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Model Identification

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Springer Handbook of Robotics

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 (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

BLUE:

best linear unbiased estimator

DH:

Denavit–Hartenberg

DOF:

degree of freedom

LVDT:

linear variable differential transformer

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Authors and Affiliations

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

    John Hollerbach Prof

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

    Wisama Khalil Prof

  3. IRCCyN, ECN, Université de Nantes, 1 Rue de la Noë, 44321, Nantes, France

    Maxime Gautier Prof

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

Correspondence to John Hollerbach Prof , Wisama Khalil Prof or Maxime Gautier Prof .

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Editors and Affiliations

  1. PRISMA Lab, Dipartimento di Informatica e Sistemistica, Universitá degli Studi di Napoli Federico II, 80125, Napoli, Italy, Via Claudio 21

    Bruno Siciliano Prof.

  2. Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, 94305-9010, Stanford, CA, USA

    Oussama Khatib Prof.

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© 2008 Springer-Verlag

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Hollerbach, J., Khalil, W., Gautier, M. (2008). Model Identification. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30301-5_15

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  • DOI: https://doi.org/10.1007/978-3-540-30301-5_15

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  • Online ISBN: 978-3-540-30301-5

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