Papers
32
Total Citations
657
H-Index
12
About
Fengshui Jing is a prominent researcher specializing in robotic welding intelligence, computer vision, and human-robot interaction. His work sits at the intersection of industrial automation and machine learning, with a particular focus on equipping robots with the perceptual and adaptive capabilities needed for autonomous manufacturing tasks. Jing's most significant contributions center on intelligent weld seam detection and tracking. His development of deep learning architectures — including the Shuffle-YOLO-based feature extraction method and WeldNet — has advanced the field of automated seam recognition, earning over 100 combined citations. Equally impactful is his pioneering work on sensorless hand-guiding for industrial robots (79 citations), enabling intuitive teach-by-hand programming without costly force sensors — a breakthrough for practical shopfloor deployment. His contributions to structured light vision calibration and 3D seam extraction have addressed long-standing challenges in robotic eye-in-hand systems, collectively attracting over 130 citations across multiple papers. His flexible hand-eye calibration technique, requiring no specialized rigs, further demonstrates his commitment to practical, deployable solutions. With a body of work spanning from foundational sensor calibration to cutting-edge neural network applications, Jing's research has meaningfully shaped the trajectory of intelligent robotic welding, making him an essential reference for engineers and researchers working in smart manufacturing.
Research Focus
Key Achievements
Top Papers
- 1Automatic recognition system of welding seam type based on SVM method83 citations · 2017
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- 10Reconstruction-Based Hand–Eye Calibration Using Arbitrary Objects22 citations · 2022