中国舰船研究2025，Vol.20Issue(3)：275-287,13.DOI:10.19693/j.issn.1673-3185.03679
基于改进麻雀搜索算法的AUV路径规划方法
AUV path planning method based on improved sparrow search algorithm
摘要
Abstract
[Objective]To address the challenges of complex underwater environments,particularly the un-certainties in ocean currents,this study proposes an improved sparrow search algorithm(ISSA)for autonom-ous underwater vehicles(AUVs)path planning.The goal is to enhance the efficiency and robustness of path planning by minimizing navigation time and improving path stability in uncertain conditions.[Method]The proposed ISSA incorporates several key enhancements to the classic sparrow search algorithm(SSA).First,a vector analysis method is developed to evaluate interval responses,allowing the algorithm to effectively handle uncertainties in ocean currents.By modeling the uncertain ocean currents as intervals,the algorithm can accurately calculate the energy consumption and navigation times for different paths.Second,the ISSA in-troduces segmented learning and quantum mechanisms to improve global search capabilities.These mechan-isms enable the algorithm to dynamically adjust its search strategy by learning from both elite and marginal in-dividuals within the population,thereby enhancing diversity and preventing premature convergence.Third,a Cauchy-Gaussian mechanism is integrated into the update formula to balance global exploration and local ex-ploitation during the search process.Finally,the population size is dynamically updated using Thompson sampling,allowing the algorithm to adaptively allocate computational resources based on the complexity of the environment.[Results]Simulation results demonstrate that the ISSA significantly outperforms the ori-ginal SSA and other state-of-the-art algorithms,such as particle swarm optimization(PSO),differential evolu-tion(DE),artificial bee colony(ABC),and whale optimization algorithm(WOA).Specifically,the ISSA re-duces the average maximum navigation time by 49.88%compared to the original SSA and decreases the fail-ure rate in extreme ocean current conditions by 10.6%.The ISSA also exhibits superior convergence speed,achieving near-optimal paths in approximately 20 iterations,while other algorithms require around 40 itera-tions to approach the global optimum.Moreover,the ISSA shows a lower average fitness value,indicating bet-ter optimization performance and path planning efficiency.[Conclusion]The ISSA demonstrates strong global search capabilities and robustness in dynamic and uncertain underwater environments,making it a promising solution for AUV path planning.The improvements in convergence characteristics and the ability to handle complex ocean currents highlight the algorithm's potential for practical applications.Future work will focus on further optimizing the computational efficiency of ISSA and exploring its application in more diverse and challenging underwater scenarios.关键词
自主水下航行器/三维路径规划/麻雀搜索算法/区间优化/矢量分析法/运动规划Key words
autonomous underwater vehicles/three-dimensional path planning/sparrow search algorithm/interval optimization/vector analysis method/motion planning分类
交通工程引用本文复制引用
唐李军,范云霞,周星宇,孙骞..基于改进麻雀搜索算法的AUV路径规划方法[J].中国舰船研究,2025,20(3):275-287,13.基金项目
国家自然科学基金面上项目资助(52271311) （52271311）
