Learning robot behavior with artificial neural networks and a coordinate measuring machine

In this paper the design and evaluation of artificial neural networks for learning static and dynamic positioning behavior of an industrial robot are presented. For the collection of training data, an approach based on the Levenberg-Marquardt algorithm was used to calibrate the robot and the coordinate measuring machine to a common reference system. The network design was developed and verified by measuring different robot path segments with varying motion parameters, e.g. speed, payload and path geometry. Different layouts and configurations of feed-forward networks with backpropagation learning algorithms were examined resulting in a multi-layer network based on the calculation of the forward transformation.