Abstract: Unmanned Surface Vehicle(USV) path tracking is crucial for marine autonomous operations,as environmental factors such as wind,waves,currents,and USV’s control errors can affect tracking performance.Reinforcement learning(RL),with its online interaction and real-time feedback,offers a promising approach for actively adapting to dynamic environments.However,its trial-and-error training process poses safety risks in real-world applications,and the gap between ideal simulation environments and complex real-world conditions further limits its practical effectiveness.To address these challenges,this paper proposes LECUP(Line-of-sight-guided self-Expert Cloning for USV Path tracking),a new algorithm designed for complex marine environments.LECUP first trains an RL expert in a still water environment and then uses self-expert cloning to transfer the agent to a more complex environment.To ensure effective knowledge transfer,LECUP introduces a data filling mechanism,where the ex-periences accumulated by the self-expert in the still-water environment are dimensionally padded and stored for initializing the agent in the complex environment.Then,reinforcement learning is used to fine-tune the agent in the complex environment,further enabling adaptation to the complexities of the environment.Moreover,LECUP incorporates a line-of-sight guidance module to calculate the target heading,decoupling the path tracking control from the specific geometry of the path and enhancing the USV’s adaptability to various path shapes.This method enables ongoing policy refinement in complex environments while mitigating safety risks associated with random initialization.Extensive experimental results show that LECUP performs better than baseline methods in path tracking tasks,especially under challenging conditions.

Key words: Unmanned surface vehicle, Path tracking, Reinforcement learning, Self-expert cloning, Line of sight