Multi-robot Source Navigation Method Based on Coordination Graph Monte Carlo Tree Search

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Yong Xu, Danfeng Li, Malathy Batumalay, Choon Kit Chan, Long Jiang


To address the dynamic cooperative multi-robot sequential decision problem, we propose a multi-robot source navigation method based on coordination graph Monte Carlo tree search. The Monte Carlo tree search online planning is designed with the algorithm system's structure in mind. We combine the upper confidence bound strategy with the coordination graph, which represents multi-robot cooperative communication, and solve the coordination graph using the max-sum algorithm to select joint actions. This paper proposes a time series prediction model for optimizing multi-robot movement trends using historical data, which is then used to solve the exploration and exploitation problem in Monte Carlo tree search. The experimental analysis and comparison show that our proposed method outperforms the comparison algorithms in terms of source search steps, task execution time, and source search success rate, as well as robustness and efficiency for multi-robot source search.

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