摘要
针对机器人运动中产生的误差的问题,提出了一种基于K-PSO算法的机器人运动学参数标定方法,该方法用于机器人参数标定中可对误差进行弥补,大大提高机器人的运动精度。K-PSO算法结合了K均值聚类和粒子群优化的思想,利用K均值聚类算法对机器人的运动学参数初始粒子进行分组,然后通过改进的粒子群优化算法对参数进行优化。实验结果表明,改进K-PSO方法能够有效地减少计算复杂度,并且具有更快的收敛速度和较高的参数估计精度,该方法对于机器人的精确控制和运动规划具有重要意义。
Aiming at the error problem caused by robot movement,a robot kinematic parameter calibration method based on the K-PSO algorithm is proposed.This method can be used to compensate for errors in robot parameter calibration and greatly improve the robot′s movement accuracy.The K-PSO algorithm combines the ideas of K-means clustering and particle swarm optimization.Firstly,the K-means clustering algorithm is used to group the initial particles of the robot′s kinematic parameters,then the parameters are optimized through an improved particle swarm optimization algorithm.Experimental results show that the improved K-PSO method can effectively reduce computational complexity,and has faster convergence speed and higher parameter estimation accuracy.This method holds significant implications for the precise control and motion planning of robots.
作者
郭朴
胡晓兵
李航
毛业兵
陈海军
GUO Pu;HU Xiaobing;LI Hang;MAO Yebing;CHEN Haijun(School of Mechanical Engineering,Sichuan University,Chengdu 610065,China;Industrial Technology Research Institute,Yibin Sichuan University,Yibin 644600,China)
出处
《组合机床与自动化加工技术》
北大核心
2024年第7期69-73,共5页
Modular Machine Tool & Automatic Manufacturing Technique
基金
四川省科技计划项目(2022ZHCG0049)
川大-宜宾校市战略合作项目(2020CDYB-3)。
