Design Of Multi-workpiece Sorting System Based On Machine Vision

With the advent of the "Industrial 4.0" era,it means that the future of the manufacturing company's highly integrated,intelligent automation and flexibility.The world's manufacturing plant will accelerate the realization of intelligent and efficient product manufacturing,more in line with market demand.At present,large and small robot companies at home and abroad have introduced a series of machine vision with industrial automation robot.Although the current R & D technology can not be compared with Europe and the United States and other developed countries,but for decades ago the industrial situation has made a qualitative leap.According to China's market demand,it is of great value to study industrial robots with machine vision.In this paper,a kind of workpiece sorting system based on machine vision under Windows and Linux development environment is proposed,which is systematically analyzed and experimentally verified.Firstly,the design of the hardware system and the software system is described.The general scheme design and the visual module platform of the manipulator control system are described.The opencv visual accelerator package and the Qt cross platform application development framework are introduced,and the architecture based on the modbus protocol And tcp / ip protocol network communication design and implementation methods.Then,the machine vision module is introduced in detail,including the outline of SCARA robot and the selection of image acquisition system components.The accuracy of the industrial camera are analyzed and calibrated,and the height calibration and camera distortion correction experiment are completed.Then the paper uses the image processing algorithm to extract and identify the workpiece under static state.Firstly,the image is enhanced by image enhancement,gray scale processing,noise removal,maximum interclass variance method threshold segmentation,image edge extraction and so on.Then,the center coordinates of the workpiece are calculated by using the least squares elliptic fitting operator.Then,takingout the rotation vector.Then,using the end-to-end improved convolutional neural network,and the image of the workpiece is divided into blocks in the network.Next,the convolutional network is calculated for all the blocks,and then weighted into an output value.According to the loss function,W and b will be updated,in order to achieve the classification of the workpiece.Finally,the coordinates of the main feature points of the workpiece contour are extracted,and the speed and acceleration of the workpiece movement can be obtained by the feature point tracking,and the position of the target in the future can be predicted.And for the future to achieve the mechanical arm to grasp the dynamic workpiece to make a theoretical basis for preparation.