Daniele Ragone
Papers
1
Total Citations
3
H-Index
1
About
Daniele Ragone is an emerging researcher specializing in autonomous systems, mobile robotics, and intelligent control, with a particular focus on the intersection of deep reinforcement learning and automated driving technologies. His work addresses critical challenges in the development of scalable robotic platforms for indoor applications, including warehousing, manufacturing, and automated driving evaluation environments. Ragone's most recognized contribution, "Modeling, Positioning, and Deep Reinforcement Learning Path Following Control of Scaled Robotic Vehicles" (2024), demonstrates his commitment to bridging theoretical control frameworks with real-world experimental validation — a technically demanding approach that strengthens the practical relevance of his findings. In this work, he advances path following control methodologies by leveraging deep reinforcement learning to generate precise steering commands, contributing meaningfully to the autonomous vehicle research community. While still in the early stages of his academic career, with his leading paper accumulating 3 citations shortly after publication, Ragone's research trajectory suggests a promising future in robotics and autonomous systems. His emphasis on experimental validation distinguishes his work, positioning him as a rigorous and application-driven contributor to the field of intelligent mobile robotics.
Research Focus
Key Achievements
Top Papers
- 1