一种基于人工智能的轨腰除锈机器人控制系统及方法
By using an AI-based rail web rust removal robot control system, and employing rust-stress fusion detection and multi-objective optimization algorithms, efficient, stable, and safe rust removal of rail webs in rail transit has been achieved. This solves the problems of low efficiency and poor consistency in existing technologies and protects the rail web substrate structure.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHEJIANG HAINING RAIL TRANSIT OPERATION MANAGEMENT CO LTD
- Filing Date
- 2026-05-21
- Publication Date
- 2026-07-17
AI Technical Summary
Existing rail web rust removal operations for rail transit are inefficient and have poor grinding consistency. They are difficult to balance the need to avoid cutting low-grade rust areas and to thoroughly remove high-grade rust areas. Furthermore, they lack global optimal parameter planning for the entire rust distribution, which fails to meet the surface roughness requirements of coating processes and poses risks of robot movement jamming and substrate damage.
An AI-based rail-waist rust removal robot control system is adopted. The system acquires a rust-stress fusion map through machine vision and stress sensing units, uses a deep learning model to identify the rust level and thickness, and calculates the optimal parameters for the first and second operations using a multi-objective optimization algorithm. This enables rust removal operations with fixed parameters throughout the process, and employs a dual-index judgment mechanism of residual thickness and surface roughness.
It improves the stability and safety of rust removal operations, protects the integrity of rail web substrate, enhances the consistency and efficiency of rust removal quality, adapts to changes in working conditions, and achieves continuous optimization.
Smart Images
Figure CN122231763B_ABST