Authors:
Mojtaba A. Khanesar
;
Samanta Piano
and
David Branson
Affiliation:
Faculty of Engineering, University of Nottingham, NG7 2RD, U.K.
Keyword(s):
Positional Accuracy, Forward Kinematic Calibration, Laser Tracker System, Multi-output Least Squares, Support Vector Regression.
Abstract:
Precision positioning of industrial robots is a vital requirement on the factory floor. Robot end effector positioning using joint angle readings from joint encoders and industrial robot forward kinematics (FKs) is a common practice. However, mechanical wear, manufacturing and assembly tolerances, and errors in robot dimension measurement result in parameter uncertainties in the robot FK model. Uncertainties in robot FK result in inaccurate position measurement. In this paper, we use a multi-output least squares support vector regression (MLS-SVR) method to improve the positioning accuracies of industrial robots using a highly accurate laser tracker system, Leica AT960-MR. This equipment is a non-contact metrology one capable of performing measurements with error of less than 3/ . To perform this task, industrial robot FK is formulated as a regression problem whose unknown parameters are tuned using laser tracker position data as target values. MLS-SVR algorithm is used to estimate t
he industrial robot FK parameters. It is observed that using the proposed approach, the accuracy of industrial robot FKs in terms of mean absolute errors of static and near-static motion in all three dimensions decreases from its measured value: from 71.9 to 20.9 (71% decrease).
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