Gibson Barbosa

Universidade Federal de Pernambuco

Papers

7

Total Citations

82

H-Index

6

About

Gibson Barbosa is a researcher specializing in robotics, computer vision, and intelligent systems, with a particular focus on human-robot collaboration and edge computing applications. His work sits at the intersection of deep learning and practical robotics deployment, addressing critical challenges in industrial safety and constrained-device computation. Barbosa's most influential contribution, "FCN-Pose" (2022, 20 citations), demonstrates his commitment to making deep learning viable on resource-limited IoT devices by developing pruned and quantized neural networks for robot pose estimation — a technically demanding achievement that bridges the gap between computational efficiency and accuracy. Complementing this, his framework for robotic arm pose estimation using deep and extreme learning models further advances the field of robot motion prediction. A significant thread throughout his research is ensuring safety in human-robot collaborative environments. Works such as "HOSA" and his collision detection studies (collectively accumulating over 40 citations) reflect a sustained effort to develop end-to-end safety architectures for industrial settings. His gripper design research additionally highlights a hands-on, hardware-oriented dimension to his scholarship. With a growing citation record across multiple high-impact publications, Barbosa represents an emerging voice in applied robotics and intelligent safety systems.

Research Focus

Key Achievements

6
H-Index
7
Papers
82
Total Citations
12
Avg Citations/Paper
🏆 Most Cited Paper
FCN-Pose: A Pruned and Quantized CNN for Robot Pose Estimation for Constrained Devices
20 citations · 2022
📈 Most Prolific Year: 2022 (3 Papers)
🤝 Key Collaborators: 19
🏛 Institutions: Universidade Federal de Pernambuco

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 15 days ago