Target recognition and grasping strategies for soft robotic manipulators in unstructured environments.

In unstructured environments, robots face challenges in efficiently and accurately grasping irregular, fragile objects. To address this, this paper introduces a soft robotic hand tailored for such settings and enhances You Only Look Once v5s (YOLOv5s), a lightweight detection algorithm, to achieve efficient grasping. A rapid pneumatic network-based soft finger structure, broadly applicable to various irregularly placed objects, is designed, with a mathematical model linking the bending angle of the fingers to input gas pressure, validated through simulations. The YOLOv5s model is improved by integrating the Coordinate Attention (CA) mechanism in the backbone layer, refining the Spatial Pyramid Pooling (SPP) module for faster detection, and adjusting the loss function to prevent misalignment between predicted and actual bounding boxes, thereby enhancing computational efficiency. Experimental comparative analysis indicates that the refined model exhibits improvements in both mean average precision and recognition speed. A soft robotic grasping experimental platform was established, with precision grasping and power grasping experiments conducted using the pose and object type data generated by the enhanced YOLOv5s-CA-SPP model network. The results show that the success rate of grabbing reaches 82% with a proper grabbing posture.