Abstract
This paper proposes the particle swarm optimization based on Shannon’s entropy to deal with the problem of odor source localization. First, a measurement model by which the robots can always observe a position is briefly described. When the detection events occur, the position of the odor source lies in the vicinity of the observed position with a higher probability. When the non-detection events occur, the position of the odor source does not lie in the vicinity of the observed position with a higher probability. Second, on the basis of the measurement model, the posteriori probability distribution on the position of the odor source is established where the detection events and non-detection events are taken into account. Third, each robot can understand the search environment by using Shannon’s entropy which can be calculated in terms of the posteriori probability distribution on the position of the odor source. Moreover, each robot should move toward the direction of the entropy reduction. By means of this principle, the particle swarm optimization algorithm is introduced to plan the movement of the robot group. Finally, the effectiveness of the proposed approach is investigated for the problem of odor source localization.
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Li, N., Lu, Q., He, Y., Wang, J. (2014). Particle Swarm Optimization Based on Shannon’s Entropy for Odor Source Localization. In: Fei, M., Peng, C., Su, Z., Song, Y., Han, Q. (eds) Computational Intelligence, Networked Systems and Their Applications. ICSEE LSMS 2014 2014. Communications in Computer and Information Science, vol 462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45261-5_15
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DOI: https://doi.org/10.1007/978-3-662-45261-5_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45260-8
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