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Using an Adaptive Fuzzy Neural Network Based on a Multi-Strategy-Based Artificial Bee Colony for Mobile Robot Control

Author

Listed:
  • Cheng-Hung Chen

    (Department of Electrical Engineering, National Formosa University, Yunlin 632, Taiwan)

  • Shiou-Yun Jeng

    (Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan)

  • Cheng-Jian Lin

    (Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan
    College of Intelligence, National Taichung University of Science and Technology, Taichung 404, Taiwan)

Abstract

This study proposes an adaptive fuzzy neural network (AFNN) based on a multi-strategy artificial bee colony (MSABC) algorithm for achieving an actual mobile robot navigation control. During the navigation control process, the AFNN inputs are the distance between the ultrasonic sensors and the angle between the mobile robot and the target, and the AFNN outputs are the robot’s left- and right-wheel speeds. A fitness function in reinforcement learning is defined to evaluate the navigation control performance of AFNN. The proposed MSABC algorithm improves the poor exploitation disadvantage in the traditional artificial bee colony (ABC) and adopts the mutation strategies of a differential evolution to balance exploration and exploitation. To escape in special environments, a manual wall-following fuzzy logic controller (WF-FLC) is designed. The experimental results show that the proposed MSABC method has improved the performance of average fitness, navigation time, and travel distance by 79.75%, 33.03%, and 10.74%, respectively, compared with the traditional ABC method. To prove the feasibility of the proposed controller, experiments were carried out on the actual PIONEER 3-DX mobile robot, and the proposed navigation control method was successfully completed.

Suggested Citation

  • Cheng-Hung Chen & Shiou-Yun Jeng & Cheng-Jian Lin, 2020. "Using an Adaptive Fuzzy Neural Network Based on a Multi-Strategy-Based Artificial Bee Colony for Mobile Robot Control," Mathematics, MDPI, vol. 8(8), pages 1-18, July.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1223-:d:389727
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    Cited by:

    1. Hou, Guolian & Fan, Yuzhen & Wang, Junjie, 2024. "Application of a novel dynamic recurrent fuzzy neural network with rule self-adaptation based on chaotic quantum pigeon-inspired optimization in modeling for gas turbine," Energy, Elsevier, vol. 290(C).

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