Abstract: Autonomous navigation systems serve as the foundation for unmanned surface ship operations, with local path planning algorithms being crucial components of these systems. The local path planning algorithms for unmanned surface ships are systematically reviewed, focusing on commonly used algorithms including the A*, Artificial Potential Field, Rapidly-exploring Random Trees, Dynamic Window Approach, Velocity Obstacle and intelligent optimization algorithms. It analyzes their fundamental principles, strengths and limitations, and summarizes their applications in path planning of unmanned surface ships. Current local path planning algorithms have demonstrated practical utility in simple scenarios such as open waters. However, significant challenges remain in complex situations, primarily due to high-density unstructured navigation environments, highly dynamic and strongly nonlinear environmental disturbances, and multi-constraint, multi-objective mission scenarios. It is recommended to further investigate the constraint-loading mechanisms of maneuvering motion models, advance multi-algorithm collaborative planning theories for full-mission scenarios and develop large model-driven decision-making and planning methodologies for unmanned surface ships. These efforts aim to address local path planning challenges in complex environments and missions. It can provide systematic references for theoretical research and engineering applications of local path planning technologies for unmanned surface ships.

Key words: unmanned surface ship, local path planning, collision avoidance, autonomous navigation system

摘要： 自主航行系统是无人船舶运行的基础,局部路径规划算法是无人船舶自主航行系统的重要组成。该文针对A*算法、人工势场算法、快速搜索随机树算法、动态窗口法、速度障碍算法及智能优化算法等常用的无人船舶局部路径规划算法进行了系统综述,分析其基本原理和优缺点,并总结其应用场景。现有的无人船舶局部路径规划算法在开阔水域等简单场景中已具备实用价值,但在处理一些复杂情况时仍面临不小的挑战,例如：高密度非结构化的航行环境、高动态强非线性的环境干扰以及多约束多目标的任务场景。在今后研究中,建议研究操纵运动模型的约束加载机理、发展面向全任务场景的多算法协同规划理论,以及构建大模型驱动的无人船舶决策规划方法,从而解决复杂环境与任务场景下的局部路径规划问题。该文将为无人船舶局部路径规划技术的理论研究与工程应用提供系统性参考。

关键词: 无人船舶, 局部路径规划, 避碰, 自主航行系统