Shida Yao
Papers
2
Total Citations
7
H-Index
2
About
Shida Yao is a rising researcher in intelligent robotic welding and advanced manufacturing, whose work bridges computer vision, deep learning, and automation. His primary research areas include 3D scanning-based robotic trajectory planning, multi-layer multi-pass welding for large-scale components, and dual-modal perception systems that fuse 2D image recognition with 3D point cloud processing. Yao’s major contributions address critical limitations in traditional offline programming—particularly for large intersecting line workpieces with asymmetric profiles and significant machining deviations. His 2025 study on handheld 3D scanning-based trajectory planning (4 citations) introduces a method that generates accurate STL models and enables precise multi-layer multi-bead welding paths, significantly improving adaptability for complex industrial parts. In another 2025 work (3 citations), Yao proposes a dual-modal framework that integrates stereo vision and deep learning, overcoming challenges of redundant point cloud data and poor robustness in teaching-free welding. Though early in his career, Yao’s innovative fusion of sensing and planning technologies is poised to impact automated manufacturing, offering scalable solutions for high-precision, large-scale welding tasks. His work exemplifies the next generation of smart robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2