UNMANNED SYSTEMS RESEARCH Aeronautics Astronautics University of

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 Mehran Mesbahi Associate Professor http: //dssl. aa. washington. edu/ Kristi A. Morgansen Associate Professor http: //www. aa. washington. edu/research/ndcl Juris Vagners Professor Emeritus http: //www. aa. washington. edu/research/afsl 3

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

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

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 NSF CAREER UW RRF NSF BE (with J. Parrish and D. Grunbaum, UW) • Small size • Coriolis effects • Unmodeled or approximated fluid dynamics elements • Communication and sensing limitations 9

UW Fin-Actuated UUV - Control • Results extendable to many fluid-body models • Rigorous mathematics with simplementation • Experimental stabilization robust ºIncorporate vortex dynamics and unsteady effects into model ºOptimal motion generation ºExtension to flexible actuators 10

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

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 NSF CAREER AFOSR (with Prof. Tara Javidi, UCSD) AFOSR (with The Insitu Group, Inc. ) Challenges • Managing time delays in local control • Allocation of resources • Construction of stabilizing controllers • Modeling for both communication and control 12

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

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)) 14

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 Identification and Influence in Networks Adaptable swarms Network identification Autonomous networks with foreign inputs 15

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

Distributed Space Systems Lab http: //dssl. aa. washington. edu Mehran Mesbahi Planar Collective UAV Coordination Decentralized UAV De-confliction 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 17

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

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 Washington Technology Center Washington Space Grant Consortium Air Force Office of Scientific Research Boeing/Insitu Northwind Marine • Heterogeneous team with different capabilities and constraints. • Environment may be complex and/or dynamic. • Algorithm scalability and inter-vehicle communication.

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

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 21

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 22

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 DARPA AFOSR Northwind Marine Wash. Technology Center • Heterogeneous agents means varying capabilities and constraints. • Actions which benefit individual agents may not benefit team. • Environmental constraints. 23

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

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

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 26

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

THANK YOU! QUESTIONS?