LiU Electronic Press
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Author:
Sun, Dali (University of Freiburg, Germany) (Foundations of Artificial Intelligence)
Kleiner, Alexander (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology) (Collaborative Robotics)
Nebel, Bernhard (University of Freiburg, Germany) (Foundations of Artificial Intelligence)
Title:
Behavior-based Multi-Robot Collision Avoidance
Department:
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab
Linköping University, The Institute of Technology
Publication type:
Conference paper (Refereed)
Language:
English
In:
Robotics and Automation (ICRA), 2014
Conference:
2014 IEEE International Conference on Robotics and Automation (ICRA 2014), May 31 - June 7, 2014 Hong Kong, China
Publisher: IEEE
Year of publ.:
2014
URI:
urn:nbn:se:liu:diva-103187
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-103187
Subject category:
Robotics
Abstract(en) :

Autonomous robot teams that simultaneously dis- patch transportation tasks are playing a more and more impor- tant role in the industry. In this paper we consider the multi- robot motion planning problem in large robot teams and present a decoupled approach by combining decentralized path planning methods and swarm technologies. Instead of a central coordi- nation, a proper behavior which is directly selected according to the context is used by the robot to keep cooperating with others and to resolve path collisions. We show experimentally that the quality of solutions and the scalability of our method are significantly better than those of conventional decoupled path planning methods. Furthermore, compared to conventional swarm approaches, our method can be widely applied in large- scale environments. 

Note:

Accepted for publication

Research funder:
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications, 1025
Available from:
2014-01-14
Created:
2014-01-14
Last updated:
2014-01-29
Statistics:
90 hits