Robot Plane Grasping Pose Detection Based on U2-Net

Abstract

Since the current grasping success rate of robots is low when performing grasping tasks in complex environments, in order to improve this problem, this paper proposes a robot grasping detection network SA-U2GNet combining U2-Net and Shuffle Attention networks. The network can not only achieve information communication between different sub-features through the attention mechanism, but also capture more contextual information from RGB-D images through the two-level nested U-shaped structure. Training and testing were performed on the Cornell and Jacquard grasp datasets, the accuracy rates reached 97.9% and 94.7% respectively, and the time required to process RGB-D images was 30ms. Compared with other methods, this method improves the accuracy and time efficiency, and the experiment verifies the feasibility and effectiveness of this method.