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Multi-Robot Path Planning for Selective Coverage

In this paper we propose a reward-driven finitehorizon model akin to a Markov Decision Process to extract the maximum amount of valuable data in least amount of time. We present a path planning algorithm that generates off-line trajectories for multiple robots to cover a region of interest by visiting the hot-spots in increasing order of their significance. An underlying distribution is assumed to assist the algorithm with recognizing the hot-spots. The trajectories generated are both time and energy efficient. We validate our technique through several simulated experiments. Although this technique can be used in any environmental domain (Air, Water or Land), in this paper we demonstrate the success of our technique using a real robot in surveying coral reefs in the presence of real world conditions.