Fan tower climbing robot navigation method based on multiple sensors
By combining a multi-source heterogeneous sensing system and dynamic path planning optimization with a digital twin system, the problems of low environmental perception accuracy and high energy consumption in existing wind turbine tower climbing robot navigation methods have been solved, achieving efficient and safe wind turbine tower detection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-16
- Publication Date
- 2026-03-31
AI Technical Summary
Existing navigation methods for wind turbine tower climbing robots based on multiple sensors have shortcomings in data processing and path planning, resulting in low accuracy and reliability of environmental perception, difficulty in quickly and comprehensively detecting wind turbine towers, high energy consumption, short battery life, and lack of flexibility when facing complex environments.
A multi-source heterogeneous sensing system is adopted, which integrates sensor data through dynamic weight adjustment algorithm and Transformer-based deep learning model to generate accurate environmental perception results. Climbing path is generated based on the three-dimensional curved surface geometry model of the tower. The path parameters are dynamically adjusted by combining real-time point cloud analysis and reinforcement learning algorithm. Force feedback and wind speed perception are introduced to adjust the adsorption force, and a digital twin system is constructed to realize remote monitoring and human-machine collaborative decision-making.
It improves the accuracy and reliability of environmental perception, optimizes the climbing path to reduce energy consumption, extends the battery life, and improves detection efficiency and safety through human-machine collaboration.
Smart Images
Figure CN121761894A_ABST