摘要
针对传统人工鱼群算法(AFSA)在解决工业机器人标定过程中存在的收敛速度慢、容易陷入局部寻优、得到的结果不稳定的问题,用变步长自适应的人工鱼群算法(SA-IAFSA)来优化参数辨识过程。对算法的目标函数进行改进,用MD-H方法建立了机器人误差模型,将几何误差辨识问题转换为高维非线性方程。实验对比了AFSA与SA-IAFSA的参数标定结果,结果表明:采用SA-IAFSA的辨识效果优于AFSA,同时加快了收敛速度,能获得较为准确的参数误差值,可使机器人绝对定位精度提升38.96%。
The traditional artificial fish school algorithm(AFSA)has the problems of slow convergence speed,easy to fall into local optimization and unstable results in the process of industrial robot calibration.To solve this problem,the variable step size adaptive artificial fish school algorithm(SA-IAFSA)is used to optimize the parameter identification process.The objective function of the algorithm is improved,the robot error model is established by the MD-H method,and the geometric error identification problem is converted into a high-dimensional nonlinear equation.The experiment compares the parameter calibration results of AFSA and SA-IAFSA,and the results show that the identification effect of SA-IAFSA is better than AFSA,and at the same time it speeds up the convergence speed,can obtain more accurate parameter error values,and can improve the absolute positioning accuracy of the robot by 38.96%.
作者
赵铁军
杨伟林
ZHAO Tie-jun;YANG Wei-lin(School of Mechanical Engineering,Shenyang University of Technology,Shenyang 110870,China)
出处
《机械工程与自动化》
2021年第5期166-168,共3页
Mechanical Engineering & Automation
关键词
机器人标定
改进鱼群算法
几何误差
robot calibration
improved fish school algorithm
geometric error
作者简介
赵铁军(1967-),男,辽宁沈阳人,教授,博士,主要研究方向:机器人技术及运用、等离子特种加工技术。
