Distributed Value Functions for Multi-Robot Exploration: a Position Paper

Laëtitia Matignon (1) , Laurent Jeanpierre (1) , Abdel-Illah Mouaddib (1)

1 Equipe MAD - Laboratoire GREYC - UMR6072

This paper addresses the problem of exploring an unknown area with a team of autonomous robots using decentralized decision making techniques. The localization aspect is not considered and it is assumed the robots share their positions and have access to a map updated with all explored areas. A key problem is then the coordination of decentralized decision processes: each individual robot must choose appropriate exploration goals so that the team simultaneously explores different locations of the environment. We formalize this problem as a Decentralized Markov Decision Process (Dec- MDP) solved as a set of individual MDPs, where interactions between MDPs are considered in a distributed value function. Thus each robot computes locally a strategy that minimizes the interactions between the robots and maximises the space coverage of the team. Our technique has been implemented and evaluated in real-world and simulated experiments.

Domaines: Intelligence artificielle [cs.AI]; Modélisation et simulation; Système multi-agents [cs.MA]

Laëtitia Matignon, Laurent Jeanpierre, Abdel-Illah Mouaddib. Distributed Value Functions for Multi-Robot Exploration: a Position Paper. Multi-Agent Sequential Decision Making in Uncertain Multi-Agent Domain (MSDM) (workshop of AAMAS), 2012, France. ⟨hal-00966784⟩