Felipe Buele
Papers
1
Total Citations
3
H-Index
1
About
Felipe Buele’s research lies at the intersection of robotics, computer vision, and intelligent automation, with a focus on enhancing real-world object classification systems. His most-cited work, “Color Classification Using a 3-DOF Robotic Arm Based on the YOLOv5 Model” (2024, 3 citations), demonstrates a practical integration of deep learning and mechatronics: a three-degree-of-freedom robotic arm that leverages the YOLOv5 architecture to sort objects by color with improved accuracy and efficiency. This contribution addresses a key industrial challenge—combining vision-based perception with precise robotic manipulation—and highlights Buele’s ability to bridge algorithmic advances with tangible automation solutions. While his citation count is still growing, the work signals a promising trajectory in applied AI and robotics. Buele’s research is particularly relevant for students and engineers exploring low-cost, scalable approaches to smart manufacturing and automated sorting systems.
Research Focus
Key Achievements
Top Papers
- 1Color Classification Using a 3-DOF Robotic Arm Based on the YOLOv5 Model3 citations · 2024