Categorization of Objects to Be Grasped Based on the Recs Concept and Grasping Task Using 3d Model Matching

This paper presents a practical grasping method for domestic robots based on the RECS (Robot Environment Compromise System) concept. The method classifies and registers the shapes and dimensions of household objects. A depth camera captures RGB images and point cloud data, while a YOLO-based deep learning model detects objects and estimates their positions. Subsequently, 3D model matching identifies each object's posture, enabling grasping at optimal registered points. The system is implemented on a mobile robot equipped with a dual-arm manipulator. Experimental results demonstrate the robot's ability to effectively grasp various household items, highlights its potential for real-world home and office robotic applications.