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A novel cascade calibration method for robotic grinding system

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Abstract

This paper presents an efficient cascade calibration method with an improved Levenberg–Marquardt and sine–cosine hybrid algorithm to enhance the absolute positioning accuracy of robotic grinding systems. To expedite convergence in the Levenberg–Marquardt algorithm, a dynamic adaptive weight mechanism is introduced, enhancing global and local search capabilities. Furthermore, a novel learning rate, combining exponential and cosine functions, addresses local optima in the algorithm. The improved Levenberg–Marquardt algorithm is employed to obtain suboptimal values for robot kinematic parameter deviations. Subsequently, these values are used as central points for generating a candidate solution set in the sine–cosine algorithm, resulting in more accurate kinematic parameter deviation identification. This innovative dual-search optimization approach combines the two algorithms. Experimental results confirm the substantial improvements in absolute positioning accuracy and surface machining precision achieved by the proposed model, with the calibration method’s effectiveness verified through experimentation.

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References

  1. Deng Y, Hou X, Li B, Wang J, Zhang Y (2024) A highly powerful calibration method for robotic smoothing system calibration via using adaptive residual extended Kalman filter. Robot Comput Integr Manuf 86:102660

    Article  Google Scholar 

  2. Li Z, Li S, Luo X (2021) An overview of calibration technology of industrial robots. IEEE/CAA J Automatica Sinica 8(1):23–36

    Article  Google Scholar 

  3. Xie Z, Zong P, Yao P, Ren P (2019) Calibration of 6-DOF industrial robots based on line structured light. Optik 183:1166–1178

    Article  Google Scholar 

  4. Gan Y, Duan J, Dai X (2019) A calibration method of robot kinematic parameters by drawstring displacement sensor. Int J Adv Rob Syst 16(5):1–9

    Google Scholar 

  5. Jiang Z, Zhou W, Li H, Mo Y, Ni W, Huang Q (2018) A new kind of accurate calibration method for robotic kinematic parameters based on the extended Kalman and particle filter algorithm. IEEE Trans Industr Electron 65(4):3337–3345

    Article  Google Scholar 

  6. Ma L, Bazzoli P, Sammons P, Landers R, Bristow D (2018) Modeling and calibration of high-order joint-dependent kinematic errors for industrial robots. Robot Comput Integr Manuf 50:153–167

    Article  Google Scholar 

  7. Sun T, Lian B, Zhang J, Song Y (2018) Kinematic calibration of a 2-DoF over-constrained parallel mechanism using real inverse kinematics. IEEE Access 6:67752–67761

    Article  Google Scholar 

  8. Nonoyama K, Liu Z, Fujiwara T, Alam M, Nishi T (2022) Energy-efficient robot configuration and motion planning using genetic algorithm and particle swarm optimization. Energies 15(6):2074

    Article  Google Scholar 

  9. Cao H, Nguyen H, Tran T, Tran H, Jeon J (2022) A robot calibration method using a neural network based on a butterfly and flower pollination algorithm. IEEE Trans Ind Electron 69(4):3865–3875

    Article  Google Scholar 

  10. Wu G, Shi G (2019) Experimental statics calibration of a multi-constraint parallel continuum robot. Mech Mach Theory 136:72–85

    Article  Google Scholar 

  11. Li Z, Li S, Bamasag O, Alhothali A, Luo X (2022) Diversified regularization enhanced training for effective manipulator calibration. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2022.3153039

    Article  Google Scholar 

  12. Zhao H, Yu L, Jia H, Li W, Sun J (2016) A new kinematic model of portable articulated coordinate measuring machine. Appl Sci 6(7):181

    Article  Google Scholar 

  13. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Article  Google Scholar 

  14. Zhang J, Wang X, Wen K, Zhou Y, Yue Y, Yang J (2018) A simple and rapid calibration methodology for industrial robot based on kinematic constraint and two-step error. Ind Robot Int J 45:715–721

    Article  Google Scholar 

  15. Deng Y, Hou X, Li B, Wang J, Zhang Y (2024) 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

    Article  Google Scholar 

  16. Wu L, Crawford R, Roberts J (2017) An analytic approach to converting POE parameters into D–H parameters for serial-link robots. IEEE Robot Autom Lett 2(4):2174–2179

