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Research Report on the Application of Maintenance Robots for Coating Offshore Wind Turbine Blades

Author(s)

Junjie Qin*, Shuo Feng, Xinyu Long, Xianbin Fang, Yong Zheng, Shengjie Hu

Affiliation(s)

College of Electrical Engineering, Southwest Minzu University, Chengdu, China *Corresponding Author.

Abstract

Traditional manual and drone coating maintenance methods have problems such as low efficiency, high risk, and uneven spraying, which are difficult to meet the operation and maintenance needs of offshore wind farms. To address this challenge, this study proposed an intelligent operation platform that integrates a tracked vacuum adsorption mobile chassis, multi-sensor fusion and hierarchical control system. The robot system uses Jetson Nano as the main control device (equipped with the EfficientDet-D7x model), combined with the STM32F103ZET6 auxiliary control unit to achieve full-process coordination of damage detection, motion control and spray repair. Among them, Jetson Nano has 128 The GPU acceleration capability of the CUDA core achieves 98.7% mAP accuracy (48 FPS) in blade damage detection, which is a significant performance improvement over the Raspberry Pi (85% mAP, 12 FPS); the crawler vacuum adsorption chassis optimizes motion control through the fuzzy PID algorithm, increasing the response speed by 30.5%. Combined with the vacuum pump to dynamically adjust the adsorption force, it can move stably on the slippery and tilted blade surface; the STM32 unit processes IMU and ultrasonic sensor data in real time, and interacts efficiently with Jetson Nano through the UART protocol to ensure the accuracy of the robot arm's spray path planning.

Keywords

Climbing Robot; Crawler Vacuum Adsorption Structure; Efficientdet Target Detection Algorithm; Jetson Nano; STM32F103ZET6

References

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