ICRA 2012 Paper Abstract

Close

Paper TuD05.1

Matignon, Laetitia (Université de Caen Basse-Normandie - GREYC/CNRS), Jeanpierre, Laurent (University of Caen), Mouaddib, Abdel-Illah (GREYC-UMR 6072)

Distributed Value Functions for Multi-Robot Exploration

Scheduled for presentation during the Regular Session "Multi-Robot Systems II" (TuD05), Tuesday, May 15, 2012, 16:30−16:45, Meeting Room 5 (Ska)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on June 22, 2018

Keywords Networked Robots, Planning, Scheduling and Coordination, Robust/Adaptive Control of Robotic Systems

Abstract

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 maximizes the space coverage of the team. Our technique has been implemented and evaluated in real-world and simulated experiments.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2018 PaperCept, Inc.
Page generated 2018-06-22  21:09:43 PST  Terms of use