Abstract | ||
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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-robot partially observable Markov decision processes (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. In addition, communication models in the multi-agent POMDP literature severely mismatch with real inter-robot communication. We address this issue by exploiting a decentralized data fusion method in order to efficiently maintain a joint belief state among the robots. The paper presents two different applications: environmental monitoring with unmanned aerial vehicles (UAVs); and cooperative tracking, in which several robots have to jointly track a moving target of interest. The first one is used as a proof of concept and illustrates the proposed ideas through different simulations. The second one adds real multi-robot experiments, showcasing the flexibility and robust coordination that our techniques can provide. |
Year | DOI | Venue |
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2013 | 10.1177/0278364913483345 | ICAPS |
Keywords | DocType | Volume |
multi-robot cooperation,planning under uncertainty,decentralized data fusion | Journal | 32 |
Issue | ISSN | Citations |
6 | 2334-0835 | 12 |
PageRank | References | Authors |
0.59 | 42 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Jesús Capitán | 1 | 19 | 2.43 |
Matthijs T.J. Spaan | 2 | 863 | 63.84 |
Luis Merino | 3 | 325 | 26.09 |
Aníbal Ollero | 4 | 1109 | 123.55 |