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A learning particle swarm optimization algorithm for odor source localization

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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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Correspondence to Qiang Lu.

Additional information

This work was supported by National Natural Science Foundation of China (No. 60675043), Natural Science Foundation of Zhejiang Province of China (No. Y1090426, No.Y1090956), and Technical Project of Zhejiang Province of China (No. 2009C33045).

Qiang Lu received the B. Sc. and Ph.D. degrees in control theory and control engineering from the East China University of Science and Technology, PRC in 2000 and 2007, respectively. In 2007, he joined Hangzhou Dianzi University, Hangzhou, PRC, where he is currently an associate professor in the School of Automation.

His research interests include cooperative control of multi-robot system, swarm intelligence, and their applications for human security.

Ping Luo received the B. Sc. degree in electrical theory and new technology from the North China Electric Power University (Baoding Campus), PRC in 2000 and Ph.D. degree in electrical engineering from Zhejiang University in 2006. She joined Hangzhou Dianzi University, Hangzhou, PRC, where she is currently a lecturer in the School of Automation.

Her research interests include evolutionary computation, inverse problem of electrical engineering, and application of renewable energy.

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Lu, Q., Luo, P. A learning particle swarm optimization algorithm for odor source localization. Int. J. Autom. Comput. 8, 371–380 (2011). https://doi.org/10.1007/s11633-011-0594-0

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  • DOI: https://doi.org/10.1007/s11633-011-0594-0

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