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Springer Handbook of Robotics pp 113–138Cite as

Model Identification

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Abstract

This chapter discusses how to determine the kinematic parameters and the inertial parameters of robot manipulators. Both instances of model identification are cast into a common framework of least-squares parameter estimation, and are shown to have common numerical issues relating to the identifiability of parameters, adequacy of the measurement sets, and numerical robustness. These discussions are generic to any parameter estimation problem, and can be applied in other contexts.

For kinematic calibration, the main aim is to identify the geometric Denavit–Hartenberg (GlossaryTerm

DH

) parameters, although joint-based parameters relating to the sensing and transmission elements can also be identified. Endpoint sensing or endpoint constraints can provide equivalent calibration equations. By casting all calibration methods as closed-loop calibration, the calibration index categorizes methods in terms of how many equations per pose are generated.

Inertial parameters may be estimated through the execution of a trajectory while sensing one or more components of force/torque at a joint. Load estimation of a handheld object is simplest because of full mobility and full wrist force-torque sensing. For link inertial parameter estimation, restricted mobility of links nearer the base as well as sensing only the joint torque means that not all inertial parameters can be identified. Those that can be identified are those that affect joint torque, although they may appear in complicated linear combinations.

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Abbreviations

3-D:

three-dimensional

BLUE:

best linear unbiased estimator

DH:

Denavit–Hartenberg

DOF:

degree of freedom

IV:

instrumental variable

LVDT:

linear variable differential transformer

RMS:

root mean square

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Author information

Authors and Affiliations

  1. School of Computing, University of Utah, 50 South Central Campus Drive, UT 84112, Salt Lake City, USA

    John Hollerbach

  2. IRCCyN, ECN, University of Nantes, 1 Rue de la Noë, 44321, Nantes, France

    Wisama Khalil & Maxime Gautier

Authors
  1. John Hollerbach
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  2. Wisama Khalil
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  3. Maxime Gautier
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Corresponding author

Correspondence to John Hollerbach .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy

    Bruno Siciliano

  2. Department of Computer Sciences, Artificial Intelligence Laboratory, Stanford University, 450 Serra Mall, CA 94305, Stanford, USA

    Oussama Khatib

Video-References

Video-References

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Calibration of ABB’s IRB 120 industrial robot available from http://handbookofrobotics.org/view-chapter/06/videodetails/422

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Robot calibration using a touch probe available from http://handbookofrobotics.org/view-chapter/06/videodetails/425

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Calibration and accuracy validation of a FANUC LR Mate 200iC industrial robot available from http://handbookofrobotics.org/view-chapter/06/videodetails/430

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Dynamic identification of Staubli TX40: Trajectory without load available from http://handbookofrobotics.org/view-chapter/06/videodetails/480

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Dynamic identification of Staubli TX40: Trajectory with load available from http://handbookofrobotics.org/view-chapter/06/videodetails/481

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Dynamic identification of Kuka LWR: Trajectory without load available from http://handbookofrobotics.org/view-chapter/06/videodetails/482

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Dynamic identification of Kuka LWR: Trajectory with load available from http://handbookofrobotics.org/view-chapter/06/videodetails/483

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Dynamic identification of a parallel robot: Trajectory with load available from http://handbookofrobotics.org/view-chapter/06/videodetails/485

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Dynamic identification of Kuka KR270: Trajectory without load available from http://handbookofrobotics.org/view-chapter/06/videodetails/486

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Dynamic identification of Kuka KR270: trajectory with load available from http://handbookofrobotics.org/view-chapter/06/videodetails/487

:

Dynamic identification of a parallel robot: Trajectory without load available from http://handbookofrobotics.org/view-chapter/06/videodetails/488

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Hollerbach, J., Khalil, W., Gautier, M. (2016). Model Identification. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-32552-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-32552-1_6

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