摘要
针对粒子群优化算法在进行自主式水下机器人三维路径规划时,收敛精度低、收敛速度慢和易陷入局部最优等问题,提出了一种改进粒子群优化算法。提出的改进粒子群算法利用标准的粒子群2011(standard particle swarm optimization 2011,SPSO 2011)算法的速度和位置更新规则,引入自适应参数平衡局部和全局搜索能力,提高收敛精度;引入遗传算法中的多交叉算子和变异算子等进化算子以及改进位置更新策略来加快算法的收敛速度,同时避免算法陷入局部最优。该算法综合考虑路径长度、路径平滑性和路径安全性因素来建立路径规划算法的适应度函数。针对特定的航行环境,基于MATLAB平台进行系统仿真。仿真结果表明,提出的路径规划算法收敛速度更快,收敛精度更高,且不易陷入局部最优。
Aiming at the problems of the low convergence accuracy,slow convergence and easily falling into local optimal when performing three-dimensional path planning of autono⁃mous underwater vehicles,an improved particle swarm optimization algorithm is proposed.The proposed improved particle swarm algorithm uses the speed and position update rules of the standard particle swarm optimization 2011(SPSO 2011)algorithm to introduce adaptive parameters to balance local and global search capabilities and improve convergence accuracy.Evolutionary operators such as multi-cross operator and mutation operator in genetic algorithms and improved position update strategy are introduced to accelerate the convergence speed of the algorithm and avoid the algorithm falling into local optimum.The proposed algorithm compre⁃hensively considers the path length,path smoothness,and path security to establish the fitness function of the path planning algorithm.For the specific navigation environments,system simu⁃lation is based on the MATLAB platform.The simulation results show that the proposed path planning algorithm has faster convergence speed,higher convergence accuracy,and is not easy to fall into local optimum.
作者
展邦顺
安顺
何燕
王龙金
ZHAN Bangshun;AN Shun;HE Yan;WANG Longjin(College of Electromechanical Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)
出处
《青岛科技大学学报(自然科学版)》
2026年第1期134-139,共6页
Journal of Qingdao University of Science and Technology(Natural Science Edition)
基金
国防重点实验室建设项目(JCKYS2021SXJQR-02).
关键词
自主式水下机器人
三维路径规划
改进SPSO
2011算法
自适应参数
进化算子
改进位置更新策略
AUV
three-dimensional path planning
improved spso 2011 algorithm
adaptive parameters
evolutionary operator
modified position update strategy自主式水下机器人(autonomous underwater
