Skip to main content
SpringerLink
Log in

Joint module angle error analysis and modelling of self-driven articulated arm coordinate measuring machine

  • Original Article
  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

The self-driven articulated arm coordinate measuring machine (self-driven AACMM) is a new type of flexible coordinate measuring equipment. The integrated joint module is introduced to the AACMM joint for self-driven control and automatic measurement, resulting in the joint angle error of self-driven AACMM. In this study, an ideal measurement model of the self-driven AACMM have been established. The sources of angle error of joint module is analysed, and single and comprehensive models of the joint module’s angle error are established. Numerical simulation of the angle error model of the single joint module is conducted by MATLAB. An angle error calibration experiment of the joint module is carried out with the photoelectric autocollimator and the metal 36-sided prism. Results show that each joint module produces different torsional deformation due to load. The angle error of joint module 1, 6 are most and least affected by the load torque, the actual average errors of the two are 37.64 arcsec and −0.9632 arcsec, respectively. The simulated and calibrated single-joint module angle error trends are cyclical, and the calibrated angle error range is [−100.4 arcsec, 205.2 arcsec]. The harmonic error component in the harmonic reducer and the magnetic encoder is an important factor of the angle error of joint module. The eatablished angle error model of single joint module can be effiectively applied to comprehensive error compensation for high measurement accuracy of self-driven AACMM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. S. Lv et al., Design of mechatronics robot joint and its control system hardware, Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 33(1) (2021) 111–117.

    Google Scholar 

  2. L. Zhao, H. Yan and J. Yu, Modular industrial robot based on joint module, Modular Machine Tool and Automatic Manufacturing Technique, 9 (2008) 63–67.

    Google Scholar 

  3. Z. Yu, Y. Qiu and C. Li, Development of an integrated joint module of space robot and its application in the field of space detection, Academic Conference of the Space Exploration Professional Committee of the Chinese Space Science Society, Beihai (2003) 222–227.

  4. R. Liu, Error source analysis and modeling of self-driven AACMM, Master’s Thesis, Anhui University of Science and Technology, China (2020).

    Google Scholar 

  5. G. Du et al., A cognitive joint angle compensation system based on self-feedback fuzzy neural network with incremental learning, IEEE Transactions on Industrial Informatics, 17(4) (2021) 2928–2937.

    Article  Google Scholar 

  6. X. Luo, Y. Zhang and L. Zhang, Study of error compensations and sensitivity analysis for 6-dof serial robot, Engineering Computations, 38(4) (2020) 1851–1868.

    Article  Google Scholar 

  7. J. Zhang and J. Cai, Error analysis and compensation method of 6-axis industrial robot, International Journal on Smart Sensing and Intelligent Systems, 6(4) (2013) 1383–1399.

    Article  MathSciNet  Google Scholar 

  8. C. Wang et al., Research on improving the measurement accuracy of the AACMM based on indexing joint, Measurement Science and Technology, 32(11) (2021) 115011.

    Article  Google Scholar 

  9. D. Zheng et al., Measurement accuracy of articulated arm CMMs with circular grating eccentricity errors, Measurement Science and Technology, 27(11) (2016) 115011.

    Article  Google Scholar 

  10. H. Jia et al., A new method of angle measurement error analysis of rotary encoders, Applied Sciences, 9(16) (2019) 3415.

    Article  Google Scholar 

  11. H. Jia et al., Compensation of rotary encoders using fourier expansion-back propagation neural network optimized by genetic algorithm, Sensors, 20(2603) (2020) 2–14.

    Google Scholar 

  12. A. Olabi et al., Improving the accuracy of industrial robots by offline compensation of joints errors, 2012 IEEE International Conference on Industrial Technology (2012) 492–497.

  13. L. Cantelli et al., A joint-angle estimation method for industrial manipulators using inertial sensors, IEEE/ASME Transactions on Mechatronics, 20(5) (2015) 2486–2495.

    Article  Google Scholar 

  14. Y. Labbé et al., Single-view robot pose and joint angle estimation via render & compare, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2021) 1654–1663.

  15. L. Ma et al., Modeling and calibration of high-order joint-dependent kinematic errors for industrial robots, Robotics and Computer-Integrated Manufacturing, 50(2018) (2017) 153–167.

    Google Scholar 

  16. S. Khalaf et al., Robot control and kinematic analysis with 6DoF manipulator using direct kinematic method, Bulletin of Electrical Engineering and Informatics, 10(1) (2021) 70–78.

    Article  Google Scholar 

  17. H.-N. Nguyen, P.-N. Le and H.-J. Kang, A new calibration method for enhancing robot position accuracy by combining a robot model-based identification approach and an artificial neural network-based error compensation technique, Advances in Mechanical Engineering, 11(1) (2019) 1–11.

