Can perform under turn-rate constraints and limit sensing capability Can guarantee collision free and reach destination Formation flying Leader-Followers on Unicycle model UAV Using navigation function

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

Coordinated Searching Using Autonomous Agents Washington Technology Center Washington Space Grant Consortium Air Force Office of Scientific Research Boeing/Insitu Northwind Marine 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.

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

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

22 Human-in-the-Loop Control Architectures Goals Develop a system for rapid verification and validation of strategic, autonomous algorithms. Investigate interactions between human and automated algorithms. Challenges 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. Heterogeneous agents means varying capabilities and constraints. Actions which benefit individual agents may not benefit team. Environmental constraints. Challenges DARPA AFOSR Northwind Marine Wash. Technology Center

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 Evolution-Based Cooperative Planning Systems (ECoPS) Elliot Bay mission Agents adapt plan to accommodate changing environment

Risk Assessment Tool for UAS Operations 26 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 “Acceptable system safety studies must include a hazard analysis, risk assessment, and other appropriate documentation,” -FAA

27 The next demonstration

THANK YOU! QUESTIONS?