Adversarial Modeling in the Robotic Coverage Problem

Abstract

The general problem of covering an area is a fundamental problem in robotics with applications in various domains. 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 a simplistic adversarial model, in which the threats are randomly scattered across the environment. In this paper, we allow the adversary to choose the locations of the threats in a way that maximizes the probability of stopping the robot performing the coverage. In other words, we discuss the problem of finding the best strategy to defend a given area from being covered by an agent, using k given guards. We show that although in general finding an optimal strategy for an adversary with zero knowledge is NP-Hard, for certain values of k an optimal strategy can be found in polynomial time, and for others we suggest heuristics that can significantly improve the random baseline strategy.