A geometric optimization approach to tracking maneuvering targets using a heterogeneous mobile sensor network

A methodology is developed to deploy a mobile sensor network for the purpose of detecting and capturing mobile targets in the plane. The mobile sensor network consists of a set of heterogeneous robotic sensors modeled as hybrid systems with individual processing capabilities. The targets are modeled by a Markov motion process that is commonly used in target tracking applications. Since the sensors are installed on mobile robots and have limited range, the geometry of their platforms and fields-of-view play a critical role in motion planning and obstacle avoidance. The methodology presented in this paper uses line transversals and cell decomposition in order to compute sensing and pursuit strategies that maximize the probability of detection, while minimizing energy consumption. The approach is demonstrated through progressive simulation scenarios involving multiple sensors installed on UGVs and UAVs, that are characterized by different sensing and motion capabilities, but are deployed to cooperatively detect, track, and pursue the same set of maneuvering targets.