期刊
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
卷 38, 期 2-3, 页码 316-337出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364918755924
关键词
Decentralized Monte Carlo tree search; multi-robot systems; decentralized planning; active perception; variational methods
类别
资金
- Australian Research Council [DP140104203]
- Australian Centre for Field Robotics
- New South Wales State Government
- Faculty of Engineering and Information Technologies, The University of Sydney, under the Faculty Research Cluster Program
- University of Sydney's International Research Collaboration Award
We propose a decentralized variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimize its own actions by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of their search trees, which are used to update the joint distribution using a distributed optimization approach inspired by variational methods. Our method admits any objective function defined over robot action sequences, assumes intermittent communication, is anytime, and is suitable for online replanning. Our algorithm features a new MCTS tree expansion policy that is designed for our planning scenario. We extend the theoretical analysis of standard MCTS to provide guarantees for convergence rates to the optimal payoff sequence. We evaluate the performance of our method for generalized team orienteering and online active object recognition using real data, and show that it compares favorably to centralized MCTS even with severely degraded communication. These examples demonstrate the suitability of our algorithm for real-world active perception with multiple robots.
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