Papers
9
Total Citations
71
H-Index
4
About
Yonglong Li is a leading researcher in intelligent robotic systems for hydraulic infrastructure inspection and maintenance. His work focuses on three key areas: underwater structural inspection in challenging environments, wall-climbing welding robots, and autonomous defect detection using deep learning. Li’s most impactful contribution is a 3D point cloud capture method for underwater structures in turbid environments (23 citations), which overcomes the limitations of traditional techniques affected by turbidity, low light, and distortion. He also developed an intelligent wall-climbing welding robot system (13 citations) for large-scale steel structure manufacture, integrating robot body, control, and welding subsystems. His image-based underwater inspection system for stilling basin slab abrasion (12 citations) and exposed aggregate detection using Attention U-Net networks (10 citations) demonstrate his pioneering use of AI for automated damage assessment. Li has further advanced the field with an automated robotic system for diversion tunnel defect inspection (3 citations) and a variable buoyancy system for hybrid crawler-flyer underwater vehicles (2 citations). His recent work on auto-heading control for over-actuated underwater vehicles using active disturbance rejection control (2023) continues to push boundaries in autonomous robotics for critical infrastructure.
Research Focus
Key Achievements
Top Papers
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