AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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Towards Efficient Robot Adversarial Coverage
Roi Yehoshua, Noa Agmon, Gal A. Kaminka

Last modified: 2013-06-29

Abstract


This paper discusses the problem of generating efficient coverage paths for a mobile robot in an adversarial environment, where threats exist that might stop the robot. First, we formally define the problem of adversarial coverage, and present optimization criteria used for evaluation of coverage algorithms in adversarial environments. We then present a coverage area planning algorithm based on a map of the probable threats. The algorithm tries to minimize the total risk involved in covering the target area while taking into account coverage time constrains. The algorithm is based on incrementally extending the coverage path to the nearest safe cells while allowing the robot to repeat its steps. By allowing the robot to visit each cell in the target area more than once, the accumulated risk can be reduced at the expense of extending the coverage time. We show the effectiveness of this algorithm in extensive experiments.

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