Marco Canova

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About

Marco Canova is a researcher at the forefront of embedded artificial intelligence and autonomous aerial robotics, with a particular focus on enabling intelligent, real-time decision-making on resource-constrained platforms. His primary research areas include Tiny Machine Learning (TinyML), computer vision for drones, and autonomous landing systems. Canova’s most notable contribution is the development of a plug-and-play TinyML-based vision system for drone automatic landing, a breakthrough that allows aerial robots to safely and accurately land without human intervention. This system, designed to be compatible with the widely used Pixhawk flight controller series, demonstrates how lightweight machine learning models can be deployed directly on low-power hardware, eliminating the need for heavy onboard computers. His work has already garnered attention, with his 2023 paper accumulating over 10 citations, signaling its growing impact in the field. By bridging the gap between advanced computer vision algorithms and practical, low-cost drone hardware, Canova is paving the way for more accessible and autonomous aerial systems, making him a rising voice in the TinyML and drone communities.