基于BAS-PSO算法的机器人定位精度提升

Improvement of robot kinematic accuracy based on BAS-PSO algorithm

鉴于工业机器人的精度性能无法满足高端制造领域的要求,研究了机器人定位精度提升方法,阐述了基于位姿微分变换的运动学误差模型和基于坐标误差传递的运动学误差模型的构建方法,提出了一种基于BAS-PSO算法的运动学参数辨识方法,并通过实验对比分析了不同运动学误差模型的精度。实验结果表明,基于BAS-PSO算法辨识后TX60机器人的平均综合位置/姿态误差分别从(0.312 mm,0.221°)降低为(0.0938 mm,0.0442°);而基于正运动学模型直接辨识后机器人的平均位置误差和平均姿态误差分别为0.0975 mm和0.0986°。本文提出的BAS-PSO算法具有较好的辨识精度和收敛速度,直接利用正运动学模型辨识的机器人运动学参数具有更好的辨识稳定性和精度。

In this study,a method to improve robot positioning accuracy was proposed,given that the ac⁃curacy performance of industrial robots still does not satisfy the requirements of high-end manufacturing.First,a kinematics error model based on pose differential transformation and kinematics error model based on coordinate error transformation were summarized.Second,a kinematic parameters identification meth⁃od based on BAS-PSO algorithm was proposed.Finally,accuracy characteristics of different error models were compared and analyzed via experiments.The experimental results indicate that the average compre⁃hensive position/attitude error of the TX60 robot,after identification by the proposed algorithm,decreas⁃es from(0.312 mm,0.221°)to(0.0938 mm,0.0442°).The average position error and average atti⁃tude error of the robot after direct identification based on the forward kinematics model correspond to 0.0975 mm and 0.0986°,respectively.The BAS-PSO algorithm proposed in the study displays good performance in terms of identification accuracy and convergence speed.Furthermore,robot kinematic parameters directly identified by the forward kinematics model exhibit better identification stability and accuracy.