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Kinematics Parameter Calibration of Serial Industrial Robots Based on Partial Pose Measurement

Author

Listed:
  • Tiewu Xiang

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Xinyi Jiang

    (School of Automation, Nanjing Institute of Technology, Nanjing 210096, China)

  • Guifang Qiao

    (School of Automation, Nanjing Institute of Technology, Nanjing 210096, China)

  • Chunhui Gao

    (School of Automation, Nanjing Institute of Technology, Nanjing 210096, China)

  • Hongfu Zuo

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

The kinematics parameter error is the main error factor that affects the absolute accuracy of industrial robots. The absolute accuracy of industrial robots can be effectively improved through kinematics calibration. The error model-based method is one of the main methods for calibrating the kinematics parameter error. This paper presents a kinematics parameter calibration method for serial industrial robots based on partial pose measurement. Firstly, the kinematics and the pose error models have been established based on the modified Denavit–Hartenberg (MDH) model. By introducing the concept of error sensitivity, the average significance index is proposed to quantitatively analyze the effects of the kinematics parameter error on the pose error of a robot. The results show that there is no need to measure the full pose error of the robot. Secondly, a partial pose measurement device and method have been presented. The proposed device can measure the position error and the attitude error on the x -axis or y -axis. Finally, the full pose error model, the NP-type partial pose error model, and the OP-type partial pose error model have been applied for calibrating the kinematics parameter errors. The experimental results show that the effectiveness of the OP-type partial pose error model is consistent with the full pose error model.

Suggested Citation

  • Tiewu Xiang & Xinyi Jiang & Guifang Qiao & Chunhui Gao & Hongfu Zuo, 2023. "Kinematics Parameter Calibration of Serial Industrial Robots Based on Partial Pose Measurement," Mathematics, MDPI, vol. 11(23), pages 1-18, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:23:p:4802-:d:1289461
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    References listed on IDEAS

    as
    1. Cozmin Cristoiu & Mario Ivan & Ionuţ Gabriel Ghionea & Cristina Pupăză, 2023. "The Importance of Embedding a General forward Kinematic Model for Industrial Robots with Serial Architecture in Order to Compensate for Positioning Errors," Mathematics, MDPI, vol. 11(10), pages 1-28, May.
    2. Gongdan Xu & Zhiwei Zhang & Zhiwu Li & Xiwang Guo & Liang Qi & Xianzhao Liu, 2023. "Multi-Objective Discrete Brainstorming Optimizer to Solve the Stochastic Multiple-Product Robotic Disassembly Line Balancing Problem Subject to Disassembly Failures," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    3. Rui Wang & Xiangyu Guo & Songmo Li & Lin Wang, 2023. "Separation and Calibration Method of Structural Parameters of 6R Tandem Robotic Arm Based on Binocular Vision," Mathematics, MDPI, vol. 11(11), pages 1-24, May.
    4. Xiulan Wen & Shun He & GuiFang Qiao & Dongxia Wang & Aiguo Song & ChuanShuai Kang & Zhongyan Lv, 2019. "Uncertainty Estimation of Robot Geometric Parameters and End-Effecter Position Based on New Generation GPS," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-11, June.
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