Stochastic Tree Search with Useful Cycles for patrolling problems

Bilal Kartal, Julio Godoy, Ioannis Karamouzas, Stephen J. Guy

Research output: Contribution to journalConference articlepeer-review

18 Scopus citations

Abstract

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 languageEnglish (US)
Article number7139357
Pages (from-to)1289-1294
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
Volume2015-June
Issue numberJune
DOIs
StatePublished - Jun 29 2015
Event2015 IEEE International Conference on Robotics and Automation, ICRA 2015 - Seattle, United States
Duration: May 26 2015May 30 2015

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