Research on LSTM-PPO Obstacle Avoidance Algorithm and Training Environment for Unmanned Surface Vehicles

作者：Wan Luo, Xiang Wang, Fang Han, Zhiguo Zhou, Junyu Cai, Lin Zeng, Hong Chen, Jiawei Chen, Xuehua Zhou · 发表于：Journal of Marine Science and Engineering · 年份：2025 · DOI：10.3390/jmse13030479 · 被引用次数：16 · 研究领域：Maritime Navigation and Safety

The current unmanned surface vehicle (USV) intelligent obstacle avoidance algorithm based on deep reinforcement learning usually adopts the mass point model to train in an ideal environment. However, in actual navigation, due to the influence of the ship model and the water surface environment, the training set is triggered. The reward function does not match the actual situation, resulting in a poor obstacle avoidance effect. In response to the above problems, this paper proposes a long and short memory network-proximal strategy optimization (LSTM-PPO) intelligent obstacle avoidance algorithm for non-particle models in non-ideal environments, and designs a corresponding deep reinforcement learning training environment. We integrate the motion characteristics of the unmanned boat and the influencing factors of the surface environment, based on the curiosity-driven set reward function, to improve its autonomous obstacle avoidance ability, combined with the LSTM network to identify and save obstacle information to improve the adaptability to the unknown environment; virtual simulation is performed in Unity. The engine builds a USV physical model and a refined water deep reinforcement learning training environment including a variety of obstacle models. The experimental results demonstrate that the LSTM-PPO algorithm exhibits an effective and rational obstacle avoidance effect, with a success rate of 86.7%, an average path length of 198.52 m, and a convergence time of 1.5 h. A c...