Papers
4
Total Citations
107
H-Index
4
About
Adrian Manzanilla is a robotics and control systems researcher whose work centers on autonomous underwater vehicles (AUVs), with particular emphasis on real-time navigation, computer vision, and robust control design. His most influential contribution, "Autonomous Navigation for Unmanned Underwater Vehicles: Real-Time Experiments Using Computer Vision" (2019), has garnered 82 citations and represents a significant advance in the field by integrating parallel tracking and mapping (PTAM) with an Extended Kalman Filter to achieve vision-based localization using a single camera — a notably cost-effective and practical solution for underwater autonomy. This work has become a key reference for researchers tackling the inherent challenges of GPS-denied underwater environments. Beyond navigation, Manzanilla has made meaningful contributions to AUV design and control theory, developing nonlinear adaptive algorithms and robust PD controllers capable of handling real-world perturbations and model uncertainties. His 2017 paper on AUV-UMI design further demonstrates his hands-on approach to translating theoretical frameworks into functional robotic platforms. Across his body of work, Manzanilla consistently bridges the gap between control mathematics and experimental validation, making his research especially valuable to engineers and students working on real-world autonomous underwater systems.
Research Focus
Key Achievements
Top Papers
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- 2Design and Control of an Autonomous Underwater Vehicle (AUV-UMI)10 citations · 2017
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