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Humanoid Robotics: A Reference pp 1–23Cite as

Calibration and Parameter Estimation

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Abstract

The geometric and inertial parameters of a robot are of crucial importance for the development of model-based control and validation of simulation results. These parameters are key parameters in the equations of motion. Most of the time, they are parameters provided by CAD data; however, experience has shown that CAD data are only a rough approximation of the true parameters because the CAD data do not take into account cables and small equipment, in addition the robot may be subject to several modifications and enhancements with time. This chapter describes the state of the art in kinematics calibration and dynamics identification for humanoid robots. After presenting the fundamental equations and the resolution of the problem, this chapter emphasizes the practical implementations to facilitate the identification and to guarantee a good accuracy of the results. In particular, this chapter emphasizes the three key aspects to perform accurate identification: (1) modeling; (2) generating motions for identification; (3) practical implementation. Experimental examples and results are used to illustrate their importance.

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References

  1. W. Khalil, E. Dombre,Modeling, Identification and Control of Robots (Hermès Penton, London, 2002)

    MATH  Google Scholar 

  2. S. Hayati, M. Mirmirani, Improving the absolute positioning accuracy of robot manipulators. J.Robot. Syst. 2(4), 397–413 (1985)

    Article  Google Scholar 

  3. W. Khalil, A. Vijayalingam, B. Khomutenko, I. Mukhanov, P. Lemoine, G. Ecorchard, OpenSYMORO: an open-source software package for symbolic modelling of robots, in IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Hong Kong, 2014, pp. 1206–1211

    Google Scholar 

  4. J.M. Hollerbach, C.W. Wampler, The calibration index and taxonomy for robot kinematic calibration methods. Int. J. Robot. Res. 15(6), 573–591 (1996)

    Article  Google Scholar 

  5. N. Moutinho, M. Brandao, R. Ferreira, J. Gaspar, A. Bernardino, A. Takanishi, J. Santos-Victor, Online calibration of a humanoid robot head from relative encoders, IMU readings and visual data, in Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, 2012, pp. 2070–2075

    Google Scholar 

  6. O. Birbach, B. Bäuml, U. Frese, Automatic and self-contained calibration of a multi-sensorial humanoid’s upper body, in Proceedings of the IEEE International Conference on Robotics and Automation, Saint Paul, 2012, pp. 3103–3108

    Google Scholar 

  7. Y. Nakamura, K. Yamane, Dynamics computation of structure-varying kinematic chains and its application to human figures. IEEE Trans. Robot. Autom. 16(2), 124–134 (2000)

    Article  Google Scholar 

  8. K. Yamane, Practical kinematic and dynamic calibration methods for force-controlled humanoid robots, in Proceedings of the IEEE-RAS International Conference on Humanoid Robots, Bled, 2011, pp. 269–275

    Google Scholar 

  9. K. Ayusawa, G. Venture, Y. Nakamura, Identifiability and identification of inertial parameters using the underactuated base-link dynamics for legged multibody systems. Int. J. Robot. Res. 33(3), 446–468 (2014)

    Article  Google Scholar 

  10. K. Ayusawa, Y. Ikegami, Y. Nakamura, Simultaneous global inverse kinematics and geometric parameter identification of human skeletal model from motion capture data. Mech. Mach. Theory 74, 274–284 (2014)

    Article  Google Scholar 

  11. K. Yoshida, D. Nenchev, M. Uchiyama, Moving base robotics and reaction management control, in Proceedings of the 7th International Symposium of Robotics Research, Pittsburg, 1995, pp. 100–109

    Google Scholar 

  12. Y. Fujimoto, S. Obata, A. Kawamura, Robust biped walking with active interaction control between foot and ground, in Proceedings of the IEEE International Conference on Robotics and Automation, Leuven, 1998, pp. 2030–2035

