Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF Robot

Ping Rui; Guifang Qiao; Xiulan Wen; Ying Zhang; Dongxia Wang

doi:10.16578/j.issn.1004.2539.2019.06.007

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Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF Robot

Theory·Reserach | 更新时间：2022-10-01

- Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF Robot
- Journal of Mechanical Transmission Vol. 43, Issue 6, Pages: 37-42(2019)
- 作者机构：
  
  1.南京工程学院自动化学院，江苏南京 210096
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2019.06.007
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芮平,乔贵方,温秀兰等.串联6自由度机器人关节刚度辨识与误差补偿研究[J].机械传动,2019,43(06):37-42.

Rui Ping,Qiao Guifang,Wen Xiulan,et al.Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF Robot[J].Journal of Mechanical Transmission,2019,43(06):37-42.
芮平,乔贵方,温秀兰等.串联6自由度机器人关节刚度辨识与误差补偿研究[J].机械传动,2019,43(06):37-42. DOI： 10.16578/j.issn.1004.2539.2019.06.007.

Rui Ping,Qiao Guifang,Wen Xiulan,et al.Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF Robot[J].Journal of Mechanical Transmission,2019,43(06):37-42. DOI： 10.16578/j.issn.1004.2539.2019.06.007.

摘要

为提高串联6自由度机器人的绝对定位精度，针对几何参数误差补偿后的工业机器人关节刚度参数展开研究。首先，基于虚拟关节模型建立了工业机器人一维关节刚度误差模型。其次，为提高关节刚度参数的辨识精度与效率，利用BP神经网络对刚度误差模型进行拟合，以优化遗传算法的初始种群适应度。最后，利用激光跟踪仪AT930和ER10L-C10机器人进行实验，验证以上误差模型与关节刚度参数辨识算法。实验结果表明，经过关节刚度误差补偿后，机器人的平均距离误差与最大距离误差分别为0.248 5 mm与0.333 2 mm。相比于补偿前的距离误差，机器人定位精度提高了33.7%。因此，通过改进遗传算法辨识得到的机器人关节刚度参数能够有效地提高机器人定位精度。

Abstract

To improve the absolute positional accuracy of the serial six-DOF robot， the joint stiffness error of industrial robots after geometric parameter error compensation is studied. Firstly，the one-dimensional joint stiffness error model of industrial robots is established based on the virtual joint model. Secondly，in order to improve the identification accuracy and efficiency of joint stiffness parameters, the BP neural network is applied to fit the stiffness error model to optimize the initial population fitness of genetic algorithm. Finally，the laser tracker AT930 and ER10L-C10 robot are used to verify the above error model and joint stiffness parameter identification algorithm. The experimental results show that the average distance error and maximum distance error of the robot are 0.248 5 mm and 0.333 2 mm respectively after the joint stiffness error compensation. Compared with the distance error before error compensation，the positional accuracy of robot is improved by 33.7%. Therefore，through the proposed improved genetic algorithm can identify the joint stiffness parameters accurately and improve the positional accuracy effectively.

关键词

工业机器人参数标定关节刚度误差补偿遗传算法

Keywords

Industrial robotParameter calibrationJoint stiffnessError compensationGenetic algorithm

references

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