MSGS:基于深度相机的多规格机器人抓取系统OA

[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 effi-ciency 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 materi-als.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 sce-narios.[Results]Experiments show that the detection model can successfully detect and classify small-feature tar-gets,achieving an accuracy rate of 98.3% and an mAP of 0.993.Through multiple positioning and grasping exper-iments,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.[Conclu-sions]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.