Guangqing Chen

Shandong University of Science and Technology

Papers

Total Citations

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About

Guangqing Chen is a leading researcher in intelligent robotics for power infrastructure, specializing in deep learning-based visual perception and robotic manipulation for high-voltage transmission systems. His work addresses critical challenges in automating the inspection and maintenance of power grids, combining computer vision with mechanical design. Chen’s most cited paper (6 citations) introduces an innovative approach that integrates DeblurGANv2 with an improved YOLOv5 algorithm to accurately identify transmission line hardware, enabling line patrol robots to perform precise obstacle-crossing maneuvers. He has also contributed a comprehensive review of adsorption techniques for wall-climbing robots (4 citations), systematically analyzing the advantages and limitations of various methods to guide future design. In his latest work (2 citations), Chen designed and experimentally validated a novel climbing robot for power transmission towers, using Ansys analysis to optimize key components for reliable grasping. His research directly impacts the safety and efficiency of grid maintenance, offering practical solutions for real-world deployment. Chen’s work is essential reading for engineers and researchers developing autonomous systems for critical infrastructure inspection.