DQN与规则结合的智能船舶动态自主避障决策

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中图分类号：U675.73 文献标志码：A DOI：10.12305/j.issn.1001-506X.2025.06.27

Abstract：Current intelligent ship collsion avoidance decision-making faces challenges such as repetitive training and difficulty in adapting to diverse encounter scenarios.An intellgent ship dynamicautonomous obstacle avoidance decision-making algorithm based on deep Q network（DQN）is proposed.The proposed algorithm designs a partially observable autonomous obstacle avoidance model that improves and trains deep network through deep reinforcement learning. By employing a training approach with random start and end points，the proposed algorithm enables intellgent ships to achieve autonomous collsion avoidance in environments combining dynamic and static scenarios without the need for repetitive training. Simulation experiments validate that the proposed algorithm can achieve autonomous collsion avoidance decision-making without repeated training，therebyreducing training costs.It demonstrates a certain level of generalization capability and robustness，offering a solution for autonomous collision avoidance in complex navigation environment for intelligent ships.

Keywords：dynamic autonomous obstacle avoidance； inteligent ship；without repetitive training；deep reinforcement learning （DRL）

0 引言

责观察周围情况并遵守《国际海上避碰规则》（Conventionon the International Regulations forPreventing Collisions atSea，COLREGs）来确保船舶安全[1。（剩余13686字）