About

R. Garrido is a prominent robotics and control systems researcher whose work spans visual servoing, neural network-based compensation, robot manipulator control, and parallel robotics. Over more than two decades, Garrido has made substantial contributions to the stability analysis and practical implementation of vision-based control systems, consistently addressing real-world challenges such as uncertain gravitational torques, camera calibration errors, and unknown friction dynamics. Among his most influential contributions is a series of works on stable visual servoing for planar robot manipulators, where he employed radial basis function neural networks to compensate for modeling uncertainties — a line of research that established robust theoretical foundations while remaining experimentally grounded. His 2022 paper on closed-loop parameter identification for robot manipulators has already garnered 35 citations, reflecting the continued relevance of his methodologies. Garrido has also advanced the control of parallel robots, developing PD controllers and strict Lyapunov-based stability proofs under uncertain camera orientations. His early work on web-based robotics education demonstrates a commitment to broadening access to the field. Collectively, his publications have accumulated over 130 citations, making him a respected voice in intelligent robot control and vision-guided automation research.

Research Focus

Key Achievements

8
H-Index
18
Papers
160
Total Citations
9
Avg Citations/Paper
🏆 Most Cited Paper
Stable robot manipulator parameter identification: A closed-loop input error approach
35 citations · 2022
📈 Most Prolific Year: 2006 (3 Papers)
🤝 Key Collaborators: 17
🏛 Institutions: Instituto Politécnico Nacional, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago