Sensory feedback structures for robots with supervised learning

Abstract

A concept for sensory feedback and sensor-based teach-in in robotics is presented. Based on previous work of different authors a scheme of hybrid sensory-position control is proposed and has been realized that interpretes every rudimentary teach command in terms of sensory interaction with the environment by generating the socalled artificial constraints and the refined path automatically. Robots with arbitrarily programmable stiffness are one outcome of our technique. Motion commands and sensor data are stored together. The latter ones are then available as reference values for the repetition mode in a possibly changing environment. It is shown that by introducing pseudo-forces/torques the proposed techniques are equally applicable to different kinds of sensor, as are force-torque-sensors, range finders or inductive sensors. The sensor-ball-technique as developed at DFVLR is discussed as one physical realization. Operational systems of this kind, first tested with an ASEA robot in our lab, are going into industrial application now.