Abstract:

[Objective] Machine vision is widely used in industrial scenarios for material recognition and sorting in production processes. However, some current grasping methods suffer from low efficiency and limited adaptability to single specifications. [Methods] This study proposes a Multi-Specification Grasping System (MSGS) for robots, which optimizes the YOLOv5 model to detect and classify various materials. The system can adaptively control the extension distance of the robotic arm's end-effector based on the size of different types of materials, thereby accommodating sorting tasks for materials of various sizes in different scenarios. [Results] Experiments show that the detection model can successfully detect and classify small-feature targets, achieving an accuracy rate of 98.3% and an mAP of 0.993. Through multiple positioning and grasping experiments, the robotic arm's end-effector can adjust its extension distance according to the material specifications and complete grasping actions with a success rate of 100% and an average contraction error of 5mm. [Conclusions] This grasping system can effectively perform classification, positioning, and grasping of materials with multiple specifications, providing valuable technical insights for the advancement of industrial vision.

Key words: machine vision, multiple specifications, target detection, random grasping, depth camera, system