Adaptive minimization of the maximal path deviations of industrial robots

Abstract

A learning system is presented which uses feedforward control to improve the accuracy of standard position controlled robots. The method is executed on joint level since in this case there are less couplings than in the cartesian space. On the other side the main goal is to reduce the maximal deviation from a given cartesian path. This requires extended algorithms which are derived and examined using a KUKA KR6/1 industrial robot. The universal controller is adapted to minimize the maximal path error and then shows significantly better performance when repeating the training path or a similar trajectory.