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Fuzzy behavior-based control trained by module learning to acquire the adaptive behaviors of mobile robots

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  • Izumi, Kiyotaka
  • Watanabe, Keigo

Abstract

Intelligent control techniques for robotic systems have been used with some success in a wide variety of applications. In this paper, we construct a method for the intelligent control system of a robot using the fuzzy behavior-based control, which decomposes the control system into several elemental behaviors, and each one is realized by fuzzy reasoning. In particular, a module learning method is investigated for obtaining each representative group behavior, so that the robot can, consequently, acquire more general knowledge or fuzzy reasoning, than a central learning method. The proposed method is applied for an obstacle-avoidance problem of a mobile robot; the effectiveness of the method is illustrated through some simulations.

Suggested Citation

  • Izumi, Kiyotaka & Watanabe, Keigo, 2000. "Fuzzy behavior-based control trained by module learning to acquire the adaptive behaviors of mobile robots," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 51(3), pages 233-243.
    • Handle: RePEc:eee:matcom:v:51:y:2000:i:3:p:233-243
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    Cited by:

    1. My, Chu Anh & Makhanov, Stanislav S. & Van, Nguyen A. & Duc, Vu M., 2020. "Modeling and computation of real-time applied torques and non-holonomic constraint forces/moment, and optimal design of wheels for an autonomous security robot tracking a moving target," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 170(C), pages 300-315.

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