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Evolutionary design method of probabilistic finite state machine for swarm robots aggregation

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Abstract

This paper proposes to use evolutionary computations to determine the parameters of probabilistic finite state machine controllers for swarm robots. The robots are evolved to perform an aggregation task. This problem was formulated as an optimization problem and solved by the PSO. Several computer simulations were conducted to investigate the validity of the proposed method. The results obtained in this paper show us that the proposed method is useful for the aggregation problem and the best evolved controllers are feasible as well as interpretable. This would be transferable to real swarm robots problems.

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Notes

  1. Şahin [3] claimed that these criteria should be used as a measure of the degree of SR in a particular study.

  2. \(N_o\) denotes the number of robots when the PSO was applied to design the PFSM.

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Authors and Affiliations

  1. Setsunan University, 17-8 Ikeda-Nakamachi, Neyagawa, Osaka, 572-8508, Japan

    Yoshiaki Katada

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  1. Yoshiaki Katada
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Correspondence to Yoshiaki Katada.

Additional information

SWARM This work was presented in part at the 2nd International Symposium on Swarm Behavior and Bio-Inspired Robotics, Kyoto, October 29–November 1, 2017.

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Katada, Y. Evolutionary design method of probabilistic finite state machine for swarm robots aggregation. Artif Life Robotics 23, 600–608 (2018). https://doi.org/10.1007/s10015-018-0496-0

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