Experiment design for robot dynamic calibration

Abstract

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 discussed.