Linyi Fang

Civil Aviation Flight University of China

Papers

Total Citations

H-Index

About

Linyi Fang is a researcher advancing the field of autonomous aerial robotics, with a primary focus on visual servoing control for unmanned aerial vehicles (UAVs). Their work addresses critical challenges in vision-based UAV navigation, particularly the problem of maintaining target visibility under field-of-view (FOV) constraints. Fang's most-cited paper (2023, 17 citations) introduces a deep reinforcement learning framework for visual servoing that enables UAVs to robustly track objects even when the target risks leaving the camera's FOV, overcoming a key limitation in traditional image-based control methods. Their second major contribution (2023, 10 citations) develops a fuzzy logic-based approach to image-based visual servoing (IBVS), offering a more adaptive and computationally efficient alternative to conventional control techniques. Together, these works demonstrate Fang's expertise in integrating machine learning with classical control theory to solve real-world UAV motion control problems. By combining deep reinforcement learning and fuzzy logic with visual feedback systems, Fang has made significant strides in improving the reliability and precision of autonomous drone operations, with direct applications in surveillance, inspection, and search-and-rescue missions. Their research continues to shape the next generation of intelligent, vision-guided aerial systems.