    Article  Google Scholar 

  17. Deng Y, Hou X, Li B, Wang J, Zhang Y (2023) A novel method for improving workpiece component smoothing quality in robotic smoothing systems by compensating path errors. Opt Express 31(19):30359–30378

    Article  Google Scholar 

  18. Xu P, Cheung B, Li B (2019) A complete, continuous, and minimal product of exponentials-based model for five-axis machine tools calibration with a single laser tracker, an R-test, or a double ball-bar. J Manuf Sci Eng 141(4):041010

    Article  Google Scholar 

  19. Huang B, Ma C (2019) A Shamanskii-like self-adaptive Levenberg–Marquardtt method for nonlinear equations. Comput Math Appl 77(2):357–373

    Article  MathSciNet  Google Scholar 

  20. Nielsen H (1999) Damping parameter in Marquardtt’s method. In: Informatics and mathematical modelling. Technical University of Denmark, DTU, pp 1–31

  21. Park I, Lee B, Cho S, Hong Y, Kim J (2012) Laser-based kinematic calibration of robot manipulator using differential kinematics. IEEE-ASME Trans Mechatron 17(6):1059–1067

    Article  Google Scholar 

  22. Deng X, Ge L, Li R, Liu Z (2020) Research on the kinematic parameter calibration method of industrial robot based on LM and PF algorithm. In: Proceedings of Chinese control and decision conference (CCDC), pp 2198–2203

  23. Shang M, Luo X, Liu Z, Chen J, Yuan Y, Zhou M (2019) Randomized latent factor model for high-dimensional and sparse matrices from industrial applications. IEEE/CAA J Automatica Sinica 6(1):131–141

    Article  MathSciNet  Google Scholar 

  24. Deng Y, Hou X, Li B, Wang J, Zhang Y (2023) Review on mid-spatial frequency error suppression in workpiece components manufacturing. Int J Adv Manuf Technol 126:4827–4847

    Article  Google Scholar 

  25. Wan S, Zhang X, Xu M, Wang W, Jiang X (2018) Region-adaptive path planning for precision workpiece polishing with industrial robots. Opt Express 26:23782–23795

    Article  Google Scholar 

  26. Li W, Xie H, Zhang G, Yan S, Yin Z (2015) Hand-eye calibration in visually-guided robot grinding. IEEE Trans Cybern 46(11):2634–2642

    Article  Google Scholar 

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Acknowledgements

This research was funded by the Collaborative Education Program of the Ministry of Education of China (202102145008, 202102145013), Innovation Fund for Industry-university Research of Chinese Universities (No. 2020ITA03041), National Natural Science Foundation of China (62101076); Sichuan Provincial Youth Science Foundation (2022NSFSC0920) and the Talents Start-up Project of Scientific Research in Chengdu University of Information Technology (KYTZ202102, 376157). The Quality of Personnel Training and Reform of Education & Teaching Program of Chengdu Technological University (No. 20210205), The Science and Technology Service Corps Program of Chengdu Technological University (No. 2023FW039).

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Authors and Affiliations

  1. School of Economics and Management, Chengdu Technological University, Chengdu, 611730, Sichuan, China

    Jian Liu

  2. Sichuan Institute of Industrial Big-Data Applications, Chengdu, 611730, China

    Jian Liu, Yonghong Deng & Yulin Liu

  3. Department of Mechanical and Electrical Engineering, Liaocheng University Dongchang College, Liaocheng, 252000, China

    Dong Li

  4. School of Software Engineering, Chengdu University of Information Technology, Chengdu, 610225, China

    Yonghong Deng, Linlin Chen, Peiyang Wei & Zhibin Li

  5. College of Communication Engineering, Chengdu University of Information Technology, Chengdu, 610225, China

    Zhenzen Hu

  6. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Peiyang Wei

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  1. Jian Liu
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  2. Yonghong Deng
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  3. Yulin Liu
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  4. Dong Li
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  5. Linlin Chen
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  6. Zhenzen Hu
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  7. Peiyang Wei
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  8. Zhibin Li
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Correspondence to Yonghong Deng or Zhenzen Hu.

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Liu, J., Deng, Y., Liu, Y. et al. A novel cascade calibration method for robotic grinding system. Intel Serv Robotics (2024). https://doi.org/10.1007/s11370-024-00534-5

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