Research On Robot Optimal Control Of Industrial Production Line

Since the idea of "Industry 4.0" was put forward,industrial automation assembly line technology has led the storm of the fourth industrial revolution.The role of industrial robots in the assembly line is also increasing.The introduction of vision systems on the robot arms of industrial lines has become a hot spot of the times.The vision systems of robot arms have been widely used in the processing of industrial lines.This article analyzes the development process of the current robotic arm sorting system,focuses on the design based on the visual sorting process,learns the kinematics,image processing,visual servoing of the robotic arm,and uses the laboratory's IRB120 small six-degree-of-freedom machine The arm builds a vision-based modular sorting line system,through which the process of sorting and storing different color workpieces is realized.In order to accomplish this task,the following researches have been carried out in this paper:Analyze the trajectory of the robot arm.By studying the application of visual servo technology in the pipeline,the visual servo structure in this paper is determined;the kinematics of the robotic arm in the industrial pipeline is analyzed,and the kinematics model is established by the DH parameter algorithm,and the forward and inverse kinematics equations of the robotic arm are solved,and In the MATLAB simulation environment,the polynomial interpolation method is used to solve the motion trajectory of the manipulator.Set up the vision system of the robot arm in the industrial assembly line and process the image.The vision system in this paper uses a servo structure based on position control.From various calibration methods,the algorithm with high calibration accuracy-Zhang Zhengyou calibration method is used to calibrate the vision camera.The smoothing and sharpening processes are used to complete the image preprocessing,and an improved adaptive filtering method is proposed to smooth the workpiece image.First-order and second-order differential algorithms are used to achieve image sharpening.Through learning,MATLAB is used to simulate Roberts operator,Sobel operator,Prewitt operator,and Laplacian operator.The simulation results of various operators are compared and selected.Optimal extraction of edge operators.Use image feature extraction and color recognition to complete the sorting process of the assembly line.This paper uses two methods of threshold segmentation and cluster segmentation to distinguish the background and target.According to the characteristics of the workpiece,the parameters of the workpiece are extracted through connected area labeling and Harris focus detection,and the parameters such as the position coordinates and side length of the workpiece are obtained.Two methods,color recognition method based on extraction threshold and Alex Net neural network model,are used to identify the color of the workpiece.Complete the design and experiment of a modular sorting line system.The sorting and storage process of the workpiece is completed by the visual sorting unit of the robotic arm.After several experiments,this system has a high value for use,and can be used for rapid visual sorting tasks in industrial assembly lines.