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Decentralized multi-robot planning to explore and perceive

Laëtitia Matignon 1 Laurent Jeanpierre 2 Abdel-Illah Mouaddib 2
1 SMA - Systèmes Multi-Agents
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 Equipe MAD - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : In a recent French robotic contest, the objective was to develop a multi-robot system able to autonomously map and explore an unknown area while also detecting and localizing objects. As a participant in this challenge, we proposed a new decentralized Markov decision process (Dec-MDP) resolution based on distributed value functions (DVF) to compute multi-robot exploration strategies. The idea is to take advantage of sparse interactions by allowing each robot to calculate locally a strategy that maximizes the explored space while minimizing robots interactions. In this paper, we propose an adaptation of this method to improve also object recognition by integrating into the DVF the interest in covering explored areas with photos. The robots will then act to maximize the explored space and the photo coverage, ensuring better perception and object recognition.
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Contributor : Laetitia Matignon <>
Submitted on : Thursday, September 17, 2015 - 10:21:15 AM
Last modification on : Wednesday, July 8, 2020 - 12:43:50 PM

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Laëtitia Matignon, Laurent Jeanpierre, Abdel-Illah Mouaddib. Decentralized multi-robot planning to explore and perceive. Acta Polytechnica, Czech Technical University in Prague, 2015, 55 (3), pp.169-176. ⟨10.14311/AP.2015.55.0169⟩. ⟨hal-01200755⟩



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