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New automated learning CPG for rhythmic patterns

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Abstract

In this paper, we suggest a new supervised learning method called Fourier based automated learning central pattern generators (FAL-CPG), for learning rhythmic signals. The rhythmic signal is analyzed with Fourier analysis and fitted with a finite Fourier series. CPG parameters are selected by direct comparison with the Fourier series. It is shown that the desired rhythmic signal is learned and reproduced with high accuracy. The resulting CPG network offers several advantages such as, modulation and robustness against perturbation. The proposed learning method is simple, straightforward and efficient. Furthermore, it is suitable for on-line applications. The effectiveness of the proposed method is shown by comparison with four other supervised learning methods as well as an industrial robotic trajectory following application.

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Authors and Affiliations

  1. Center for Applied Research on Soft Computing and Intelligent Systems (CARSIS), Mechanical Engineering Department, Ferdowsi University of Mashhad, P.O. Box 9177948974, FUM campus, Azadi Sq., Mashhad, Khorasan Razavi, Iran

    Yadollah Farzaneh, Alireza Akbarzadeh & Ali Akbar Akbari

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  1. Yadollah Farzaneh
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  2. Alireza Akbarzadeh
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  3. Ali Akbar Akbari
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Correspondence to Yadollah Farzaneh.

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Farzaneh, Y., Akbarzadeh, A. & Akbari, A.A. New automated learning CPG for rhythmic patterns. Intel Serv Robotics 5, 169–177 (2012). https://doi.org/10.1007/s11370-012-0111-5

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  • DOI: https://doi.org/10.1007/s11370-012-0111-5

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