Real-time Inertial Parameter Identification of Floating-Base Robots Through Iterative Primitive Shape Division
Jiafeng Xu, Yu Zheng, Xinyang Jiang, Sicheng Yang, Lingzhu Xiang, Zhengyou Zhang
Abstract
Dynamic models play a key role in robot motion generation and control and the identification of inertial parameters is a critical component for obtaining an accurate dynamic model of a robot. This paper presents a novel iterative primitive shape division method for the inertia parameter identification of floating-base robots. Describing a robot by a set of primitive shapes with uniform mass distributions, the method iteratively divides the primitive shapes into smaller ones and refines their masses, which quickly converges to yielding the true inertia parameters of the robot. This method guarantees the physical consistency of the obtained parameters, possesses a high computational efficiency for online deployment, and works without contact force measurement. Furthermore, it can be used to estimate the position and magnitude of an external load applied to the robot. Simulations and experiments on a quadruped robot have been conducted to verify the effectiveness and efficiency of the proposed method.
BibTeX
@inproceedings{icra2022_realtimeinertial,
title = {Real-time Inertial Parameter Identification of Floating-Base Robots Through Iterative Primitive Shape Division},
author = {Jiafeng Xu and Yu Zheng and Xinyang Jiang and Sicheng Yang and Lingzhu Xiang and Zhengyou Zhang},
booktitle = {ICRA 2022},
year = {2022}
}