Papers
6
Total Citations
66
H-Index
4
About
Yinan Wang is a rising leader in intelligent robotic manufacturing, whose research centers on coverage path planning, robotic quality inspection, and industrial automation. Wang’s most impactful contribution is a novel coverage path planning framework that explicitly controls measurement uncertainty during robotic quality inspection—a critical advance for verifying dimensional specifications in sheet structures. This work, published in 2022, has already garnered 37 citations, reflecting its immediate relevance to precision manufacturing. Wang further extended this line of inquiry by developing distributed task allocation and sequential planning methods for multi-station, multi-robot coordinate assembly processes, addressing real-world challenges in complex production lines. In parallel, Wang has advanced robotic vision measurement systems through parameter identification and scanning pose optimization, tackling the persistent issue of measurement error in non-contact inspection. More recently, Wang introduced GeoContrast, a geometric knowledge-based contrast learning approach for industrial point cloud segmentation, enabling high-fidelity simulation environments for robotic process planning. With additional work on two-stage trajectory planning for online quality measurement and fast shaft hole assembly using 2D point cloud matching, Wang is systematically bridging the gap between theoretical robotics and practical, high-efficiency manufacturing solutions.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6