Skip to main content
SpringerLink
Log in

Parameter estimation survey for multi-joint robot dynamic calibration case study

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Accurate model parameters are the basis of robot dynamics. Many linear and nonlinear models have been proposed to calibrate the inertial parameters and friction parameters of multi-joint robots. However, methods of choosing a model and calculating its parameters still have few summaries. This paper reviews typical linear/nonlinear models and different calculation methods for robot dynamic calibration. Through simulations, the features of different methods are analyzed, including torque error, parameter error, model adaptability, solution time, and anti-interference ability of the calibration results. Finally, an experiment performed on a six-degree-of-freedom industrial manipulator is used as an example to illustrate how to select the model for a specified robot. These comparisons and experiments provide references for the parameter calibration of multi-joint robots.

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. Jamisola R. Dynamics identification and control of an industrial robot. In: Proceedings of the 9th International Conference on Advanced Robotics, Kyongju, 1999. 749–754

    Google Scholar 

  2. Hollerbach J, Khalil W, Gautier M. Model identification. In: Springer Handbook of Robotics. Berlin: Springer, 2016. 113–138

    Chapter  Google Scholar 

  3. van Damme M, Beyl P, Vanderborght B, et al. Estimating robot end-effector force from noisy actuator torque measurements. In: Proceedings of IEEE International Conference on Robotics and Automation, Shanghai, 2011. 1108–1113

    Google Scholar 

  4. Richalet J, Fiani P. The global approach in identification protocol optimization. In: Proceedings of International Conference on Control Applications, Albany, 1995. 423–431

    Chapter  Google Scholar 

  5. Gautier M, Janot A, Vandanjon P O. A new closed-loop output error method for parameter identification of robot dynamics. IEEE Trans Contr Syst Technol, 2013, 21: 428–444

    Article  Google Scholar 

  6. Janot A, Vandanjon P O, Gautier M. A generic instrumental variable approach for industrial robot identification. IEEE Trans Contr Syst Technol, 2014, 22: 132–145

    Article  Google Scholar 

  7. Montazeri A, West C, Monk S D, et al. Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm. Int J Control, 2017, 90: 661–683

    Article  MathSciNet  MATH  Google Scholar 

  8. Wensing P M, Kim S, Slotine J J E. Linear matrix inequalities for physically consistent inertial parameter identification: a statistical perspective on the mass distribution. IEEE Robot Autom Lett, 2018, 3: 60–67

    Article  Google Scholar 

  9. Sousa C D, Cortesão R. Physical feasibility of robot base inertial parameter identification: a linear matrix inequality approach. Int J Robot Res, 2014, 33: 931–944

    Article  Google Scholar 

  10. Welch G, Bishop G. An Introduction to the Kalman Filter. Chapel Hill, Technical Report. 1995

    Google Scholar 

  11. Gautier M, Poignet P. Extended Kalman filtering and weighted least squares dynamic identification of robot. Control Eng Practice, 2001, 9: 1361–1372

    Article  Google Scholar 

  12. Bona B, Indri M. Friction compensation in robotics: an overview. In: Proceedings of the 44th IEEE Conference Decision Control, Seville, 2005. 4360–4367

    Chapter  Google Scholar 

  13. Swevers J, Verdonck W, Schutter J D. Dynamic model identification for industrial robots. IEEE Control Syst, 2007, 27: 58–71

    Article  MATH  Google Scholar 

  14. Ding L, Wu H, Yao Y, et al. Dynamic model identification for 6-DOF industrial robots. J Robot, 2015, 2015: 1–9

    Article  Google Scholar 

  15. Freidovich L, Robertsson A, Shiriaev A, et al. LuGre-model-based friction compensation. IEEE Trans Contr Syst Technol, 2010, 18: 194–200

    Article  Google Scholar 

  16. Astrom K J, Carlos C C. Revisiting the LuGre friction model. IEEE Control Syst, 2008, 28: 101–114

    Article  MATH  Google Scholar 

  17. Bompos N A, Artemiadis P K, Oikonomopoulos A S, et al. Modeling, full identification and control of the mitsubishi PA-10 robot arm. In: Proceedings of 2007 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Zurich, 2007. 1–6

    Google Scholar 

  18. Wernholt E, Gunnarsson S. Nonlinear identification of a physically parameterized robot model 1. IFAC Proc Vol, 2006, 39: 143–148

    Article  Google Scholar 

  19. Grotjahn M, Daemi M, Heimann B. Friction and rigid body identification of robot dynamics. Int J Solids Struct, 2001, 38: 1889–1902

    Article  MATH  Google Scholar 

  20. Lee S D, Ahn K H, Song J B. Torque control based sensorless hand guiding for direct robot teaching. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, 2016. 745–750

    Google Scholar 

  21. Blumenkranz S J, Prisco G M, DiMaio S P, et al. Force and torque sensing in a surgical robot setup arm. US Patent, 9 895 813, 2018

  22. Erden M S, Jonkman J A. Physical human-robot interaction by observing actuator currents. Int J Robot Autom, 2012, 27: 233–243

    Google Scholar 

  23. Nagamatsu Y, Shirai T, Suzuki H, et al. Distributed torque estimation toward low-latency variable stiffness control for gear-driven torque sensorless humanoid. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, 2017. 5239–5244

