Model Identification

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.

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • John Hollerbach
    • 1
  • Wisama Khalil
    • 2
  • Maxime Gautier
    • 2
  1. 1.School of ComputingUniversity of UtahSalt Lake CityUSA
  2. 2.IRCCyN, ECNUniversity of NantesNantesFrance

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