• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2018, Vol. 54 ›› Issue (17): 142-148.doi: 10.3901/JME.2018.17.142

• 特邀专栏:智能制造装备 • 上一篇    下一篇

面向机器人砂带打磨的加权手眼标定算法

张铁1, 叶景杨1, 刘晓刚2   

  1. 1. 华南理工大学机械与汽车工程学院 广州 510640;
    2. 桂林航天工业学院广西高校机器人与焊接重点实验室 桂林 541004
  • 收稿日期:2017-10-11 修回日期:2018-02-09 出版日期:2018-09-05 发布日期:2018-09-05
  • 通讯作者: 张铁(通信作者),男,1968年出生,博士,教授,博士研究生导师。主要研究方向为工业机器人。E-mail:merobot@scut.edu.cn
  • 基金资助:
    国家科技重大专项(2015ZX04005006)、广东省科技重大专项(2014B090921004,2014B090920001)、广州市科技重大项目(201604040009)和广西高校机器人与焊接重点实验室课题基金(JQR2015KF02)资助项目。

Weighted Hand-eye Calibration Algorithm for Robot Grinding

ZHANG Tie1, YE Jingyang1, LIU Xiaogang2   

  1. 1. Institute of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640;
    2. Guangxi Key Laboratory of Robotics and Welding, Guilin University of Aerospace Technology, Guilin, 541004
  • Received:2017-10-11 Revised:2018-02-09 Online:2018-09-05 Published:2018-09-05

摘要: 由于砂带打磨工序中往往存在外部因素干扰,因此研究抗干扰能力强的手眼标定算法是机器人砂带打磨系统的关键问题之一。通过分析机器人打磨系统中的手眼标定数学模型,并利用带尖点的标定工具快速获取测量数据,在原有奇异值分解算法的基础上,提出基于加权的改进方案,在初次计算手眼关系后计算出各个测量数据的误差,并根据测量数据误差的大小重新分配其对应的权重,最后重新计算手眼关系。通过仿真测试可知,改进后的算法较原有算法对测量数据的误差敏感度降低,标定精度稳定性好。通过试验验证可知,改进后的算法较原有算法平均标定误差比SVD算法减少了45.9%,最大误差减少了24.4%,从而验证了改进算法的抗干扰性能,并通过实际打磨测试证实了改进算法更适用于机器人打磨系统手眼标定。

关键词: 机器人打磨, 加权, 奇异值分解, 手眼标定

Abstract: As the external interference factor in the belt grinding environment, it is one of the key problems to study the anti-jamming ability of hand-eye calibration algorithm in the robot belt grinding system. By analyzing the mathematical model of hand-eye calibration in the robot grinding system and using the calibration tool, the measurement data are obtained quickly. Based on the original singular value decomposition algorithm, an improved scheme based on weight is proposed. After calculating the initial hand-eye relationship and the calibration error for each measurement data and according to the size of the measurement data error to reallocate its corresponding weight, and the hand-eye relationship would be recalculated finally. Through the simulation test, the improved algorithm is less sensitive than the original algorithm to the measurement data, and the calibration accuracy is good. The experimental results show that the improved algorithm is 45.9% less than the original algorithm, and the maximum error is reduced by 24.4% compared with the original algorithm. The improved algorithm is proved by the actual grinding test, and is more suitable for the robot grinding system.

Key words: hand-eye calibration, robot grinding, singular value decomposition, weighted

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