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An active learning hybrid reliability method for positioning accuracy of industrial robots

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Abstract

Popsitioning accuracy is an important index for evaluating the capacity of industrial robots. As a mechanism with multi-degree of freedom, the uncertainties of industrial robots are diverse and analyzing the positioning accuracy reliability is time consuming. To improve computation efficiency, a new active learning method based on Kriging model is proposed for hybrid reliability analysis of positioning accuracy with random and interval variables. In this study, the updated samples were selected through U learning function in the vicinity of limit-state function. A new stopping criterion based on expected risk function was exploited to judge whether the accuracy of Kriging model is enough. Two numerical examples and one engineering example were provided to verify the efficiency and accuracy of the proposed method. The results indicate that the proposed method is accurate and efficient.

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Acknowledgments

This work was financially supported by the National Key R&D Program of China (Grant No. 2017YFB1301300), the National Natural Science Foundation of China (Grant No. 51905146), the Key R&D Plan Program of Hebei Province (Grant No. 19211808D) and the Research Program of Education Bureau of Hebei Province (Grant No. QN2019141).

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Authors and Affiliations

  1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China

    Dequan Zhang, Song Liu, Jinhui Wu & Yimin Wu

  2. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China

    Jie Liu

Authors
  1. Dequan Zhang
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  2. Song Liu
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  3. Jinhui Wu
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  4. Yimin Wu
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  5. Jie Liu
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Correspondence to Dequan Zhang.

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Recommended by Editor Ja Choon Koo

Dequan Zhang is an Assistant Professor of Mechanical Engineering, Hebei University of Technology, Tianjin, China. He received the Ph.D. in Mechanical Engineering from Hunan University, Hunan, China, in 2018. His research interests are in the area of reliability analysis, design under uncertainty and robotics.

Song Liu is a master’s student in Mechanical Engineering, Hebei University of Technology, Tianjin, China. His research interests are in the area of reliability analysis and error compensation for industrial robot.

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Zhang, D., Liu, S., Wu, J. et al. An active learning hybrid reliability method for positioning accuracy of industrial robots. J Mech Sci Technol 34, 3363–3372 (2020). https://doi.org/10.1007/s12206-020-0729-8

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  • DOI: https://doi.org/10.1007/s12206-020-0729-8

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