Efficient Dynamic Path Planning Algorithm for ASVs Based on Transfer Reinforcement Learning

Autonomous surface vessels (ASVs) play a crucial role in military, civilian and scientific research fields, and path planning technology is the foundation for ensuring the navigation of ASVs. However, reinforcement learning algorithms face challenges in path planning, such as low sample utilization efficiency and difficulties in collecting effective data. To address these issues, this paper proposes an efficient reinforcement learning path planning algorithm based on knowledge transfer. First, a state similarity model is introduced to measure the differences between source and target samples. By identifying and transferring similar samples, the learning speed for new tasks is significantly accelerated. Second, considering the complex rules of the International Regulations for Preventing Collisions at Sea (COLREGs), high-quality collision avoidance samples from the source task are selected as knowledge transfer objects, thereby reducing the failure rate of agents in the collision avoidance process. Finally, the impact of the proportion of source task samples in the total sample pool on algorithm performance is analyzed. A progressive exploration method based on sample proportion is established to mitigate the issue of negative transfer. Simulation results demonstrate that the collision avoidance success rate of the algorithm has been significantly improved, with an average success rate of 91% across five typical collision avoidance scenarios—an increase of over 30% compared to the traditional DDPG algorithm. The convergence speed of the reward value improved by 50% after 1000 training episodes. Real-world ship tests verified that it can achieve real-time collision avoidance in complex environments, with a path safety compliance rate of 100%. The algorithm effectively enhances the reliability and safety of autonomous navigation for ASVs.