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Proceedings Paper

A biologically inspired approach to modeling unmanned vehicle teams
Author(s): Roger S. Cortesi; Kevin S. Galloway; Eric W. Justh
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Paper Abstract

Cooperative motion control of teams of agile unmanned vehicles presents modeling challenges at several levels. The "microscopic equations" describing individual vehicle dynamics and their interaction with the environment may be known fairly precisely, but are generally too complicated to yield qualitative insights at the level of multi-vehicle trajectory coordination. Interacting particle models are suitable for coordinating trajectories, but require care to ensure that individual vehicles are not driven in a "costly" manner. From the point of view of the cooperative motion controller, the individual vehicle autopilots serve to "shape" the microscopic equations, and we have been exploring the interplay between autopilots and cooperative motion controllers using a multivehicle hardware-in-the-loop simulator. Specifically, we seek refinements to interacting particle models in order to better describe observed behavior, without sacrificing qualitative understanding. A recent analogous example from biology involves introducing a fixed delay into a curvature-control-based feedback law for prey capture by an echolocating bat. This delay captures both neural processing time and the flight-dynamic response of the bat as it uses sensor-driven feedback. We propose a comparable approach for unmanned vehicle modeling; however, in contrast to the bat, with unmanned vehicles we have an additional freedom to modify the autopilot. Simulation results demonstrate the effectiveness of this biologically guided modeling approach.

Paper Details

Date Published: 1 May 2008
PDF: 12 pages
Proc. SPIE 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II, 696405 (1 May 2008); doi: 10.1117/12.778373
Show Author Affiliations
Roger S. Cortesi, Naval Research Lab. (United States)
Kevin S. Galloway, Naval Research Lab. (United States)
Eric W. Justh, Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 6964:
Evolutionary and Bio-Inspired Computation: Theory and Applications II
Misty Blowers; Alex F. Sisti, Editor(s)

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