基于改进粒子群算法的海流环境下无人水面艇路径规划  

Path planning of unmanned surface vessels in ocean current environment based on improved particle swarm optimization algorithm

作　　者：白响恩[1] 孙广志 徐笑锋 BAI Xiang’en;SUN Guangzhi;XU Xiaofeng(Merchant Marine College,Shanghai Maritime University,Shanghai 201306,China)

机构地区：[1]上海海事大学商船学院,上海201306

出　　处：《上海海事大学学报》2023年第4期1-7,共7页Journal of Shanghai Maritime University

基　　金：国家自然科学基金(42176217);上海高水平地方高校创新团队(海事安全与保障)项目。

摘　　要：针对海流环境下无人水面艇(unmanned surface vessel,USV)的多目标路径规划问题,构建USV的多目标路径规划模型,并提出一种改进的粒子群(particle swarm optimization,PSO)算法。采用自适应惯性权重来平衡算法的全局和局部搜索能力,避免算法过早收敛;引入精英反向学习策略提升算法跳出局部最优解的能力。仿真结果表明,改进的算法具有更好的寻优能力和鲁棒性,能够有效解决海流环境下USV的路径规划问题。For the multi-objective path planning of unmanned surface vessels(USVs)in the ocean current environment,a multi-objective path planning model of USVs is constructed,and an improved particle swarm optimization(PSO)algorithm is proposed.The adaptive inertia weight is adopted to balance the global and local search ability of the algorithm,and the premature convergence of the algorithm is avoided;the elite opposition-based learning strategy is introduced to improve the ability of the algorithm to jump out of the local optimal solution.The simulation results show that,the improved algorithm is of better optimization ability and robustness,and can effectively solve the path planning problem of USVs in the ocean current environment.

关 键 词：无人水面艇(USV) 路径规划 自适应方法 精英反向学习 粒子群(PSO)算法 

分 类 号：U664.82[交通运输工程—船舶及航道工程] U675.7[交通运输工程—船舶与海洋工程]