测试科学与仪器2025，Vol.16Issue(2)：245-257,13.DOI:10.62756/jmsi.1674-8042.2025024
改进麻雀搜索算法的无人水面艇路径规划
Unmanned surface vehicles path planning with improved sparrow search algorithm
摘要
Abstract
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.关键词
适应度函数/转向角/混沌映射/反向惯性权重/柯西分布/正弦混沌映射Key words
fitness function/steering angle/chaotic mapping/inverted inertia weights/Cauchy distribution/sinusoidal chaotic mapping引用本文复制引用
于豪,王鑫,彭皓..改进麻雀搜索算法的无人水面艇路径规划[J].测试科学与仪器,2025,16(2):245-257,13.基金项目
This work was supported by Shandong Provincial Department of Science and Technology Project(No.2022C01246) （No.2022C01246）
National Undergraduate Innovation Training Project(Nos.202410390028,202310390026),Fujian Provincial Undergraduate Innovation Training Project(No.202410390093),and Jimei University Innovation Training Project(Nos.2024xj224,2023xj179). （Nos.202410390028,202310390026）
