Andrea Albanese

Papers

Total Citations

H-Index

About

Andrea Albanese is a researcher at the forefront of embedded artificial intelligence and autonomous aerial robotics, with a particular focus on TinyML-driven vision systems. Her most notable contribution is the development of a plug-and-play TinyML-based vision system for drone automatic landing, a breakthrough that enables aerial platforms to land safely and accurately without human intervention. This work, published in 2023, has already garnered 10 citations, reflecting its timely impact on the growing field of edge AI for robotics. By designing a system compatible with the widely used Pixhawk flight controller series, Albanese has made advanced autonomous landing capabilities more accessible to the drone community. Her research bridges the gap between machine learning efficiency and real-time hardware constraints, addressing critical challenges in power-constrained, resource-limited environments. Albanese’s work is particularly valuable for students and researchers exploring practical applications of TinyML in autonomous systems, as it demonstrates how lightweight neural networks can be deployed on low-cost hardware to achieve robust, real-world performance. Her contributions are paving the way for safer, more autonomous drone operations in applications ranging from delivery to surveillance.