Manuel Gomes
Papers
3
Total Citations
23
H-Index
2
About
Manuel Gomes is a researcher at the forefront of advancing human-robot collaboration, with a primary focus on developing safe, intuitive, and sensor-rich manufacturing environments. His work centers on the calibration and integration of multi-modal sensor systems—including 3D volumetric monitors and visual recognition tools—to create collaborative industrial cells where robots and humans can work side-by-side safely. Gomes’s major contributions include the design of a sensor-to-pattern calibration framework that ensures accurate geometric transformations between diverse sensors, a critical step for reliable perception in dynamic workspaces. His most cited paper (14 citations) establishes this framework for multi-modal industrial cells, while his subsequent work on a learning-based collaborative cell (7 citations) demonstrates how these systems can be made more versatile and user-friendly through adaptive interaction abilities. Gomes also developed the ATOM Calibration Framework (2 citations), which provides intuitive visualization and interaction tools for calibrating complex sensor arrays. His research is not only technically rigorous but also practically oriented, aiming to bridge the gap between laboratory innovation and real-world industrial deployment. Through his work, Gomes is helping to define the next generation of flexible, safe, and intelligent manufacturing systems.
Research Focus
Key Achievements
Top Papers
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