一种工业零部件混流装配用人形机器人自适应抓取系统及方法
An adaptive grasping system that combines deep learning material analysis and visual feedback solves the problem of grasping objects of various materials, achieving low-cost, high-safety flexible grasping and preventing damage and slippage of objects.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- YUNNAN OPEN UNIV
- Filing Date
- 2026-04-03
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies lack the ability to recognize the material properties of industrial parts with diverse physical attributes. This makes mechanically adaptable solutions prone to damaging objects, contact recognition solutions pose a risk of destruction and are inefficient, and traditional vision solutions ignore physical attributes and have high hardware costs for force control.
By employing deep learning material analysis algorithms and robot hand control algorithms, the system acquires images of components through optical image sensing cameras, performs material property analysis and vulnerability assessment, dynamically generates grasping strategies, and combines visual feedback for anti-slip closed-loop correction, thereby achieving non-contact material prediction and motion strategy generation.
Without the need for expensive force-sensitive sensors, it achieves low-cost, high-safety, and highly versatile flexible intelligent gripping, preventing object damage and slippage, and improving assembly line efficiency.
Smart Images
Figure CN121973246B_ABST