Asymptotically Optimal Planning for Non-myopic Multi-Robot Information Gathering
A recent paper by members of the DCIST alliance develops a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active Simultaneous Localization and Mapping (SLAM), surveillance, environmental monitoring and others. The goal is to compute control policies for mobile robot sensors which minimize the accumulated uncertainty of a dynamic hidden state over an a priori unknown horizon. To design optimal sensor policies, we propose a novel nonmyopic sampling-based approach that simultaneously explores both the robot motion space and the information space reachable by the sensors. We show that the proposed algorithm is probabilistically complete, asymptotically optimal, and convergences exponentially fast to the optimal solution. Moreover, we demonstrate that by biasing the sampling process towards regions that are expected to be informative, the proposed method can quickly compute sensor policies that achieve user-specified levels of uncertainty in large-scale estimation tasks that may involve large multi-robot teams, workspaces, and dimensions of the hidden state. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale estimation tasks.
Target localization and tracking scenario: Two robots with limited field-of-view (blue ellipses) navigate an environment with obstacles to localize and track six targets of interest. Target uncertainty is illustrated in red.
Source: Yiannis Kantaros, Brent Schlotfeldt, Nikolay Atanasov, and George J. Pappas: ‘Asymptotically Optimal Planning for Non-myopic Multi-Robot Information Gathering’ In Proceedings of the 2019 Robotics: Science and Systems (RSS), Freiburg, Germany, June 2019.
Points of Contact: George J. Pappas
