Unmanned surface vehicles path planning with improved sparrow search algorithm

To enable optimal navigation for unmanned surface vehicle (USV), we proposed an adaptive hybrid strategy-based sparrow search algorithm (SSA) for efficient and reliable path planning. The proposed method began by enhancing the fitness function to comprehensively account for critical path planning metrics, including path length, turning angle, and navigation safety. To improve search diversity and effectively avoid premature convergence to local optima, chaotic mapping was employed during the population initialization stage, allowing the algorithm to explore a wider solution space from the outset. A reverse inertia weight mechanism was introduced to dynamically balance exploration and exploitation across different iterations. The adaptive adjustment of the inertia weight further improved convergence efficiency and enhanced global optimization performance. In addition, a Cauchy-Gaussian hybrid update strategy was incorporated to inject randomness and variation into the search process, which helped the algorithm escape local minima and maintain a high level of solution diversity. This approach significantly enhanced the robustness and adaptability of the optimization process. Simulation experiments confirmed that the improved SSA consistently outperformed benchmark algorithms such as the original SSA, PSO, and WMR-SSA. Compared with the three algorithms, in the simulated sea area, the path lengths of the proposed algorithm are reduced by 21%, 21%, and 16%, respectively, and under the actual sea simulation conditions, the path lengths are reduced by 13%, 15%, and 11%, respectively. The results highlighted the effectiveness and practicality of the proposed method, providing an effective solution for intelligent and autonomous USV navigation in complex ocean environments.