    Article  Google Scholar 

  18. Renishaw, LP2 Modular Probe System, https://www.renishaw.com/en/lp2-modular-probe-system—6750, Renishaw PLC (2021).

  19. J. Denavit and R. S. Hartenberg, A kinematic notation for lower-pair mechanisms based on matrices, Journal of Applied Mechanics, 21(5) (1995) 215–221.

    MATH  Google Scholar 

  20. P. Yang, Structural design and optimization of Self-driven articulated arm coordinate measuring machine, Master’s Thesis, Anhui University of Science and Technology, China (2020).

    Google Scholar 

  21. Mintasca Wiki, Technical Parameter of QDD Pro NE30-100-70, http://wiki.mintasca.com/wiki/cn/index.html#!index.md, Minda Intelligent Technology Co., Ltd. (2021).

  22. Y. Hu et al., An angle error compensation method based on harmonic analysis for integrated joint modules, Sensors, 20(6) (2020) 1715.

    Article  Google Scholar 

  23. Y. Shen and Q. Ye, Theory and Design of Harmonic Gear Transmission, 1st Ed., Machinery Industry Press Publication, Beijing (1985).

    Google Scholar 

  24. Y. Li et al., Performance margin modeling and reliability analysis for harmonic reducer considering multi-source uncertainties and wear, IEEE Access, 8 (2020).

  25. T. Feng et al. A new kind of absolute magnetic encoder, Sensors, 21(9) (2021) 3095.

    Article  Google Scholar 

  26. J. W. Park et al., A linear compensation method for improving the accuracy of an absolute multipolar magnetic encoder, IEEE Access, 9 (2021) 19127–19138.

    Article  Google Scholar 

  27. L. Wang et al., Study on high precision magnetic encoder based on the arctangent cross-intervals tabulation method, Australian Journal of Electrical and Electronics Engineering, 13(3) (2016) 167–173.

    Article  Google Scholar 

  28. J. Lara and T. Li, A novel algorithm based on polynomial approximations for an efficient error compensation of magnetic analog encoders in PMSMs for EVs, IEEE Transactions on Industrial Electronics, 63(6) (2016) 3377–3388.

    Article  Google Scholar 

  29. T. N.-C. Tran et al., Improving the accuracy of an absolute magnetic encoder by using harmonic rejection and a dual-phase-locked loop, IEEE Transactions on Industrial Electronics, 66(7) (2019) 5476–5486.

    Article  Google Scholar 

  30. T. Feng et al., Development of a combined magnetic encoder, Sensors Review, 36(4) (2020) 386–396.

    Article  Google Scholar 

  31. H. Li et al., High-precision angular speed tracking control of gimbal system with harmonic reducer, IEEE Transactions on Industrial Electronics, 69(8) (2022) 8168–8177.

    Article  Google Scholar 

  32. H. Hao, C. Wang and Y. Huang, Design of harmonic reducer transmission accuracy tester, Instrument Technique and Sensor, 12 (2019) 49–51.

    Google Scholar 

  33. J. Shen, Research on the method and device for detecting the performance of harmonic reducer, Master’s Thesis, Xi’an University of Science and Technology, China (2019).

    Google Scholar 

  34. Z. Fu, Dynamic simulation of harmonic drive, Master’s Thesis, Xiamen University, China (2017).

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Key Research and Development Projects in Anhui Province of China (Grant No. 202004a07020046) and the National Natural Science Foundation of the PR China (Grant No.51775163). The authors would like to thank other members of research team for their contributions to this project.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Anhui University of Science and Technology, Huainan, 232001, China

    Mei Shen, Hongtao Yang, Jingjing Cheng, Mengyao Zhang & Tingting Hu

  2. Institute of Environmentally Friendly Materials and Occupational Health, Anhui University of Science and Technology, Wuhu, 241000, China

    Mei Shen, Hongtao Yang, Jingjing Cheng, Mengyao Zhang & Tingting Hu

  3. School of Instrument Science and Opto-electronics Engineering, Hefei University of Technology, Hefei, 230009, China

    Yi Hu

Authors
  1. Mei Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongtao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingjing Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mengyao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tingting Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yi Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongtao Yang.

Additional information

Hongtao Yang received his B.S. and M.S. degrees in Measurement and Control Technology and Instrument from Anhui University of Science and Technology in China in 1993 and 2001, respectively. He obtained his Ph.D. degree from Hefei University of Technology in 2007. He is now a Professor of the School of Mechanical Engineering at Anhui University of Science and Technology. His main research interests are precision testing technology, modern precision theory and application.

Mei Shen is a Ph.D. majored in Mechanical Engineering of Anhui University of Science and Technology. Her research interests include precision machinery and precision measurement.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shen, M., Yang, H., Cheng, J. et al. Joint module angle error analysis and modelling of self-driven articulated arm coordinate measuring machine. J Mech Sci Technol 36, 6329–6344 (2022). https://doi.org/10.1007/s12206-022-1144-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-022-1144-0

Keywords

Search

Navigation