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.