Visual inspection and grasping methods based on deep learning

Abstract

Aiming at the problems of existing robot grasping systems that have high hardware requirements, are difficult to adapt to different objects, and produce large harmful torques during the grasping process, a visual detection and grasping method based on deep learning is proposed. The channel attention mechanism is used to improve YOLO-V3, enhance the network's ability to extract image features, improve the effect of target detection in complex environments, and increase the average recognition rate compared with the original . Aiming at the problem of discreteness of the current pose estimation angle, a minimum area bounding rectangle (MABR) algorithm based on the main network embedded in the Visual Geometry Group 16 (VGG-16) is proposed to perform grasping pose estimation and angle optimization. The average error between the improved grasping angle and the actual angle of the target is less than , which greatly reduces the harmful torque applied by the two-finger manipulator to the object during the grasping process. A visual grasping system was built using UR5 robotic arm, pneumatic two-finger manipulator, Realsense D435 camera and ATI-Mini45 six-dimensional force sensor. Experiments show that the proposed method can effectively grasp and classify different objects, has low hardware requirements, and reduces harmful torque by about , thereby reducing damage to objects. It has good application prospects.