Robot manipulator calibration using neural network and a camera-based measurement system

Abstract

A robot manipulator calibration method is proposed using a camera-based measurement system and a neural network algorithm. The position errors at various points within the calibration space are first obtained by camera-based measurement devices. A window consisting of multiple cells surrounding the interpolated positions is used to form the input and output pairs of training data set. A neural network model is utilized to approximate the error surface. The target pose is then compensated for by the position errors obtained by the neural network model. Numerical experiment is performed based on a common industrial set-up. A significant improvement in accuracy is obtained by the proposed techniques in comparison with traditional bilinear analytical methods.