Modelling and identification of a six axes industrial robot

This paper deals with the modelling and identification of a six axes industrial St ¨aubli RX90 robot. A non-linear finite element method is used to generate the dynamic equations of motion in a form suitable for both simulation and identification. The latter requires that the equations of motion are linear in the inertia parameters. Joint friction is described by a friction model that describes the friction behaviour in the full velocity range necessary for identification. Experimental parameter identification by means of linear least squares techniques showed to be very suited for identification of the unknown parameters, provided that the problem is properly scaled and that the influence of disturbances is sufficiently analysed and managed. An analysis of the least squares problem by means of a singular value decomposition is preferred as it not only solves the problem of rank deficiency, but it also can correctly deal with measurement noise and unmodelled dynamics

Experiment Design for Robot Dynamic Calibration

Common robot calibration procedures use least-squares (LS) techniques to obtain estimates of the identifiable parameters. The "quality" of the resulting estimates depends significantly upon the used excitation input. The search for the best excitation trajectory is usually posed as a nonlinear path optimization problem aimed at optimizing suitable measures of the LS normal equations matrix. In this paper a parametrization of the class of reference joints trajectories is introduced and a solution framework based on genetic evolution is proposed. The efficiency of the method is illustrated by experimental tests on a SCARA two-link manipulator. Issues related to data acquisition and signals reconstruction and filtering are also discusse