Rivelino Nunes
Papers
1
Total Citations
3
H-Index
1
About
Rivelino Nunes is a researcher at the forefront of intelligent robotics and autonomous systems, with a focused expertise in integrating reinforcement learning with digital twin technologies. His most-cited work, "Navigation robot training with Deep Q-Learning monitored by Digital Twin" (2022), introduces a groundbreaking approach to vehicular robot navigation. In this study, Nunes demonstrates how Deep Q-Learning can be applied to train a robot for part transportation within a constrained environment, while a Digital Twin provides real-time monitoring and validation. This synergy between virtual simulation and physical execution not only enhances training efficiency but also ensures safer, more reliable task completion. Though early in its citation impact, this work has already garnered 3 citations, signaling growing recognition among peers. Nunes’ contributions are pivotal for advancing autonomous navigation in industrial settings, bridging the gap between theoretical reinforcement learning and practical robotics. His research promises to reduce development costs and improve adaptability in dynamic environments, making him a rising voice in the field of cyber-physical systems and intelligent automation.
Research Focus
Key Achievements
Top Papers
- 1Navigation robot training with Deep Q-Learning monitored by Digital Twin3 citations · 2022