Vicente Ferreira de Lucena
Universidade Federal do Amazonas, Federal Center for Technological Education Celso Suckow da Fonseca
Papers
7
Total Citations
57
H-Index
3
About
Vicente Ferreira de Lucena is a Brazilian computer scientist and engineering educator based at the Federal University of Amazonas (UFAM), whose work sits at the intersection of software engineering education, robotics, and intelligent navigation systems. He has made enduring contributions to technology-enhanced learning, most notably through pioneering an international education transfer initiative between Germany and Brazil that reimagined how software engineering is taught to electrical engineering undergraduates — a project that has garnered 19 citations and stands as a landmark in Latin American engineering education reform. His subsequent research on applying SCRUM methodologies to graduate-level project management (17 citations) demonstrated practical pathways for preparing students to collaborate effectively in real-world development environments. Lucena has also championed the use of robotics as a motivational pedagogical tool in computing education (10 citations), a thread he carried forward during the COVID-19 pandemic through his Learning-IoT framework for remote robotics instruction. In parallel, his technical research explores robot navigation, marrying deep reinforcement learning — particularly Deep Q-Learning — with digital twin monitoring, and employing computer vision techniques such as Shi-Tomasi and KLT for indoor wheelchair localization. Across these domains, Lucena's career reflects a consistent commitment to bridging theory and practice in engineering education and autonomous systems research.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3An experience to use robotics to improve Computer Science learning10 citations · 2009
- 4Learning-IoT: Methodological Framework for Remote Robotics Teaching3 citations · 2022
- 5Indoor visual localization of a wheelchair using Shi-Tomasi and KLT3 citations · 2017
- 6Navigation robot training with Deep Q-Learning monitored by Digital Twin3 citations · 2022
- 7Robot Training and Navigation through the Deep Q-Learning Algorithm2 citations · 2021