细纱断头捻接作业机器人的原纱纱头精准定位方法及系统
By monitoring yarn static electricity in real time and using deep neural networks to predict dynamic deviation vectors, the robot path and gripping force are dynamically adjusted, solving the problem of inaccurate yarn end positioning caused by static interference and improving yarn end positioning accuracy and splicing success rate.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- DONGHUA UNIV
- Filing Date
- 2026-04-27
- Publication Date
- 2026-07-17
AI Technical Summary
In textile production, electrostatic interference can cause inaccurate yarn end positioning, affecting the splicing quality. Existing technologies make it difficult to monitor and dynamically adjust robot operation strategies in real time to overcome electrostatic interference.
By monitoring the static electricity data of the yarn surface in real time using electrostatic sensors and combining it with a deep neural network model to predict the dynamic deviation of the yarn end positioning caused by electrostatic interference, the robot's operation path and gripping force are dynamically optimized to ensure accurate positioning.
It effectively suppresses yarn tip deviation caused by static electricity and environmental fluctuations, improves yarn tip positioning accuracy and splicing success rate, and reduces the risk of yarn breakage and splicing failure.
Smart Images
Figure CN122082173B_ABST