    Google Scholar 

  13. M. Gautier, S. Briot, Global identification of drive gains parameters of robots using a known payload, in Proceedings of the IEEE International Conference on Robotics and Automation, Saint Paul, 2012, pp. 2812–2817

    Google Scholar 

  14. G. Venture, P. Ripert, W. Khalil, M. Gautier, P. Bodson, Modeling and identification of passenger car dynamics using robotics formalism. IEEE Trans. Intell. Transp. Syst. 7(3), 349–359 (2006)

    Article  Google Scholar 

  15. O. Khatib, A unified approach for motion and force control of robot manipulators: the operational space formulation. IEEE J. Robot. Autom. 3(1), 43–53 (1987)

    Article  Google Scholar 

  16. W. Khalil, F. Bennis, Symbolic calculation of the base inertial parameters of closed-loop robots. Int. J. Robot. Res. 14(2), 112–128 (1995)

    Article  Google Scholar 

  17. M. Gautier, Numerical calculation of the base inertial parameters of robots, in Proceedings of the IEEE International Conference on Robotics and Automation, Tsukuba, 1990, pp. 1020–1025

    Google Scholar 

  18. W. Khalil, M. Gautier, C. Enguehard, Identifiable parameters and optimum configurations for robots calibration. Robotica 9(1), 63–70 (1991)

    Article  Google Scholar 

  19. W. Khalil, S. Besnard, P. Lemoine, Comparison study of the geometric parameters calibration methods. Int. J. Robot. Autom. 15(2), 56–67 (2000)

    Google Scholar 

  20. M. Gautier, W. Khalil, Exciting trajectories for the identification of base inertial parameters of robots. Int. J. Robot. Res. 11(4), 362–375 (1992)

    Article  Google Scholar 

  21. J. Swevers, C. Ganseman, D.-B. Tukel, J.D. Schutter, H.V. Brussel, Optimal robot excitation and identification. IEEE Trans. Robot. Autom. 13(5), 730–740 (1997)

    Article  Google Scholar 

  22. G. Venture, K. Ayusawa, Y. Nakamura, A numerical method for choosing motions with optimal excitation properties for identification of biped dynamics – an application to human, in Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, 2009, pp. 1226–1231

    Google Scholar 

  23. C. Presse, M. Gautier, New criteria of exciting trajectories for robot identification, in Proceedings of the IEEE International Conference on Robotics and Automation, Atlanta, 1993, pp. 907–912

    Google Scholar 

  24. K. Otani, T. Kakizaki, Motion planning and modeling for accurately identifying dynamic parameters of an industrial robotic manipulator, in Proceedings of the International Symposium on Industrial Robots, Tokyo, 1993, pp. 743–748

    Google Scholar 

  25. V. Fedorov, W. Studden, E. Klimko, Theory of Optimal Experiments (Academic Press, New York, 1972)

    Google Scholar 

  26. Y. Sun, J.M. Hollerbach, Observability index selection for robot calibration, in Proceedings of the IEEE International Conference on Robotics and Automation, Pasadena, 2008, pp. 831–836

    Google Scholar 

  27. J.H. Borm, C.H. Menq, Determination of optimal measurement configurations for robot calibration based on observibility measure. Int. J. Robot. Res. 10(1), 51–63 (1991)

    Article  Google Scholar 

  28. M.R. Driels, U.S. Pathre, Significance of observation strategy on the design of robot calibration experiments. J. Rob. Syst. 7(2), 197–223 (1990)

    Article  Google Scholar 

  29. A. Nahvi, J. Hollerbach, The noise amplification index for optimal pose selection in robot calibration, in Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, 1996, pp. 647–654

    Google Scholar 

  30. Y. Sun, J.M. Hollerbach, Active robot calibration algorithm, in Proceedings of the IEEE International Conference on Robotics and Automation, Pasadena, 2008, pp. 1276–1281

