The Compensation Using Neural Networks for the Position and Orientation Errors of Robot Manipulators

Abstract

A method using neural-networks to compensate for errors in the position and orientation of robot manipulators is proposed. The errors of robot manipulators due to miss in setting or modeling are measured and learned by a neural network. The reference input to robot controllers is modified by using the neural network. The compensation algorithm is discussed in the paper. A method to add the second neural network to the first one is also proposed in order to obtain the accuracy of the compensation without the much increase of the learning time in the back-propagation process. The proposed compensation method is investigated by computer simulation for a two-link and a six-link manipulator, and the method is compared with the one using linear interpolation. It is verified that the errors in the robot manipulators are significantly reduced by using the proposed compensation method. The method is also applied to a two-link SCARA type D.D. robot and its effectiveness is verified by experiments.