Papers

2

Total Citations

5

H-Index

2

About

Madson Rodrigues Lemos is a researcher at the forefront of intelligent robotics, specializing in the integration of reinforcement learning and digital twin technologies for autonomous navigation. His work centers on training robots to perform complex tasks, such as transporting parts within constrained environments, by leveraging the Deep Q-Learning algorithm. Lemos’s key contribution lies in demonstrating how this algorithm can be effectively applied to vehicular navigation, creating decision-making systems that enable robots to learn optimal movement and task execution through trial and error. Notably, his 2022 paper, "Navigation robot training with Deep Q-Learning monitored by Digital Twin," introduces a novel approach by pairing this learning process with a digital twin—a virtual replica of the physical system—allowing for safer, more efficient training and real-time monitoring. While his citation counts are currently modest (3 and 2 citations for his most-cited works), Lemos’s research is pioneering in its practical application of deep reinforcement learning to industrial robotics, laying important groundwork for future advancements in autonomous systems and smart manufacturing.

Research Focus

Key Achievements

2
H-Index
2
Papers
5
Total Citations
3
Avg Citations/Paper
🏆 Most Cited Paper
Navigation robot training with Deep Q-Learning monitored by Digital Twin
3 citations · 2022
📈 Most Prolific Year: 2022 (1 Papers)
🤝 Key Collaborators: 8
🏛 Institutions: Instituto Nacional de Pesquisas da Amazônia, Universidade Federal do Amazonas

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 16 days ago