Cooperative Multi-robot Map-Building Under Unknown Environment

In this paper, multi-robot map building problem in a complex and unknown environment is investigated, and a map building approach is presented based on particle swarm optimization algorithm for global optimization, as well as Hilbert curve on the target region detection of multi-robot cooperative. P...

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Bibliographic Details
Published in2009 International Conference on Artificial Intelligence and Computational Intelligence Vol. 3; pp. 392 - 396
Main Authors Chunyang Liu, Yingwei Ma, Chang'an Liu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Artificial intelligence
Computational intelligence
Computer science
Evolutionary computation
Intelligent robots
map building
Mobile robots
multi-robot cooperation
particle
Particle swarm optimization
Robot sensing systems
Shape
Uncertainty
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Summary:In this paper, multi-robot map building problem in a complex and unknown environment is investigated, and a map building approach is presented based on particle swarm optimization algorithm for global optimization, as well as Hilbert curve on the target region detection of multi-robot cooperative. Particle Swarm Optimization has characteristics of evolutionary computation and swarm intelligence, which can provide a good way to make different robots away from each other, near to their last destination and shortest time of arriving each region between robots during a map-building process. Hilbert curve can avoid duplication of the same detection area with the detection radius of the robot. Simulation experiment of comparing with S shape random exploring algorithm shows that this method will enable the robot to find the approximate optimal target area, reduce the probability of duplicate detection, and improve the efficiency of detection.
ISBN:1424438357
9781424438358
DOI:10.1109/AICI.2009.271