Name
Abstract | ||
|---|---|---|
In this paper, we propose an approach based on an interaction-oriented resolution of decentralized Markov decision processes (Dec-MDPs) primary motivated by a real-world application of decentralized decision makers to explore and map an unknown environment. This interaction-oriented resolution is based on distributed value functions (DVF) techniques that decouple the multi-agent problem into a set of individual agent problems and consider possible interactions among agents as a separate layer. This leads to a significant reduction of the computational complexity by solving Dec-MDPs as a collection of MDPs. Using this model in multi-robot exploration scenarios, we show that each robot computes locally a strategy that minimizes the interactions between the robots and maximizes the space coverage of the team. Our technique has been implemented and evaluated in simulation and in real-world scenarios during a robotic challenge for the exploration and mapping of an unknown environment by mobile robots. Experimental results from real-world scenarios and from the challenge are given where our system was vice-champion. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.5555/2343896.2343925 | AAMAS |
Keywords | Field | DocType |
multi-robot exploration scenario,decentralized markov decision process,unknown environment,interaction-oriented resolution,decentralized decision maker,real-world application,value function,real-world scenario,computational complexity,robotic challenge,planning | Computer science,Markov decision process,Artificial intelligence,Robot,Mobile robot,Machine learning,Robot planning,Computational complexity theory,Distributed computing | Conference |
ISBN | Citations | PageRank |
0-9817381-3-3 | 1 | 0.36 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laëtitia Matignon | 1 | 88 | 9.43 |
| Laurent Jeanpierre | 2 | 65 | 9.16 |
| Abdel-Illah Mouaddib | 3 | 310 | 44.84 |