Alessandro De Gloria
Papers
4
Total Citations
85
H-Index
4
About
Alessandro De Gloria is a leading researcher in multi-robot systems and embedded artificial intelligence, with a focus on deploying machine learning on resource-constrained devices. His pioneering work on "Multi-robot search and rescue team" (2011, 60 citations) established foundational strategies for coordinating robot teams in GPS-denied indoor environments, addressing the critical challenge of locating and evacuating survivors in unknown, hazardous spaces. De Gloria has significantly advanced the field of edge AI through his investigation of "Memory Efficient Binary Convolutional Neural Networks on Microcontrollers" (2022, 10 citations), demonstrating how binarization techniques enable powerful neural networks to run on industrial microcontrollers with limited memory and processing power. His research on "Air-ground multi-agent robot team coordination" (2011, 10 citations) further explores the integration of aerial and ground robots for complex search and rescue missions. More recently, De Gloria has applied his expertise to "A Tiny CNN for Embedded Electronic Skin Systems" (2022, 5 citations), pushing the boundaries of tactile sensing for robotics. His work bridges the gap between theoretical machine learning and practical, real-world deployment in safety-critical applications.
Research Focus
Key Achievements
Top Papers
- 1Multi-robot search and rescue team60 citations · 2011
- 2Memory Efficient Binary Convolutional Neural Networks on Microcontrollers10 citations · 2022
- 3Air-ground multi-agent robot team coordination10 citations · 2011
- 4A Tiny CNN for Embedded Electronic Skin Systems5 citations · 2022