Improving Position Accuracy of Robot Manipulators Using Neural Networks

Abstract

A neural network algorithm is proposed to estimate the positional errors in a robot manipulator calibration process. The position errors at various points within the calibration space are first obtained by a camera or other measurement devices. A window consisted of multiple cells surrounding the interpolated position is used to form the input and output pairs of training data set. A neural network model is utilized to extract the local feature of the error surface. The target pose is then compensated by the position errors obtained by neural network model. Numerical experiment is performed based on a common industrial setup. A significant improvement in accuracy is obtained by the proposed techniques in comparison with traditional analytical methods.