    Google Scholar 

  31. J. Jovic, F. Philipp, A. Escande, K. Ayusawa, E. Yoshida, A. Kheddar, G. Venture, Identification of dynamics of humanoids: systematic exciting motion generation, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, 2015, pp. 2173–2179

    Google Scholar 

  32. K. Yoshida, W. Khalil, Verification of the positive definiteness of the inertia matrix of manipulators using base inertial parameters. J. Rob. Res. 19(5), 498–510 (2000)

    Article  Google Scholar 

  33. V. Bonnet, G. Venture, Fast determination of the planar body segment inertial parameters using affordable sensors. IEEE Trans. Neural Syst. Rehabil. Eng. 23(4), 628–635 (2015)

    Article  Google Scholar 

  34. J. Nocedal, S.J. Wright, Numerical Optimization, Springer Series in Operations Research and Financial Engineering, 2nd edn. (Springer, New York, 2006)

    MATH  Google Scholar 

  35. K. Ayusawa, G. Venture, Y. Nakamura, Real-time implementation of physically consistent identification of human body segments, in Proceedings of the IEEE International Conference on Robotics and Automation, San-Francisco, 2011, pp. 6282–6287

    Google Scholar 

  36. M. Gautier, G. Venture, Identification of standard dynamic parameters of robots with positive definite inertia matrix, in Proceedings of the IEEE International Conference on Intelligent Robots, Tokyo, 2013, pp. 5815–5820

    Google Scholar 

  37. R. Fletcher, Practical Methods of Optimization, 2nd edn. (Wiley, New York, 1987)

    MATH  Google Scholar 

  38. S. Gamage, J. Lasenby, New least squares solutions for estimating the average centre of rotation and the axis of rotation. J. Biomech. 35(1), 87–93 (2002)

    Article  Google Scholar 

  39. A. Kirk, J. O'Brien, D.A. Forsyth, Skeletal parameter estimation from optical motion capture data, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, 2004, pp. 782–788

    Google Scholar 

  40. Q.C. Phan, K. Ayusawa, K. Kubota, Y. Nakamura, On the structural identifiability of joint parameters from motion capture data, in IEEE International Conference on Systems, Man, and Cybernetics, Seoul, 2012, pp. 1586–1591

    Google Scholar 

  41. M. Gleicher, Retargetting motion to new characters, in Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, Orlando, 1998, pp. 33–42

    Google Scholar 

  42. N. Pollard, J. Hodgins, M. Riley, C. Atkeson, Adapting human motion for the control of a humanoid robot, in Proceedings of the IEEE International Conference on Robotics and Automation, Washington, DC, 2002, pp. 1390–1397

    Google Scholar 

  43. K. Ayusawa, M. Morisawa, E. Yoshida, Motion retargeting for humanoid robots based on identification to preserve and reproduce human motion, in Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Seattle, 2015, pp. 2774–2779

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Koganei, 184-8588, Tokyo, Japan

    Gentiane Venture

  2. CNRS-AIST JRL(Joint Robotics Laboratory), UMI3218/RL, Intelligent Systems Research Institute, National Institute of Advanced Industrial Science and Technology, AIST Tsukuba Central 1, 1-1-1 Umezono, 305-8560, Tsukuba, Ibaraki, Japan

    Ko Ayusawa

Authors
  1. Gentiane Venture
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  2. Ko Ayusawa
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Corresponding author

Correspondence to Gentiane Venture .

Editor information

Editors and Affiliations

  1. Intuitive Surgical , Sunnyvale, California, USA

    Ambarish Goswami

  2. Singapore, Singapur, Singapore

    Prahlad Vadakkepat

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Venture, G., Ayusawa, K. (2017). Calibration and Parameter Estimation. In: Goswami, A., Vadakkepat, P. (eds) Humanoid Robotics: A Reference. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7194-9_6-1

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  • DOI: https://doi.org/10.1007/978-94-007-7194-9_6-1

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-7194-9

  • Online ISBN: 978-94-007-7194-9

  • eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering

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