    Google Scholar 

  24. Vuong N D, Marcelo A H, Li Y P, et al. Improved dynamic identification of robotic manipulators in the linear region of dynamic friction. IFAC Proc Vol, 2009, 42: 167–172

    Article  Google Scholar 

  25. Vuong N D, Marcelo A H. Dynamic model identification for industrial robots. Acta Polytech Hung, 2009, 6: 51–68

    Google Scholar 

  26. Stürz Y R, Affolter L M, Smith R S. Parameter identification of the KUKA LBR iiwa robot including constraints on physical feasibility. IFAC-PapersOnLine, 2017, 50: 6863–6868

    Article  Google Scholar 

  27. Duan X J, Zhi J H, Chen H M, et al. Two novel robust adaptive parameter estimation methods with prescribed performance and relaxed PE condition. Sci China Inf Sci, 2018, 61: 129203

    Article  MathSciNet  Google Scholar 

  28. Jahandideh H, Namvar M. Use of pso in parameter estimation of robot dynamics; part one: no need for parameterization. In: Proceedings of the 16th International Conference on System Theory, Control and Computing, Sinaia, 2012. 1–6

    Google Scholar 

  29. Craig J. Introduction to Robotics: Mechanics and Control. 3rd ed. Upper Saddle River: Pearson Education, 2005. 165–200

    Google Scholar 

  30. Kammerer N, Garrec P. Dry friction modeling in dynamic identification for robot manipulators: theory and experiments. In: Proceedings of 2013 IEEE International Conference on Mechatronics, Kagawa, 2013. 422–429

    Chapter  Google Scholar 

  31. Wu D W, Liu Q, Xu W J, et al. External force detection for physical human-robot interaction using dynamic model identification. In: Proceedings of International Conference on Intelligent Robotics and Applications, 2017. 581–592

    Chapter  Google Scholar 

  32. Wang X M, He X K, Bao Y, et al. Parameter estimates of Heston stochastic volatility model with MLE and consistent EKF algorithm. Sci China Inf Sci, 2018, 61: 042202

    Article  Google Scholar 

  33. Janot A, Vandanjon P O, Gautier M. Identification of 6 DOF rigid industrial robots with the instrumental variable method. IFAC Proc Vol, 2012, 45: 1659–1664

    Article  Google Scholar 

  34. Sousa C D, Cortesão R. Physically feasible dynamic parameter identification of the 7-DOFWAM robot. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, 2013. 2868–2873

    Google Scholar 

  35. Marino I P, Miquez J. Gradient-descent methods for parameter estimation in chaotic systems. In: Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, Zagreb, 2005. 440–445

    Google Scholar 

  36. Reeves C R. Genetic Algorithms. Boston: Springer, 2010. 109–139

    Google Scholar 

  37. Candy J V. Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods. Hoboken: John Wiley Sons, 2009. 237–293

    Book  Google Scholar 

  38. Wu J, Wang J, You Z. An overview of dynamic parameter identification of robots. Robot Comput-Integrated Manuf, 2010, 26: 414–419

    Article  Google Scholar 

  39. Sun Y, Hollerbach J M. Observability index selection for robot calibration. In: Proceedings of 2008 IEEE International Conference on Robotics and Automation, London, 2008. 831–836

    Chapter  Google Scholar 

  40. Bargsten V, Zometa P, Findeisen R. Modeling, parameter identification and model-based control of a lightweight robotic manipulator. In: Proceedings of 2013 IEEE International Conference on Control Applications (CCA), Hyderabad, 2013. 134–139

    Chapter  Google Scholar 

  41. Calafiore G, Indri M, Bona B. Robot dynamic calibration: optimal excitation trajectories and experimental parameter estimation. J Robot Syst, 2001, 18: 55–68

    Article  MATH  Google Scholar 

  42. Swevers J, Ganseman C, Tukel D B, et al. Optimal robot excitation and identification. IEEE Trans Robot Autom, 1997, 13: 730–740

    Article  Google Scholar 

  43. Presse C, Gautier M. New criteria of exciting trajectories for robot identification. In: Proceedings of 1993 IEEE International Conference on Robotics and Automation, Atlanta, 1993. 907–912

    Google Scholar 

  44. Liu J C, Wu Z X, Yu J Z, et al. Sliding mode fuzzy control-based path-following control for a dolphin robot. Sci China Inf Sci, 2018, 61: 024201

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant Nos. U1713222, 61773378, 61421004, U1806204), Beijing Science and Technology Project (Grant No. Z181100003118006), and Youth Innovation Promotion Association CAS.

Author information

Authors and Affiliations

  1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Shaolin Zhang, Shuo Wang, Fengshui Jing & Min Tan

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

    Shaolin Zhang, Shuo Wang, Fengshui Jing & Min Tan

  3. CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, 200031, China

    Shuo Wang

Authors
  1. Shaolin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fengshui Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Min Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuo Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, S., Wang, S., Jing, F. et al. Parameter estimation survey for multi-joint robot dynamic calibration case study. Sci. China Inf. Sci. 62, 202203 (2019). https://doi.org/10.1007/s11432-018-9726-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-018-9726-3

Keywords

Search

Navigation