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Autonomous Maritime Vehicle Systems @ Virginia Tech

Autonomous Maritime Vehicle Systems @ Virginia Tech. Wayne Neu, Craig Woolsey, Dan Stilwell, Chris Wyatt, Mike Roan Contact: Dan Stilwell stilwell@vt.edu (540) 231-3204. Autonomous Vehicles High-Speed AUV 475 AUV VT ASV Fundamental Research Dynamics and control

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Autonomous Maritime Vehicle Systems @ Virginia Tech

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  1. Autonomous Maritime Vehicle Systems @ Virginia Tech Wayne Neu, Craig Woolsey, Dan Stilwell, Chris Wyatt, Mike Roan Contact: Dan Stilwell stilwell@vt.edu (540) 231-3204

  2. Autonomous Vehicles • High-Speed AUV • 475 AUV • VT ASV • Fundamental Research • Dynamics and control • Multi-vehicle cooperation • Data fusion • Stochastic mapping • Applied Research • Environmental adaptive sampling • Control design • Distributed navigation • Distributed signal processing • Contributors • Wayne Neu (AOE) • Craig Woolsey (AOE) • Dan Stilwell (ECE) • Chris Wyatt (ECE) • Mike Roan (ME)

  3. High-Speed AUV • Engineering highlights • No passive roll stability • requires active roll control • 50% heavier than displacement • sinks fast when not moving • Nose-down hover when not in flight • Virginia Tech activities • Propulsion • Hydrodynamics • Guidance/control • Electronics/software • Flight testing • Development Costs: $350K • Development time: 10 months

  4. HSAUV Launch Neutrally ballasted vehicle at high speed

  5. Heavy Ballast, AUVFest 2007 Animation of data from AUVfest June 7, 2007 ~675 ft. run at 10 Knots (40 sec)

  6. Active Roll Control • Two independent props provide thrust & roll control • Allows orientation control in hover

  7. 475 AUV • Design goals • Rapid algorithm development • Low-cost (~$9K) • Orthodox hardware/software • Features • Acoustic comms and nav • Client/server software architecture • Removable mast

  8. 475 AUV Payloads • CTD/DO probe • Towed array (on-going) • Blueview FLS (on-going) • Magnetometer (on-going)

  9. Towed array 8 Piezoceramic Cylindrical Broadband Hydrophones 2cm All analog and digital electronics Ethernet to AUV

  10. Adaptive Environmental Sampling • Adaptive transects • Create plume map, or boundary map, or track a boundary • Utilize a plume indicator function temperature plume indicator function Boundary track Temperature alone does not predict outflow Plume indicator function more clearly shows outflow

  11. Small plume localization/mapping

  12. AUV platoon Estimation (data fusion) Control (motion) Multi-Vehicle Coordination Key Theoretical Challenges Communication Closed-loop data fusion and control • Control and estimation are coupled • Unwanted coupling matters for fast and/or bandwidth-limited systems • Sparse and time-varying networks topologies • Low bandwidth (80 bits/sec!?) • Latencies Stilwell, D. J., Bollt, E. M., Roberson, D. G., 2006, "Sufficient Conditions for Fast Switching Synchronization in Time-Varying Network Topologies," SIAM J. Applied Dynamical Systems, vol. 6, no. 1, pp. 140-156. Porfiri, M. Stilwell, D. J., Bollt, E. M., Skufca, J. D. 2007, “Random Talk: Random Walk and Synchronizability in a Moving Neighborhood Network,” in Physica D, in press. Porfiri, M., Roberson, D. G., Stilwell, D. J., Tracking and Formation Control of Multiple Autonomous Agents: A Two-Level Consensus Approach, Automatica, in press.

