Papers
2
Total Citations
14
H-Index
1
About
Emanuele Cereda is a researcher at the forefront of autonomous micro-robotics and embedded artificial intelligence. His primary research areas include deep learning for resource-constrained systems, swarm robotics, and onboard computer vision for nano-scale drones. Cereda’s most notable contribution is the development of an ultra-low power, end-to-end monocular relative localization system for nano-quadrotors—sub-40g drones with sub-100mW processing capabilities. This work, published in 2023 and already cited 13 times, demonstrates how deep neural networks can enable peer-to-peer localization without external infrastructure, a critical enabler for autonomous swarm behaviors. By pushing the boundaries of what is computationally possible on severely limited hardware, Cereda has opened new pathways for distributed sensing and coordination in miniature aerial robots. His work is particularly impactful for applications in search-and-rescue, environmental monitoring, and defense, where tiny, energy-efficient drones must operate collaboratively. Cereda’s achievements highlight a rare combination of algorithmic innovation and systems engineering, making him a rising figure in the intersection of embedded AI and field robotics.
Research Focus
Key Achievements
Top Papers
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