This work discusses the problem of adversarial coverage, in which one or more robots are required to visit every point of a given area, which contains threats that might stop the robots. The objective of the robots is to cover the target area as quickly as possible, while maximizing the percentage of covered area before they are stopped. This problem has many real-world applications, from performing coverage missions in hazardous fields such as nuclear power plants, to surveillance of enemy forces in the battlefield and field demining. Previous studies of the problem dealt with single-robot coverage. Using a multi-robot team for the coverage has clear advantages in terms of both coverage time and robustness: even if one robot is totally damaged, others may take over its coverage subtask. Hence, in this paper we describe a multi-robot coverage algorithm for adversarial environments that tries to maximize the percentage of covered area before the team is stopped, while minimizing the coverage time. We analytically show that the algorithm is robust, in that as long as a single robot is able to move, the coverage will be completed. We also establish theoretical bounds on the minimum covered area guaranteed by the algorithm and on the coverage time. Lastly, we evaluate the effectiveness of the algorithm in an extensive set of environments and settings.
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