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A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning

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  • Haoqian Huang
  • Chao Jin
  • Hassan Zargarzadeh

Abstract

In order to solve the problems of rapid path planning and effective obstacle avoidance for autonomous underwater vehicle (AUV) in 2D underwater environment, this paper proposes a path planning algorithm based on reinforcement learning mechanism and particle swarm optimization (RMPSO). A feedback mechanism of reinforcement learning is embedded into the particle swarm optimization (PSO) algorithm by using the proposed RMPSO to improve the convergence speed and adaptive ability of the PSO. Then, the RMPSO integrates the velocity synthesis method with the Bezier curve to eliminate the influence of ocean currents and save energy for AUV. Finally, the path is developed rapidly and obstacles are avoided effectively by using the RMPSO. Simulation and experiment results show the superiority of the proposed method compared with traditional methods.

Suggested Citation

  • Haoqian Huang & Chao Jin & Hassan Zargarzadeh, 2021. "A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning," Complexity, Hindawi, vol. 2021, pages 1-13, December.
    • Handle: RePEc:hin:complx:8993173
    DOI: 10.1155/2021/8993173
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    Cited by:

    1. Yan, Zheping & Yan, Jinyu & Wu, Yifan & Cai, Sijia & Wang, Hongxing, 2023. "A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 209(C), pages 55-86.

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