Felipe J. S. Vasconcelos
Papers
2
Total Citations
44
H-Index
2
About
Felipe J. S. Vasconcelos is a robotics researcher whose work sits at the intersection of system identification, control theory, and intelligent automation. His most influential contribution, "Identification by Recursive Least Squares With Kalman Filter (RLS-KF) Applied to a Robotic Manipulator" (2021), has earned 41 citations by pioneering a hybrid method that fuses recursive least squares estimation with Kalman filtering for real-time system identification. This approach enables robotic manipulators to learn and adapt their dynamic models on the fly, significantly improving control accuracy in industrial settings where precision is paramount. In related work on "Path Planning Collision Avoidance using Reinforcement Learning" (2020), Vasconcelos explores how reinforcement learning can generate collision-free trajectories without disrupting production workflows—a critical capability as factories demand both speed and safety. His research addresses the fundamental challenge of making industrial robots more autonomous and responsive, with direct applications in manufacturing, assembly, and quality control. By combining rigorous mathematical methods with practical engineering solutions, Vasconcelos is helping to bridge the gap between theoretical control systems and the real-world demands of modern robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Path Planning Collision Avoidance using Reinforcement Learning3 citations · 2020