Title :
Calibration of industrial robots with product-of-exponential (POE) model and adaptive Neural Networks
Author :
Tao, P.Y. ; Yang, G.
Author_Institution :
Singapore Inst. of Manuf. Technol., A*STAR, Singapore, Singapore
Abstract :
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.
Keywords :
calibration; control engineering computing; industrial robots; position control; radial basis function networks; POE model; RBF NN; adaptive neural networks; augmented robot model; error sources; industrial robots; positioning accuracy; product-of-exponential model; radial basis function neural network; two stage calibration process; Adaptation models; Artificial neural networks; Calibration; Data models; Joints; Robots; Training;
Conference_Titel :
Robotics and Automation (ICRA), 2015 IEEE International Conference on
Conference_Location :
Seattle, WA
DOI :
10.1109/ICRA.2015.7139380
