Daniel Magree

Papers

Total Citations

H-Index

About

Daniel Magree is a robotics researcher whose work centers on autonomous aerial systems, particularly vision-based navigation and control for micro air vehicles (MAVs). His most cited paper, "Vision-based closed-loop tracking using micro air vehicles" (2016, 10 citations), details the target detection and tracking architecture developed for the Georgia Tech Aerial Robotics team. This system enabled vision-aided navigation, allowing MAVs to detect and track targets autonomously—a critical capability for real-world applications like search-and-rescue and surveillance. Magree’s contributions were instrumental in the team’s success at the American Helicopter Society (AHS) Micro Aerial Vehicle challenge, showcasing how onboard vision can replace GPS-dependent navigation in cluttered environments. His work also includes the "Georgia Tech Team Entry for the 2011 AUVSI International Aerial Robotics Competition" (2011, 2 citations), highlighting his early involvement in competitive robotics. By integrating computer vision with closed-loop control, Magree has advanced the practicality of small, agile drones, making his research a foundation for students and engineers exploring autonomous flight and visual servoing.