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Objects in cluttered environments may have similar sizes and shapes, which remains a huge challenge for robot grasping manipulation. The existing segmentation methods, such as Mask R-CNN and Yolo-v8, tend to lose the shape details of objects when dealing with messy scenes, and this loss of detail limits the grasp performance of robots in complex environments. This paper proposes a high-performance grasp detection algorithm with a multi-target semantic segmentation model, which can effectively improve a robot’s grasp success rate in cluttered environments. The algorithm consists of two cascades: Semantic Segmentation and Grasp Detection modules (SS-GD), in which the backbone network of the semantic segmentation module is developed by using the state-of-the-art Swin Transformer structure. It can extract the detailed features of objects in cluttered environments and enable a robot to understand the position and shape of the candidate object. To construct the grasp schema SS-GD focused on important vision features, a grasp detection module is designed based on the Squeeze-and-Excitation (SE) attention mechanism, to predict the corresponding grasp configuration accurately. The grasp detection experiments were conducted on an actual UR5 robot platform to verify the robustness and generalization of the proposed SS-GD method in cluttered environments. A best grasp success rate of 91.7% was achieved for cluttered multi-target workspaces.
Zhong et al. (Sat,) studied this question.