Online robotic adversarial coverage

In the robotic coverage problem, a robot is required to visit every point of a given area using the shortest possible path. In a recently introduced version of the problem, adversarial coverage, the covering robot operates in an environment that contains threats that might stop it. Previous studies of this problem dealt with finding optimal strategies for the coverage, that minimize both the coverage time and the probability that the robot will be stopped before completing the coverage. However, these studies assumed that a map of the environment, which includes the specific locations of the threats, is given to the robot in advance. In this paper, we deal with the online version of the problem, in which the covering robot has no a-priori knowledge of the environment, and thus has to use real-time sensor measurements in order to detect the threats. We employ a frontier-based coverage strategy that determines the best frontier to be visited by taking into account both the cost of moving to the frontier and the safety of the region that is reachable from it. We also examine the effect of the robot´s sensing capabilities on the expected coverage percentage. Finally, we compare the performance of the online algorithm to its offline counterparts under various environmental conditions.