Frontier-Based RTDP: A New Approach to Solving the Robotic Adversarial Coverage Problem

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Area coverage is an important problem in robotics, where one or more robots are required to visit all points in a given area. In this paper we consider a recently introduced version of the problem, adversarial coverage, in which the covering robot operates in an environment that contains threats that might stop it. The objective is to cover the target area as quickly as possible, while minimizing the probability that the robot will be stopped before completing the coverage. We first model this problem as a Markov Decision Process (MDP), and show that finding an optimal policy of the MDP also provides an optimal solution to this problem. Since the state space of the MDP is exponential in the size of the target area's map, we use real-time dynamic programming (RTDP), a well-known heuristic search algorithm for solving MDPs with large state spaces. Although RTDP achieves faster convergence than value iteration on this problem, practically it cannot handle maps with sizes larger than 7x7. Hence, we introduce the use of frontiers, states that separate the covered regions in the search space from those uncovered, into RTDP. Frontier-Based RTDP (FBRTDP) converges orders of magnitude faster than RTDP, and obtains significant improvement over the state-of-the-art solution for the adversarial coverage problem.