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.

Keywords

Interaction force estimation / Momentum-based observer / Newton predictor / Force control

CRediT authorship contribution statement

Zhengguo Zhu: Data curation, Formal analysis, Methodology, Validation, Writing - original draft. Weikai Ding: Data curation, Formal analysis, Validation. Weiliang Zhu: Validation. Daoling Qin: Validation. Teng Chen: Project administration, Writing - review & editing. Xuewen Rong: Funding acquisition, Project administration. Guoteng Zhang: Funding acquisition, Project administration, Supervision, Validation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported in part by the National Key Research and Development Program of China (2022YFB4701504), the National Natural Science Foundation of China (62373223 and 62203268), and Youth Innovation and Technology Support Plan for Higher Education Institutions in Shandong Province (2023KJ029).

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