Probabilistic Approach to Online Stiffness Estimation for Robotic Tasks

Abstract

Information about environmental stiffness is useful for robotic tasks involving interactions with unstructured and unknown environments. However, online estimation remains a challenge. Owing to the nature of its calculation algorithm, a large amount of noise may be generated, depending on the response value of the force and position. In this study, we propose a variable gain filter that predicts the degree of such noise using a probabilistic approach and reflects only reliable data in the estimation. We show experimentally that the proposed method improves the accuracy of the stiffness estimation without degrading the estimation time constant.