Papers
6
Total Citations
199
H-Index
4
About
Xiaorui Shi is a researcher whose work sits at the intersection of industrial automation, computer vision, and robotic systems. His most significant contribution to date is a two-stage industrial defect detection framework combining improved YOLOv5 and an optimized Inception-ResnetV2 model, a 2022 paper that has garnered over 120 citations and represents a meaningful advance in automated quality control for manufacturing environments. Shi has also made notable strides in robotic welding, developing point cloud-based path planning methods for complex geometries such as impeller blades and steel mesh — work that reduces the burden of manual robot programming and improves welding precision. His 2022 paper on impeller blade welding has accumulated 42 citations, underscoring its relevance to industrial robotics communities. Beyond perception and planning, Shi has contributed a review of fault-tolerant control strategies for robots, reflecting a broader interest in making robotic systems resilient in hazardous or unpredictable environments. Across his portfolio, Shi's research consistently addresses real-world manufacturing challenges — from defect detection to weld seam extraction — positioning him as a practical and applied voice in intelligent robotics and industrial automation.
Research Focus
Key Achievements
Top Papers
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- 4A Review on Fault-Tolerant Control for Robots7 citations · 2020
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