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An automatic calibration algorithm for laser vision sensor in robotic autonomous welding system

Author

Listed:
  • Runquan Xiao

    (Shanghai Jiao Tong University)

  • Yanling Xu

    (Shanghai Jiao Tong University)

  • Zhen Hou

    (Shanghai Jiao Tong University)

  • Chao Chen

    (Shanghai Jiao Tong University)

  • Shanben Chen

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

Abstract

Visual sensor plays an important part in intelligentized welding systems, and the calibration of the vision sensor is the indispensable part of visual systems. Aiming at the problem of the tedious calibration process, this paper describes an automatic calibration algorithm. First, the robot motion equation and the motion range constraint equation are proposed to ensure that the collected images of calibration grid and laser line meet the calibration requirements. Based on these two equations, the automatic collection procedure can be realized. Second, the simplified visual servoing method and the Extended Kalman filter were used to adjust the images and rectify system parameters, respectively, which will improve the stability of the calibration motion. Third, to reduce the impact of the complex welding environments, a robustness feature extraction algorithm based on local threshold is studied. And then, the laser plane and hand-eye matrix are fitted with optimization algorithms to ensure calibration accuracy. Finally, the simulation experiments prove the feasibility and stability of the proposed algorithm. And the actual calibration tests suggest that the algorithm can significantly improve calibration efficiency. Moreover, the experimental results of welding guidance and seam tracking confirm that the calibration precision has met the welding requirement.

Suggested Citation

  • Runquan Xiao & Yanling Xu & Zhen Hou & Chao Chen & Shanben Chen, 2022. "An automatic calibration algorithm for laser vision sensor in robotic autonomous welding system," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1419-1432, June.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:5:d:10.1007_s10845-020-01726-3
    DOI: 10.1007/s10845-020-01726-3
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    References listed on IDEAS

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    1. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
    2. Zhifen Zhang & Shanben Chen, 2017. "Real-time seam penetration identification in arc welding based on fusion of sound, voltage and spectrum signals," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 207-218, January.
    3. Guiqian Liu & Xiangdong Gao & Deyong You & Nanfeng Zhang, 2019. "Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 821-832, February.
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