Spinning threading action optimization method and system based on path deep learning

By constructing a path generation network for the robotic arm that optimizes the constraints of tension-torque, motion acceleration, and obstacle avoidance safety distance, the problem of insufficient robustness in the spinning and threading trajectory planning method is solved, and fully automated threading with high success rate and stability is achieved.

CN121798641APending Publication Date: 2026-04-07JIANGSU WEI RUIXIN ROAD TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202610282089.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-10
Publication Date
2026-04-07

AI Technical Summary

Technical Problem

Existing spinning threading trajectory planning methods are difficult to adapt to the dynamic uncertainties of the yarn itself and the production environment, resulting in insufficient robustness of the generated actions, easy execution failures, and difficulty in meeting the requirements of high success rate and high stability fully automated threading.

Method used

By acquiring the continuous three-dimensional point cloud sequence of the yarn breakage area, the maximum tensile stress that can be withstood, the yarn diameter, and the dynamic parameters of the robotic arm, tension-torque constraints, motion acceleration constraints, and obstacle avoidance safety distance constraints are constructed. The robotic arm path generation network is then optimized to generate an optimized threading trajectory.

Benefits of technology

It significantly improves the first-time success rate and trajectory stability of the threading action, reduces failures caused by breakage, slippage, or collision, and meets the full automation requirements of spinning production.

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Abstract

The invention provides a spinning threading action optimization method and system based on path deep learning, and the method comprises the steps: firstly obtaining a continuous three-dimensional point cloud sequence of a yarn broken end region, yarn physical attributes and mechanical arm dynamic parameters, and then constructing a tension-moment constraint related to the tensile strength of yarn according to the physical attributes and the dynamic parameters, meanwhile, on the basis of the three-dimensional point cloud sequence, a motion acceleration constraint associated with the anti-jitter capacity and an obstacle avoidance safety distance constraint associated with the obstacle distribution density are constructed, and then a loss function of a pre-constructed mechanical arm path generation neural network is optimized according to the three kinds of dynamic constraints linked with a scene; according to the invention, the first success rate and the track stability of the threading action can be improved, the threading failure caused by snapping, throwing or collision is reduced, the threading time is shortened on the premise of ensuring the safety of yarns, and the reliability of spinning operation is improved.
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