Path Planning Based on Deep Reinforcement Learning for Autonomous Underwater Vehicles Under Ocean Current Disturbance

• Published in: IEEE transactions on intelligent vehicles Vol. 8; no. 1; pp. 108 - 120
• Main Authors: Chu, Zhenzhong, Wang, Fulun, Lei, Tingjun, Luo, Chaomin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Autonomous underwater vehicle; Autonomous underwater vehicles; Deep learning; deep reinforcement learning; double deep Q network; Heuristic algorithms; Machine learning; Maneuverability; Obstacle avoidance; ocean current disturbance; Ocean currents; Oceans; Path planning; Splines (mathematics); Surface reconstruction; Surface topography; Unknown environments; Vehicle dynamics
• ISSN: 2379-8858; 2379-8904
• DOI: 10.1109/TIV.2022.3153352

The path planning issue of the underactuated autonomous underwater vehicle (AUV) under ocean current disturbance is studied in this paper. In order to improve the AUV's path planning capability in the unknown environments, a deep reinforcement learning (DRL) path planning method based on double deep Q Network (DDQN) is proposed. It is created from an improved convolutional neural network, which has two input layers to adapt to the processing of high-dimensional environments. Considering the maneuverability of underactuated AUV under current disturbance, especially, the issue of ocean current disturbance under unknown environments, a dynamic and composite reward function is developed to enable the AUV to reach the destination with obstacle avoidance. Finally, the path planning ability of the proposed method in the unknown environments is validated by simulation analysis and comparison studies.