Papers
1
Total Citations
4
H-Index
1
About
Shaolei Xu is a researcher advancing intelligent manufacturing through vision-guided robotic welding systems. His work focuses on computer vision, 3D reconstruction, and automated weld seam detection, addressing critical challenges in machinery manufacturing, shipbuilding, and vehicle engineering. His most-cited paper, "Feature Point Identification in Fillet Weld Joints Using an Improved CPDA Method" (2023), introduces a novel approach for accurately identifying weld seam features and reconstructing their 3D positions, directly enhancing the performance of autonomous welding robots. With 4 citations to date, this work contributes to the broader goal of achieving precise, real-time guidance for industrial robots in complex environments. Xu’s research bridges the gap between theoretical computer vision algorithms and practical manufacturing applications, offering solutions that improve productivity and quality control. His contributions are particularly valuable for students and researchers exploring the integration of AI and robotics in industrial automation, where reliable feature detection remains a key bottleneck.
Research Focus
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Top Papers
- 1