Papers
5
Total Citations
59
H-Index
4
About
Vagner Rosa is a researcher whose work spans intelligent control systems, robotic actuators, and vision-based automation, with particular expertise at the intersection of machine learning and precision robotics. His most influential contributions address one of robotics' persistent challenges: nonlinear friction in harmonic drive actuators. His 2003 and 2004 papers — garnering 19 and 18 citations respectively — pioneered neural network-based friction compensation mechanisms that significantly improved the accuracy and stability of robotic manipulators, including those with flexible links. Building on this foundation, his 2007 work extended the approach using adaptive neuro-fuzzy systems, combining the learning capabilities of neural networks with the interpretability of fuzzy logic for more robust real-world performance. Beyond actuator control, Rosa has made meaningful contributions to industrial robotics, particularly in shipyard welding automation. His vision-based measurement (VBM) systems, developed across his 2015 and 2016 publications, demonstrated practical solutions for groove detection and automated weld control using CMOS cameras and FPGA hardware — earning 14 citations for the welding measurement study alone. Taken together, Rosa's body of work reflects a consistent drive to translate advanced computational intelligence into tangible engineering solutions for precision manufacturing and automation environments.
Research Focus
Key Achievements
Top Papers
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- 2A new approach to compensate friction in robotic actuators18 citations · 2004
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