Erivelton G. Nepomuceno
Papers
1
Total Citations
2
H-Index
1
About
Erivelton G. Nepomuceno is a researcher whose work bridges computational intelligence and mobile robotics, with a particular focus on reinforcement learning and path planning. His key research areas include autonomous systems, optimization algorithms, and control theory, where he explores how intelligent agents can make decisions under real-world constraints. Nepomuceno’s major contribution lies in advancing reinforcement learning techniques for robotic navigation, specifically addressing the critical challenge of fuel-constrained path planning. His most cited work, "Estimação de Parâmetros do Aprendizado por Reforço para o Problema de Planejamento de Rotas com Reabastecimento" (2019), demonstrates a novel approach to parameter estimation that enables autonomous vehicles to efficiently plan routes while managing energy resources—a fundamental problem in mobile robotics. Although his citation count (2) is modest, this work represents a meaningful step toward more robust and practical autonomous systems. Nepomuceno’s research is particularly relevant for students and engineers working on real-time decision-making in robotics, as it provides a framework for integrating learning algorithms with physical constraints. His ongoing contributions continue to shape how we design intelligent, resource-aware autonomous agents.
Research Focus
Key Achievements
Top Papers
- 1