Gui Fu

Southwest University of Science and Technology

Papers

Total Citations

H-Index

About

Gui Fu is a leading researcher in autonomous aerial robotics, specializing in visual servoing control and intelligent path planning for unmanned aerial vehicles (UAVs). His work addresses critical challenges in UAV motion control, particularly the problem of target loss and low control precision under field-of-view (FOV) constraints. Fu’s major contributions include pioneering the use of deep reinforcement learning for visual servoing, enabling UAVs to maintain robust target tracking even in complex, dynamic environments. He has also advanced fuzzy logic-based image-based visual servoing (IBVS) methods, enhancing the precision and adaptability of UAV positioning. With his most-cited paper accumulating 17 citations in just one year, Fu’s research has quickly gained traction in the robotics community. His work on reinforcement learning for mission path planning in dynamic environments further underscores his impact, offering practical solutions for real-world UAV operations. Fu’s innovative integration of learning-based and fuzzy control approaches marks him as a rising authority in the field, with his findings poised to influence next-generation autonomous systems.