Collision Free Distributed Multi Target Tracking Using Teams of Mobile Robots with Localization Uncertainty
Accurately tracking dynamic targets relies on robots accounting for uncertainties in their own states to share information and maintain safety. The problem becomes even more challenging when there is an unknown and time varying number of targets in the environment. In this paper we address this problem by introducing four new distributed algorithms that allow large teams of robots to: i) run the prediction and ii) update steps of a distributed recursive Bayesian multi target tracker, iii) determine the set of local neighbors that must exchange data, and iv) exchange data in a consistent manner. All of these algorithms account for a bounded level of localization uncertainty in the robots by leveraging our recent introduction of the convex uncertainty Voronoi (CUV) diagram, which extends the traditional Voronoi diagram to account for localization uncertainty. The CUV diagram introduces a tessellation over the environment, which we use in this work both to distribute the multi target tracker and to make control decisions about where to search next. We examine the efficacy of our method via a series of simulations and compare them to our previous work which assumed perfect localization.
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)