Runquan Xiao
Papers
17
Total Citations
505
H-Index
11
About
Runquan Xiao is a prominent researcher specializing in robotic welding automation, computer vision, and intelligent manufacturing systems. His work centers on developing advanced algorithms and sensing technologies that enable robots to autonomously detect, track, and execute complex welding tasks with minimal human intervention. Xiao's most significant contributions lie in vision-based seam tracking and path generation for robotic arc welding. His 2019 paper on adaptive feature extraction for multiple seam types has garnered 171 citations, establishing him as a leading voice in laser vision sensing for welding applications. He has consistently pushed the boundaries of automation through innovations including binocular vision guidance frameworks, Snake model-based feature extraction, and deep learning approaches such as Siamese network architectures for real-time seam tracking. His teaching-free welding methodology represents a particularly impactful contribution, eliminating the need for manual robot programming in industrial settings. More recently, Xiao has extended his research into multi-layer, multi-pass welding strategies leveraging point cloud processing and Transformer-based architectures for geometrically complex saddle-shaped seams. With over 460 cumulative citations across his top publications, his work is shaping the future of intelligent, fully autonomous robotic welding systems in advanced manufacturing.
Research Focus
Key Achievements
Top Papers
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