Effective Tracking of Unknown Clustered Targets Using A Distributed Team of Mobile Robots

Abstract Distributed multi-target tracking is a canonical task for multi-robot systems, encompassing applications from environmental monitoring to disaster response to surveillance. In many situations the unknown distribution of the targets in a search area is non-uniform, e.g., herds of animals moving together. This paper develops a novel distributed multi-robot multi-target tracking algorithm to effectively search for and track clustered targets. There are two key features. First, there are two parallel estimators, one to provide the best guess of the current states of targets and a second to provide a coarse, long-term distribution of clusters. Second, robots use the power diagram to divide the search space between agents in a way that effectively trades off between tracking detected targets within high density areas and searching for other potential targets. Extensive simulation experiments demonstrate the efficacy of the proposed method and show that it outperforms other approaches in tracking accuracy of clustered targets while maintain good performance for uniformly distributed targets.