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.
Highlights
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
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 precision
Industrial robots can be divided into two types: serial robots and parallel robots
Summary
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. M et al [30] proposed an error method called the DIDIM method to identify the dynamic parameters of the dynamic equations and the new method was proved to be effective through an experiment with a 2-DOF rigid body robot. The matrix expression needs to decouple the dynamic parameters of the rigid bodies that make up the robot system to realize the modularization of the dynamic equations, which is convenient for computer solving and control. The vector matrix of force element of the 6R robot system is established and the resultant moment of the force elements on the rigid body are derived. The n × ne square matrix composed of Ciek is called the vector matrix of force element of the system, denoted as Ce. Ce is used to describe the distribution of force elements in each rigid body of the system.
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