AUV Path Planning Based on Behavior Cloning and Improved DQN in Partially Unknown Environments

Authors

Lijing Xing, School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Min Li, School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Xiangguang Zeng, School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Ping Zhang, University of Electronic Science and Technology of China, Chengdu 610031, China
Bei Peng, University of Electronic Science and Technology of China, Chengdu 610031, China

Abstract

Abstract: To address the problems of large randomness and slow convergence of the DQN dynamic path planning algorithm for a single autonomous underwater vehicle (AUV) in a partially unknown environment, a path planning method combining behavior cloning with A* algorithm and DQN (BA_DQN) was proposed. Based on the known environmental information, an improved A* algorithm incorporating ocean current resistance was proposed to guide DQN, thereby reducing the randomness of the DQN algorithm. By considering the complexity of the marine environment, the sampling probability was improved again after expanding the positive experience pool to enhance the training success rate. To address the problem of slow convergence in DQN, an improved algorithm based on reinforcement learning followed by behavior cloning was proposed. The BA_DQN was used to control AUV pathfinding, and simulation experiments were carried out in different task scenarios. The simulation results show that the training time of the BA_DQN algorithm is shorter than that of the DQN algorithm; its decision-making is faster than that of the A* algorithm, and its sailing time is shorter.

Recommended Citation

Xing, Lijing; Li, Min; Zeng, Xiangguang; Zhang, Ping; and Peng, Bei (2025) "AUV Path Planning Based on Behavior Cloning and Improved DQN in Partially Unknown Environments," Journal of System Simulation: Vol. 37: Iss. 11, Article 5.
DOI: 10.16182/j.issn1004731x.joss.24-0678
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss11/5

First Page

2754

Last Page

2767

CLC

TP242; TP391.9

Recommended Citation

Xing Lijing, Li Min, Zeng Xiangguang, et al. AUV Path Planning Based on Behavior Cloning and Improved DQN in Partially Unknown Environments[J]. Journal of System Simulation, 2025, 37(11): 2754-2767.

Corresponding Author

Li Min

DOI

10.16182/j.issn1004731x.joss.24-0678