Detection method of robot grasp based on lightweight network

When the robot arm uses the suction cup to grasp the task, it is faced with an unstructured scene, and it is difficult to accurately calculate the grasping posture of the robot due to the irregular placement of the object and its irregular shape. To solve this problem, a grasping detection method of manipulator based on lightweight convolutional neural network was proposed. Firstly, the Mobile Net-YOLOV4 algorithm based on lightweight convolutional neural network was used to detect the target object in the image, and the classification and location information of the target were obtained. Then according to the final detection results of the image threshold segmentation, the anchor point is corrected, and finally the corrected positioning result is obtained. The grasping experiment was carried out on the Probot anno manipulator platform. The experimental results show that, compared with other image processing methods, the proposed method can realize fast detection and location of irregular target objects, and has better robustness for the diversity of object morphology and environment.