Article
A Graph Theory-Based Method for Dynamic Modeling and
Parameter Identification of 6-DOF Industrial Robots
Jun Cheng
1
, Shusheng Bi
1,
*, Chang Yuan
1
, Lin Chen
1
, Yueri Cai
1
and Yanbin Yao
2
Citation: Cheng, J.; Bi, S.; Yuan, C.;
Chen, L.; Cai, Y.; Yao, Y. A Graph
Theory-Based Method for Dynamic
Modeling and Parameter
Identification of 6-DOF Industrial
Robots. Appl. Sci. 2021, 11, 10988.
https://doi.org/10.3390/
app112210988
Academic Editors: Giovanni Boschetti
and João Miguel da Costa Sousa
Received: 22 September 2021
Accepted: 15 November 2021
Published: 19 November 2021
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1
Robotics Institute, Beihang University, Beijing 100191, China; chengjun@buaa.edu.cn (J.C.);
yuanchang@buaa.edu.cn (C.Y.); BY1907004@buaa.edu.cn (L.C.); caiyueri@buaa.edu.cn (Y.C.)
2
AVIC Manufacturing Technology Institute, Beijing 100024, China; yaoyb@avic.com
* Correspondence: ssbi@buaa.edu.cn; Tel.: +86-10-8231-4554
Abstract:
At present, the absolute positioning accuracy and control accuracy of industrial serial
robots need to be improved to meet the accuracy requirements of precision manufacturing and
precise control. An accurate dynamic model is an important theoretical basis for solving this problem,
and precise dynamic parameters are the prerequisite for precise control. The research of dynamics
and parameter identification can greatly promote the application of robots in the field of precision
manufacturing and automation. In this paper, we study the dynamical modeling and dynamic
parameter identification of an industrial robot system with six rotational DOF (6R robot system)
and propose a new method for identifying dynamic parameters. Our aim is to provide an accurate
mathematical description of the dynamics of the 6R robot and to accurately identify its dynamic
parameters. First, we establish an unconstrained dynamic model for the 6R robot system and rewrite
it to obtain the dynamic parameter identification model. Second, we establish the constraint equations
of the 6R robot system. Finally, we establish the dynamic model of the constrained 6R robot system.
Through the ADAMS simulation experiment, we verify the correctness and accuracy of the dynamic
model. The experiments prove that the result of parameter identification has extremely high accuracy
and the dynamic model can accurately describe the 6R robot system mathematically. The dynamic
modeling method proposed in this paper can be used as the theoretical basis for the study of 6R robot
system dynamics and the study of dynamics-based control theory.
Keywords:
6R robot system; dynamical modeling; dynamic model; dynamic parameter identification;
graph theory
1. Introduction
For the manufacturing industry, which uses industrial robots for processing and
production, especially large-scale manufacturing such as aircraft manufacturing, there is a
dilemma whereby the absolute positioning accuracy of industrial robots is low and cannot
meet the accuracy requirements, which at the same time limits the development of robots
and manufacturing. The key to solving this problem is to establish an accurate dynamic
model of the robot, so as to achieve precise control and improve accuracy. Therefore, the
research of robot dynamics is of great significance both in theory and application.
Nowadays, with the development of the robot industry, the mobility, collaboration,
and adaptability of robots have become stronger, and the manufacturing industry, which
is the main application scenario of industrial robots, has shown a trend towards intelli-
gence [
1
]. As a large-scale manufacturing industry, many processes in the aviation industry
currently rely on manual work by workers [
2
], and the following problems need to be
resolved: the high labor intensity of workers leads to poor consistency, the limited working
time leads to low efficiency, and the fatigue caused by long-term labor leads to low preci-
sion. To meet the requirements of long life, fast cycle, high precision and low cost of aircraft,
industrial robots are urgently needed to replace workers’ positions [
3
,
4
]. The main task of
Appl. Sci. 2021, 11, 10988. https://doi.org/10.3390/app112210988 https://www.mdpi.com/journal/applsci