  13. Sparse Stochastic Networks Expected value of network • Results • Notion of network time constant • Relationship between network time-constant and time-constant of underlying dynamics • Proximity graphs, controlled Markov chains Porfiri, M., Stilwell, D. J., "Consensus Seeking over Random Weighted Directed Graphs," in IEEE Transactions on Automatic Control, (in press) Porfiri, M., Stilwell, D. J., Bollt, E. M., “Synchronization in random weighted directed networks,” IEEE Transactions of Circuits and Systems – I (in press), and ACC 2007. Porfiri, M., Roberson, D. G., Stilwell, D. J., “Fast switching analysis of linear switched systems using exponential splitting,” SIAM Journal of Control and Optimization (in review) and ACC 2006.

  14. AUV platoon Estimation (data fusion) Control (motion) Sparse Stochastic Networks • Data fusion with observer structure • (e.g. Kalman filter) • Block-diagonalization for certain network topologies • Two-level consensus framework • Traditional data fusion • ?? (new effort) Closed-loop data fusion and control Porfiri, M., Roberson, D. G., Stilwell, D. J., 2006, "Environmental Tracking and Formation Control of a Platoon of Autonomous Vehicles Subject to Limited Communication," Proceedings of the IEEE Int'l. Conf. on Robotics and Automation, Orlando, FL. Roberson, D. G., Stilwell, D. J., "Decentralized Control and Estimation for a Platoon of Autonomous Vehicles with a Circulant Communication Network," Automatica, (in review) and ACC 2006.

  15. Example Solutions/Applications AUVFest 2007 Tracking (vector field) Tracking (scalar field)

  16. Autonomous Surface Vehicle • Capabilities • Long-endurance (4 days) • Robust • 250lb payload • Goal • Autonomous navigation/mapping in unstructured environments • Sensors/Electronics • Laptop(s) for control and image processing • Wifi (mesh network) • Gyro-stabilized pitch, roll, heading • Omni-directional camera (stereo on going) • WAAS-GPS • Water flow velocity (DVL) • Depth • CTD/DO

  17. Navigation/mapping in unstructured environments Feature detection, classification, localization stochastic map generation Path planning • Challenges • Mapping and path planning should be independent of sensor • Many false features in maritime environment • Current focus • Moving obstacle detection and tracking • Efficient distributed mapping and path planning for multiple vehicles

  18. Feature map generation

  19. Final feature map

  20. Numerical and experimental AUV modelling for control and design Field data Numerical models

  21. Nonlinear Control Of Advanced AUVs The Liberdade/XRay flying wing underwater glider.1(Solid model courtesy MPL/SIO and UW/APL.) • Energy-based nonlinear control of streamlined AUVs: • Exploit intrinsic agility of vectored thrust vehicles. • Enhance operability in dynamic, unstructured environments. • Optimal motion planning for underwater gliders: • Analytically characterize lateral-directional maneuvers. • Leverage results from nonholonomic robot control. 1G. D’Spain (MPL/SIO) & P. Brodsky (APL/UW) will speak about Liberdade/XRay development at 8:45 AM.

  22. Control of Slender, Agile AUVs Objective: “Large-envelope” AUV control Approach: Potential energy shaping Takegaki & Arimoto, 1981. Leonard, 1996.

  23. Some Results... Potential shaping yields almost global asymptotic stability.1 Animation generated using VRMLPlot (C. Sayers). Vehicle prototype by J. Graver. 1Woolsey, IEEE Conf. Decision & Control, Dec. 2006

  24. Step 1: The Steady Turn (A Regular Perturbation Problem) 0 0 0 1 ² ² = = : Simulations use Slocum dynamic model given by Bhatta, 2006.

  25. Step 2: Optimal Motion Planning (Dubins Car) • The minimum time path at constant speed and maximum L/D is the minimum potential energy path. • Rich, current literature on path planning for Dubins car1 • Point-to-point problems with specified final heading • Point-to-point problems without final heading • Multiple waypoint (travelling salesman) problems 1See, for example, Savla, Bullo, & Frazzoli, 2006; Ma & Castanon, 2006. Also see Sussmann & Tang, 1991; Boissonnat et al, 1992.

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