Design of Industrial Robot Visual Grasping System Based on Neural Network
Aiming at the problems of fixed workpiece position and low grasping efficiency caused by robot teaching programming method,this paper studies the application of neural network in robot vision recognition and grasping planning,establishes a vision guidance scheme,develops a vision recognition system through the YOLOV5 neural network model,identifies the types of objects,obtains the coordinates of the positioning points of the objects to be grasped,puts forward the robot six-point hand-eye calibration principle,carries out the calibration experiment,and proposes the positioning method for objects with circular or rectangular top view.Finally,the grabbing experiment is completed 180 times for three kinds of objects,and the overall average success rate of the experiments is about 92.8％,which verifies the possibility of practical application in the vision recognition and grabbing robot system and effectively improves the grabbing efficiency.
万方数据
维普
