Yanqi Wang
Papers
6
Total Citations
62
H-Index
4
About
Yanqi Wang is a leading researcher in intelligent power grid inspection, specializing in robotics, computer vision, and deep learning for transmission line maintenance. Wang's major contributions center on developing hybrid flying-walking inspection robots that can autonomously navigate and inspect high-voltage power lines in challenging environments. Their work addresses critical challenges in real-world deployment, including stable walking control under windy conditions using variable universe fuzzy control, and precise autonomous landing on transmission lines using prior structural data. Wang pioneered a synthetic dataset approach combined with improved YOLOv5 for fault detection in transmission line components, achieving 37 citations for this method that overcomes the chronic shortage of real fault images. They also advanced 3D reconstruction of power lines using Neural Radiance Fields (NeRF) with progressive motion sequence images, solving the problem of thin structure reconstruction. With a three-dimensional path-following control method based on improved Line of Sight, Wang has systematically enhanced the reliability and autonomy of power line inspection robots, making significant strides toward practical smart grid applications.
Research Focus
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