A Path-Planning Approach Based on Potential and Dynamic Q-Learning for Mobile Robots in Unknown Environment.

Journal: Computational intelligence and neuroscience
Published Date: Jun 2, 2022

Abstract

The path-planning approach plays an important role in determining how long the mobile robots can travel. To solve the path-planning problem of mobile robots in an unknown environment, a potential and dynamic Q-learning (PDQL) approach is proposed, which combines Q-learning with the artificial potential field and dynamic reward function to generate a feasible path. The proposed algorithm has a significant improvement in computing time and convergence speed compared to its classical counterpart. Experiments undertaken on simulated maps confirm that the PDQL when used for the path-planning problem of mobile robots in an unknown environment outperforms the state-of-the-art algorithms with respect to two metrics: path length and turning angle. The simulation results show the effectiveness and practicality of the proposal for mobile robot path planning.

Authors

  • Bing Hao
    College of Computer and Control Engineering, Qiqihar University, Qiqihar, China.
  • He Du
    College of Computer and Control Engineering, Qiqihar University, Qiqihar, China.
  • Jianshuo Zhao
    College of Computer and Control Engineering, Qiqihar University, Qiqihar, China.
  • Jiamin Zhang
    College of Computer and Control Engineering, Qiqihar University, Qiqihar, China.
  • Qi Wang
    Biotherapeutics Discovery Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.

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