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A Novel Positioning Accuracy Improvement Method for Polishing Robot Based on Levenberg–Marquardt and Opposition-based Learning Squirrel Search Algorithm

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Abstract

Achieving high-precision manufacturing of optical components requires improving the absolute positioning accuracy of the robot to the highest possible level. Identifying the robot's kinematic parameters and compensating for kinematic errors are effective methods for improving the robot's positioning accuracy. This paper proposes a hybrid algorithm that combines the Levenberg–Marquardt algorithm and an opposition-based learning squirrel search algorithm to identify the kinematic parameters of a polishing robot. Firstly, the Levenberg–Marquardt algorithm is utilized to solve the suboptimal values of kinematic parameter deviations. Secondly, an opposition-based learning strategy is integrated into the standard squirrel search algorithm to increase the diversity of the population and prevent the population from getting stuck in local optima. The suboptimal values obtained by the Levenberg–Marquardt algorithm are subsequently used as the central values to generate the initial population for the opposition-based learning squirrel search algorithm, which helps identify more accurate kinematic parameter deviations. Ultimately, the kinematic parameters of the robot are effectively calibration. The calibration experimental results showed that the proposed method achieved a high level of calibration accuracy, resulting in a 62.61% improvement in absolute positioning error compared to before calibration. Offline machining experiments have validated the effectiveness of LM-OBLSSA in reducing deviations in the dwell points of optical components during the machining process.

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Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Acknowledgements

Special thanks to the technical staff in the laboratory for their support and provision of equipment. Their expertise and assistance were instrumental in the smooth execution of experiments and data analysis.

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Authors and Affiliations

  1. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, 610209, China

    Yonghong Deng, Xi Hou, Jia Wang & Yun Zhang

  2. School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Yonghong Deng & Bincheng Li

  3. National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, 610209, China

    Yonghong Deng, Xi Hou, Jia Wang & Yun Zhang

  4. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yonghong Deng, Xi Hou, Jia Wang & Yun Zhang

Authors
  1. Yonghong Deng
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  2. Xi Hou
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  3. Bincheng Li
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  4. Jia Wang
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  5. Yun Zhang
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Contributions

All authors contributed to the study conception and design. Yonghong Deng performed material preparation, data collection, and analysis, and wrote the first draft of the manuscript. Jia Wang offered revision and valuable suggestions. Yun Zhang contributed to revision and provided some valuable suggestions. Bincheng Li provided guidance. Xi Hou provided guidance, and final revision. All authors commented on previous versions of the manuscript, and all authors read and approved the final manuscript.

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Correspondence to Xi Hou.

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Deng, Y., Hou, X., Li, B. et al. A Novel Positioning Accuracy Improvement Method for Polishing Robot Based on Levenberg–Marquardt and Opposition-based Learning Squirrel Search Algorithm. J Intell Robot Syst 110, 8 (2024). https://doi.org/10.1007/s10846-023-02038-3

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