工业机器人BAS-PSO运动参数识别方法及实验分析
Identification Method and Experimental Analysis of Motion Parameters of Industrial Robot BAS-PSO
王启晗 1李冲1
作者信息
- 1. 新乡职业技术学院,智能制造学院,河南 新乡 453006
- 折叠
摘要
对工业机器人进行定位校准可以有效提高控制精确度,以保证工业整体运行的高效率运行.为了进一步提高工业机器人定位识别能力,设计了一种工业机器人天牛触角定位策略(BAS)-粒子群优化算法(PSO)运动参数识别方法.天牛个体速度取决于左/右须函数,为确保此项达到均衡的精度与收敛速率,采用步长调节的方式进行粗略搜索,进行精确搜索.开展实验测试分析,研究结果表明:相比较单独的PSO与BAS两种算法,BAS-PSO算法收敛效率明显是最优的,泛化性能方面表现出了显著优势,坐标误差传递模型行识别工业机器人平均位置误差在0.1mm以内.该研究有助于提高工业机器人定位精度,高效率的提高智能制造领域的辅助能力.
Abstract
Positioning and calibration of industrial robots can effectively improve the control accuracy to ensure the efficient operation of the overall industrial operation.In order to improve the position recognition ability of industrial robots,a motion parameter recognition method based on BAS and PSO was designed.The individual speed of the beetle depends on the left/right function.In order to ensure that the accuracy and convergence rate of the balance can be achieved,the step size adjustment method is used for rough search and precise search.The experimental test and analysis were carried out,and the research results showed that,compared with the two algorithms of PSO and BAS alone,the convergence efficiency of the BAS-PSO algorithm was obviously the best,and the generalization performance showed significant advantages,and the average position error of the coordinate error transfer model was less than 0.1 mm.The research is helpful to improve the positioning accuracy of industrial robots and improve the auxiliary ability of intelligent manufacturing field efficiently.
关键词
工业机器人/几何参数/天牛须搜索算法/工标定/精度性能Key words
industrial robot/geometric parameters/Longox whisker search algorithm/work calibration/precision performance引用本文复制引用
出版年
2025
NETL
NSTL
万方数据