Online Payload Identification for Tactile Robots Using the Momentum Observer

Knowledge of the robot's load inertial parameters is indispensable for accurate and safe operation, especially in collaborative robotics. However, an intuitive method for online inertial payload identification, usable while the robot is executing another online generated task, is still lacking. In this work, we propose an online payload identification approach based on the momentum observer using proprioceptive sensors of tactile robots and a novel filter design of kinematic measure-ments. Furthermore, we introduce a novel calibration scheme, that allows circumventing constraints of current calibration methods for payload identification. Specifically, the requirement of performing exactly the same motion for calibration as well as for the identification process is released. This is achieved by introducing an average virtual calibration object that improves the robot model for the identification process. In experiments with a Franka Emika Panda robot, it is shown that the proposed methods surpass common methods in terms of identification error. Especially, the novel calibration approach shows high robustness against temporal and spatial misalignment of the motions.