[Retracted] Dynamic Simulation Modeling of Industrial Robot Kinematics in Industry 4.0

Abstract

This paper studies the kinematic dynamic simulation modeling of industrial robots in the Industry 4.0 environment and guides the kinematic dynamic simulation modeling of industrial robots in the Industry 4.0 environment in the context of the research. To address the problem that each parameter error has different degrees of influence on the end position error, a method is proposed to calculate the influence weight of each parameter error on the end position error based on the MD-H error model. The error model is established based on the MD-H method and the principle of differential transformation, and then the function of uniform variation of six joint angles with time t is constructed to ensure that each linkage geometric parameter is involved in the motion causing error accumulation. Through the analysis of the robot marking process, the inverse solution is optimized for multiple solutions, and a unique engineering solution is obtained. Linear interpolation, parabolic interpolation, polynomial interpolation, and spline curve interpolation are performed on the results after multisolution optimization in the joint angle, and the pros and cons of various interpolation results are analyzed. The trajectory planning and simulation of industrial robots in the Industry 4.0 environment are carried out by using a special toolbox. The advantages and disadvantages of the two planning methods are compared, and the joint space trajectory planning method is selected to study the planning of its third and fifth polynomials. The kinetic characteristics of the robot were simulated and tested by experimental methods, and the reliability of the simulation results of the kinetic characteristics was verified. The kinematic solutions of industrial robots and the results of multisolution optimization are simulated. The methods, theories, and strategies studied in this paper are slightly modified to provide theoretical and practical support for another dynamic simulation modeling of industrial robot kinematics with various geometries.

A workload identification method of industrial robot combining dynamic model and convolutional neural network

“…In the previous studies, the existing mathematical method for identifying the dynamics parameters of an nonlinear characteristics of robots and high dimensions of operating data with a small sample are not mature. It is difficult to effectively identify the input or output parameters of high-dimensional nonlinear systems by the intelligent algorithms based on CNN with a small sample data [14]. Therefore the contributions of this paper are to carry out a method for the dynamic load prediction of a multi-degree of freedom robot with a small sample data combining preliminary robot dynamic model and CNN method, and to develop an improved mixing combination method to decrease the identified error.…”

“…Deep learning has been widely used in many fields, such as medical analysis [7,8], image recognition [9], structural analysis [4], target optimization [10,11], and so on. Although the process of machine learning is analogous to the learning process of the human brain, the exploration of its learning mechanism and the generalization ability of the model for unknown data still need to be relied on large sample data [12][13][14]. For a joint type robot with strong nonlinearity, the robot's dynamic parameters maybe change under different tasks.…”

A positioning method of mobile manipulator based on binocular vision

“…The mobile manipulator is composed of a three-degree-of-freedom mobile platform and a six-degree-of-freedom manipulator, with a total of nine degrees of freedom. The forward kinematics [4] operation is performed according to the structural parameters of the mobile manipulator, as shown in the following equation ( 1 (1)…”

Retracted: Dynamic Simulation Modeling of Industrial Robot Kinematics in Industry 4.0

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”