Yongkang Fu

Shanghai University of Engineering Science

Papers

Total Citations

H-Index

About

Yongkang Fu is a robotics researcher whose work focuses on advancing vision-guided robotic manipulation, particularly in precision assembly tasks. His key research areas include pose estimation, computer vision, and robotic automation for industrial applications. Fu's major contribution lies in addressing the critical challenge of hole pose ambiguity during peg-in-hole assembly tasks—a fundamental problem in automated manufacturing. His most cited work proposes an innovative method combining the PnP (Perspective-n-Point) algorithm with contour depth extraction, enhanced by YOLOv5-based scene segmentation, to resolve pose estimation ambiguities in unstructured environments. This approach significantly improves the accuracy and reliability of vision-guided robotic arms in complex assembly scenarios. While his citation count is currently modest (3 citations for his top paper), this reflects the recency of his work (2024) rather than its potential impact. Fu's research bridges the gap between theoretical computer vision algorithms and practical industrial robotics, offering solutions that could streamline automated assembly lines. His work is particularly relevant for researchers and engineers working on Industry 4.0 applications, where precise robotic manipulation remains a bottleneck for full automation.