Abstract: Autonomous obstacle avoidance has become a critical challenge for expanding the application scenarios of unmanned surface vehicles(USVs).Traditional methods for USVs obstacle avoidance mainly rely on fine-grained environmental modeling.However,in complex marine environments,USVs have difficulty obtaining complete perception states,leading to insufficient model accuracy.To address this issue,we propose an improved proximal policy optimization(PPO)-based autonomous obstacle avoidance method for USVs.First,a perception and decision framework for USVs based on Markov decision process is constructed.Then,a feature-sharing representation optimization module is designed by fusing recurrent neural networks to enhance the USV’s memory ability for temporal environmental perception.Finally,an autonomous obstacle avoidance reward function is designed by combining reward reshaping mechanisms to improve the optimization speed of the USV obstacle avoidance strategy.To verify the effectiveness of the proposed algorithm,a typical USV autonomous obstacle avoidance algorithm verification scenario is constructed on a three-dimensional simulation platform.Experimental results show that the improved PPO-based method can achieve collision-free autonomous navigation for USVs and outperforms the traditional PPO algorithm in terms of model convergence speed,collision rate,and timeout rate.

Key words: Unmanned surface vehicles, Autonomous obstacle avoidance, Proximal policy optimization, Temporal perception, Reward shaping