Online Learning for Vibration Suppression in Physical Robot Interaction using Power Tools

Strong and persistent vibration is harmful for both human and machine health. In humans, long exposure to vibrating power tools may induce health problems, such as the hand-arm vibration syndrome [1]. Instead in machines, vibration undermines the precision in control applications and may lead to mechanical wear [2, 3]. For these reasons, vibration suppression is an important capability for employing collaborative robots in new environments such as construction sites [4] where the vibration is a common phenomenon. This work builds on our preliminary results [5], in which we studied the feedforward vibration suppression in human-robot collaboration (HRC). The main outcome of our study was that feedforward force control increases the vibration suppression performance while maintaining a compliant impedance profile, in comparison to the variable impedance control (VIC) approach which was previously used in HRC literature for dealing with vibrations [6, 7]. We successfully applied the BMFLC algorithm [8] in our high-dof robotic arm for learning and suppressing the vibration online. In this work, we extend both our theoretical approach and experiments.