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Estimation of gravity parameters for multi-DOF serial manipulator arms using model learning with sparsity inducing norms

Chenglong Yu (State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China)
Zhiqi Li (State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China)
Dapeng Yang (State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China)
Hong Liu (State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China)
Alan F. Lynch (Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada)

Industrial Robot

ISSN: 0143-991x

Article publication date: 4 August 2021

Issue publication date: 16 November 2021

188

Abstract

Purpose

This study aims to propose a novel method based on model learning with sparsity inducing norms for estimating dynamic gravity terms of the serial manipulators. This method is realized by operating the robot, acquiring data and filtering the features in signal acquisition to adapt to the dynamic gravity parameters.

Design/methodology/approach

The core principle of the method is to analyze the dictionary composition of the basis function of the model based on the dynamic equation and the Jacobian matrix of an arm. According to the structure of the basis function and the sparsity of the features, combined with joint-angle and driving-torque data acquisition, the effective features of dynamic gravity parameters are screened out using L1-norm optimization and learning algorithms.

Findings

The theoretical analysis revealed that training data obtained based on joint angles and driving torques could rapidly update dynamic gravity parameters. The simulation experiment was carried out by using the publicly available robot model and compared with the previous disassembly method to evaluate the feasibility and performance. The real 7-degree of freedom (DOF) industrial manipulator was used to further discuss the effects of the feature selection. The results show that this estimation method can be fully operational and efficient in industrial applications.

Research limitations/implications

This approach is applicable to most serial robots with multi-DOF and the dynamic gravity parameters of the robot are estimated through learning and optimization. The method does not require prior knowledge of the robot arm structure and only requires joint-angle and driving-torque data acquisition under low-speed motion. Furthermore, as it is a data-driven-based method, it can be applied to gravity parameters updating.

Originality/value

Different from previous general robot dynamic modelling methods, the sparsity of the analytical form of dynamic equations was exploited and model learning was formulated as a convex optimization problem to achieve effective gravity parameters screening. The novelty of this estimation approach is that the method does not only require any prior knowledge but also does not require a specifically designed trajectory. Thus, this method can avoid the laborious work of parameter calibration and the induced modelling errors. By using a data-driven learning approach, the new parameter updating process can be completed conveniently when the robot carries additional mass or the end-effector changes for different tasks.

Keywords

Acknowledgements

Funding: This work is supported by the National Natural Science Foundation of China (NO.61803124), the National Basic Research Program of China (973-2013CB733124) and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant NO.51521003).

Citation

Yu, C., Li, Z., Yang, D., Liu, H. and Lynch, A.F. (2021), "Estimation of gravity parameters for multi-DOF serial manipulator arms using model learning with sparsity inducing norms", Industrial Robot, Vol. 48 No. 6, pp. 891-905. https://doi.org/10.1108/IR-03-2021-0052

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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