A Sampling-Based Motion Planning Approach to Maintain Visibility of Unpredictable Targets

Authors:
Rafael Murrieta-Cid;Benjamín Tovar;Seth Hutchinson
Affiliations:
Mechatronics Research Center, EGIC, Tec de Monterrey, Edo de México, México;Department of Computer Science, Siebel Center, University of Illinois, Urbana, USA 61801;Department of Electrical and Computer Engineering, The Beckman Institute, University of Illinois, Urbana, USA 61801
Venue:
Autonomous Robots
Year:
2005

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Abstract

This paper deals with the surveillance problem of computing the motions of one or more robot observers in order to maintain visibility of one or several moving targets. The targets are assumed to move unpredictably, and the distribution of obstacles in the workspace is assumed to be known in advance. Our algorithm computes a motion strategy by maximizing the shortest distance to escape--the shortest distance the target must move to escape an observer's visibility region. Since this optimization problem is intractable, we use randomized methods to generate candidate surveillance paths for the observers. We have implemented our algorithms, and we provide experimental results using real mobile robots for the single target case, and simulation results for the case of two targets-two observers.