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

Experiment Design for Robot Dynamic Calibration / Calafiore, Giuseppe Carlo; Indri, Marina. - STAMPA. - 4:(1998), pp. 3303-3309. (Intervento presentato al convegno 1998 IEEE International Conference on Robotics and Automation tenutosi a Leuven, Belgium nel 16-20 May 1998) [10.1109/ROBOT.1998.680948].

Experiment Design for Robot Dynamic Calibration

CALAFIORE, Giuseppe Carlo;INDRI, Marina
1998

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
1998
078034300X
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/1414908
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