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.
Keywords
- Dynamics Identification
- Kinematics Calibration
- Least Squares
- Base Parameters
- Persistent Exciting Motions
This is a preview of subscription content, access via your institution.
References
W. Khalil, E. Dombre,Modeling, Identification and Control of Robots (Hermès Penton, London, 2002)
S. Hayati, M. Mirmirani, Improving the absolute positioning accuracy of robot manipulators. J.Robot. Syst. 2(4), 397–413 (1985)
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
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)
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
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
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)
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
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)
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)
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
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
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
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)
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)
W. Khalil, F. Bennis, Symbolic calculation of the base inertial parameters of closed-loop robots. Int. J. Robot. Res. 14(2), 112–128 (1995)
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
W. Khalil, M. Gautier, C. Enguehard, Identifiable parameters and optimum configurations for robots calibration. Robotica 9(1), 63–70 (1991)
W. Khalil, S. Besnard, P. Lemoine, Comparison study of the geometric parameters calibration methods. Int. J. Robot. Autom. 15(2), 56–67 (2000)
M. Gautier, W. Khalil, Exciting trajectories for the identification of base inertial parameters of robots. Int. J. Robot. Res. 11(4), 362–375 (1992)
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)
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
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
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
V. Fedorov, W. Studden, E. Klimko, Theory of Optimal Experiments (Academic Press, New York, 1972)
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
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)
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)
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
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
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
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)
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)
J. Nocedal, S.J. Wright, Numerical Optimization, Springer Series in Operations Research and Financial Engineering, 2nd edn. (Springer, New York, 2006)
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
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
R. Fletcher, Practical Methods of Optimization, 2nd edn. (Wiley, New York, 1987)
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)
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
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
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
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
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media B.V.
About this entry
Cite this entry
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
Download citation
DOI: https://doi.org/10.1007/978-94-007-7194-9_6-1
Received:
Accepted:
Published:
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