Automatic control system and method of storage robot
By deeply learning encoding and analysis of path length and spatial distribution characteristics in the automated control system of the warehousing robot, the problem of insufficient prediction accuracy in traditional methods is solved, and more efficient time prediction and transportation task allocation is achieved.
Patent Information
- Application Number
- CN202510185671.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-20
- Publication Date
- 2025-03-21
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
When predicting the time when the robot arrives at the shelf, the traditional warehousing robot automation control method is limited by the locality of the receptive field of traditional CNN and the neglect of factors such as path length, resulting in insufficient prediction accuracy.
The data analysis and analysis algorithm based on deep learning is used to encode the path length alone, and the spatial distribution matrix of the storage robot is hollow convolutional encoding. Through the cross-modal spatiotemporal interaction representation between the low-dimensional embedded features of the path length and the topological features of the storage robot's spatial distribution, the time when the robot arrives at the shelf is intelligently predicted.
By comprehensively considering the location data and path length of the storage robot, the complex relationship between the two is captured, which improves the accuracy of time prediction and ensures the ability to select the optimal robot to perform transportation tasks.