基于CSSOA的多船智能避碰决策研究  

Multi-vessel intelligent collision avoidance decision-making based on CSSOA

作　　者：徐言民[1,2,3] 律建辉 刘佳仑 李龙浩[4] 关宏旭 XU Yanmin;LYU Jianhui;LIU Jialun;LI Longhao;GUAN Hongxu(School of Navigation,Wuhan University of Technology,Wuhan 430063,China;Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Zhuhai 519082,China;Hubei Key Laboratory of Inland Shipping Technology,Wuhan 430063,China;Intelligent Transportation Systems Research Center,Wuhan University of Technology,Wuhan 430063,China;National Engineering Research Center for Water Transportation Safety,Wuhan 430063,China)

机构地区：[1]武汉理工大学航运学院,湖北武汉430063 [2]南方海洋科学与工程广东省实验室(珠海),广东珠海519082 [3]内河航运技术湖北省重点实验室,湖北武汉430063 [4]武汉理工大学智能交通系统研究中心,湖北武汉430063 [5]国家水运安全工程技术研究中心,湖北武汉430063

出　　处：《中国舰船研究》2023年第6期88-96,共9页Chinese Journal of Ship Research

基　　金：国家自然科学基金资助项目(62003250);南方海洋科学与工程广东省实验室(珠海)资助项目(SML2021SP101)。

摘　　要：[目的]智能避碰决策作为船舶安全航行的关键技术之一,对智能船舶的发展具有重要意义。针对多船会遇下的智能避碰决策问题,提出一种基于高斯变异和Tent混沌的改进麻雀搜索优化算法(CSSOA)。[方法]算法采用Tent混沌映射初始化麻雀原始种群,提高其多样性,并对适应能力差和搜索停滞的麻雀个体进行混沌映射,利用高斯变异提升局部搜索能力和鲁棒性,改进方案优化启发式算法收敛速度慢和易陷入局部最优的问题。综合考虑船舶间船速比、最小会遇距离、相对距离、最小会遇时间、相对方位等因素,利用模糊隶属度函数建立船舶碰撞风险模型,并通过多船典型会遇场景进行实例验证。[结果]实验结果显示,改进算法的平均迭代次数较粒子群算法和原麻雀算法分别减少了77.97%和53.57%。[结论]改进后的麻雀优化算法能以更优的收敛速度寻到安全经济的避碰路径,为船舶驾驶员提供避碰决策参考。[Objective]As one of the key technologies for the safe navigation of ships,intelligent collision avoidance decision-making is of great significance for the development of intelligent ships.Aiming at the intelligent collision avoidance decision-making problem under multi-vessel encounters,an improved chaos sparrow search optimization algorithm(CSSOA)based on Gaussian variation and Tent chaos is proposed.[Methods]The algorithm uses Tent chaotic mapping to initialize the original sparrow population and improve its diversity,chaotic mapping is applied to sparrows with poor adaptability and stagnant search ability,and Gaussian mutation is used to improve the local search ability and robustness.The improved scheme optimizes the problems of heuristic algorithms such as slow convergence speed and tendency to fall into the local optimum.A collision risk model is established using the fuzzy membership function with the comprehensive consideration of the ship-to-ship speed ratio,minimum encounter distance,relative distance,minimum encounter time and relative orientation.[Results]In a typical encounter scenario involving multiple ships,the experimental results demonstrate that the average number of iterations for the improved algorithm is reduced by 77.97%and 53.57%compared to particle swarm optimization and the original sparrow algorithm respectively.[Conclusion]The improved CSSOA can achieve a safer and more efficient collision avoidance path at a superior convergence speed,providing valuable guidance for ship navigators in making collision avoidance decisions.

关 键 词：多船智能避碰决策 碰撞危险度模型 改进麻雀算法 避碰目标函数 

分 类 号：U675.96[交通运输工程—船舶及航道工程]