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Decentralized Multi-Robot Cooperation with Auctioned POMDPs

Jesús Capitán, Matthijs T. J. Spaan, Luis Merino, and An\'ibal Ollero. Decentralized Multi-Robot Cooperation with Auctioned POMDPs. In Multi-agent Sequential Decision Making in Uncertain Domains, 2011. Workshop at AAMAS11

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Abstract

Planning under uncertainty faces a scalability problem when considering multi-robot teams, as the information space scales exponentially with the number of robots. To address this issue, this paper proposes to decentralize multi-agent Partially Observable Markov Decision Process (POMDPs) while maintaining cooperation between robots by using POMDP policy auctions. Auctions provide a flexible way of coordinating individual policies modeled by POMDPs and have low communication requirements. Additionally, communication models in the multi-agent POMDP literature severely mismatch with real inter-robot communication. We address this issue by applying a decentralized data fusion method in order to efficiently maintain a joint belief state among the robots. The paper focuses on a cooperative tracking application, in which several robots have to jointly track a moving target of interest. The proposed ideas are illustrated in real multi-robot experiments, showcasing the flexible and robust coordination that our techniques can provide.

BibTeX Entry

@InProceedings{Capitan11msdm,
  author =       {Jes\'us Capit\'an and Matthijs T. J. Spaan and Luis
                  Merino and An\'ibal Ollero},
  title =        {Decentralized Multi-Robot Cooperation with Auctioned
                  {POMDPs}},
  booktitle =    {Multi-agent Sequential Decision Making in Uncertain
                  Domains},
  year =         2011,
  note =         {Workshop at AAMAS11}
}

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