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.


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    Titel :

    Stochastic Tree Search with Useful Cycles for patrolling problems


    Beteiligte:
    Kartal, Bilal (Autor:in) / Godoy, Julio (Autor:in) / Karamouzas, Ioannis (Autor:in) / Guy, Stephen J. (Autor:in)


    Erscheinungsdatum :

    2015-05-01


    Format / Umfang :

    313774 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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