Autonomous Maritime Vehicle Systems @ Virginia Tech
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Autonomous Maritime Vehicle Systems @ Virginia Tech. Wayne Neu, Craig Woolsey, Dan Stilwell, Chris Wyatt, Mike Roan Contact: Dan Stilwell [email protected] (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

Wayne Neu, Craig Woolsey,

Dan Stilwell, Chris Wyatt, Mike Roan

Contact: Dan Stilwell

[email protected]

(540) 231-3204


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  • 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)


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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


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HSAUV Launch

Neutrally ballasted vehicle at high speed


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Heavy Ballast, AUVFest 2007

Animation of data from AUVfest June 7, 2007

~675 ft. run at 10 Knots (40 sec)


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Active Roll Control

  • Two independent props provide thrust & roll control

  • Allows orientation control in hover


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475 AUV

  • Design goals

  • Rapid algorithm development

  • Low-cost (~$9K)

  • Orthodox hardware/software

  • Features

  • Acoustic comms and nav

  • Client/server software architecture

  • Removable mast


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475 AUV

Payloads

  • CTD/DO probe

  • Towed array (on-going)

  • Blueview FLS (on-going)

  • Magnetometer (on-going)


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Towed array

8 Piezoceramic Cylindrical

Broadband Hydrophones

2cm

All analog and digital electronics

Ethernet to AUV


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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



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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.


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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.


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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.


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Example Solutions/Applications

AUVFest 2007

Tracking (vector field)

Tracking (scalar field)


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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


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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





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Nonlinear Control design

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.


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Control of Slender, Agile AUVs design

Objective: “Large-envelope” AUV control

Approach: Potential energy shaping

Takegaki & Arimoto, 1981. Leonard, 1996.


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Some Results... design

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


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Step 1: The Steady Turn design

(A Regular Perturbation Problem)

0

0

0

1

²

²

=

=

:

Simulations use Slocum dynamic model given by Bhatta, 2006.


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Step 2: Optimal Motion Planning design

(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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