Learning to improve the path accuracy of position controlled robots

A learning method is presented which improves the dynamic accuracy of conventional industrial robots with integrated position control. The method is based on feedforward control being able to follow off-line programmed trajectories with high speed and negligible pose errors. For learning, the robot has to be moved along a given path. The algorithm then estimates a simple model. This model is used to build a controller which is able to modify positional commands, thus reducing the positional path error from some millimeters to approximately 0.2 mm for a Manutec r2 robot. This improvement is valid also for other, non-trained trajectories. For repetitive control of a single path the error is even lower. Measurements of path accuracy are verified using data of a force/torque sensor during tracking a known contour