An information-driven framework for motion planning in robotic sensor networks: Complexity and experiments

A geometric optimization based approach to deploy a mobile sensor network for the purpose of detecting and capturing mobile targets in the plane is presented in [1]. The sensing-pursuit problem is motivated by the Marco polo game, in which the pursuer Marco must capture multiple mobile targets that are sensed intermittently, and with very limited information. In this paper we extend the results in [1] by providing (i) a complexity analysis of the proposed cell decomposition planning algorithm, and (ii) a testbed that allows to experimentally verify the applicability of the proposed pursuit methodology.