An autonomous robot team can be employed for continuous and strategic coverage of arbitrary environments for different missions. In this work, we propose an anytime approach for creating multi-robot patrolling policies. Our approach involves a novel extension of Monte Carlo Tree Search (MCTS) to allow robots to have life-long, cyclic policies so as to provide continual coverage of an environment. Our proposed method can generate near-optimal policies for a team of robots for small environments in real-time (and in larger environments in under a minute). By incorporating additional planning heuristics we are able to plan coordinated patrolling paths for teams of several robots in large environments quickly on commodity hardware.
|Original language||English (US)|
|Number of pages||6|
|Journal||Proceedings - IEEE International Conference on Robotics and Automation|
|State||Published - Jun 29 2015|
|Event||2015 IEEE International Conference on Robotics and Automation, ICRA 2015 - Seattle, United States|
Duration: May 26 2015 → May 30 2015