NP-MBO: A newton predictor-based momentum observer for interaction force estimation of legged robots

Abstract

Swift perception of interaction forces is a crucial skill required for legged robots to ensure safe human–robot interaction and dynamic contact management. Proprioceptive-based interactive force is widely applied due to its outstanding cross-platform versatility. In this paper, we present a novel interactive force observer, which possesses superior dynamic tracking performance. We propose a dynamic cutoff frequency configuration method to replace the conventional fixed cutoff frequency setting in the traditional momentum-based observer (MBO). This method achieves a balance between rapid tracking and noise suppression. Moreover, to mitigate the phase lag introduced by the low-pass filtering, we cascaded a Newton Predictor (NP) after MBO, which features simple computation and adaptability. The precision analysis of this method has been presented. We conducted extensive experiments on the point-foot biped robot BRAVER to validate the performance of the proposed algorithm in both simulation and physical prototype.