Suppression of robot vibrations using input shaping and learning-based structural models

Abstract

Industrial robots used in manufacturing processes such as drilling of aerospace structures undergo many rapid positioning motions during each operation. Such aggressive motions excite the structural modes of the robot and cause inertial vibrations at the end-effector, which may damage the part and violate the tolerance requirements. This article presents a vibration avoidance technique based on input shaping combined with a learning-based structural dynamic model. A theoretical dynamic model is first developed for commonly used robotic arms considering the flexibilities of the first three joints of the robot. An artificial neural network is developed and used in conjunction with the dynamic model to predict the natural frequency of the system at any pose in the workspace. Transfer learning techniques are used to extend the trained artificial neural network to account for the mass of the payload with minimal data collection. To reduce the residual vibrations of the robot in rapid motions, zero-vibration derivative shapers are designed and implemented. The effectiveness of the presented methodology has been validated experimentally on a Staubli RX90CR robot with an open-architecture control system developed fully in-house. Experimental results show more than 85% reduction in residual vibrations during aggressive motions of the robot.