A learning particle swarm optimization algorithm for odor source localization |
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Authors: | Qiang Lu Ping Luo |
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Affiliation: | (1) Department of Physical Electronics, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan;(2) Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei, Tokyo 184-8588, Japan; |
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Abstract: | This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization
algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the
proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information,
wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated.
Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed.
Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm
is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally,
the effectiveness of the proposed algorithm is illustrated for the odor source localization problem. |
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Keywords: | Multi-robot system odor source localization particle swarm optimization source probability map distributed coordination architecture |
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