Calibration of industrial robots with product-of-exponential (POE) model and adaptive Neural Networks

Robot calibration is to improve the accuracy of the robot model so as to achieve better positioning accuracy within the robot work cell. Model based calibration approaches are in general limited to compensating for geometric errors and are unable to compensate for error sources that do not fit withi...

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Bibliographic Details
Published in2015 IEEE International Conference on Robotics and Automation (ICRA) pp. 1448 - 1454
Main Authors Tao, P. Y., Yang, G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2015
Subjects
Adaptation models
Artificial neural networks
Calibration
Data models
Joints
Robots
Training
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Summary:Robot calibration is to improve the accuracy of the robot model so as to achieve better positioning accuracy within the robot work cell. Model based calibration approaches are in general limited to compensating for geometric errors and are unable to compensate for error sources that do not fit within the proposed robot model. In order to compensate for the unmodeled error sources, a Radial Basis Function (RBF) Neural Network (NN) augmented robot model is proposed together with a two stage calibration process for training the NN. A simulation and an experimental study are conducted to verify the effectiveness of the proposed solution.
ISSN:1050-4729
2577-087X
DOI:10.1109/ICRA.2015.7139380