Papers
89
Total Citations
1,250
H-Index
21
About
Paulo Drews is a robotics researcher whose work spans autonomous underwater vehicles, hybrid aerial-aquatic systems, and intelligent navigation — areas where he has made consistently influential contributions. His research is perhaps best known for advancing Simultaneous Localization and Mapping (SLAM) in underwater environments, most notably through DolphinSLAM, a biologically inspired system extending mammalian navigation principles to 3D aquatic settings, which has accumulated over 60 citations alongside his broader underwater SLAM survey work. Drews has also pioneered the development of Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs), producing highly cited studies on attitude control, propeller configuration, and novel vehicle concepts like the HyDrone — work that collectively reflects a deep integration of dynamic modeling and robust control theory. His contributions extend into deep learning for robotics, including vision-based obstacle avoidance for AUVs and deep reinforcement learning for mapless 3D UAV navigation. Early work on sensor fusion calibration between 3D laser range finders and cameras further demonstrates his breadth across perception systems. With multiple papers exceeding 40 citations, Drews has established himself as a significant voice in autonomous robotics research, bridging theoretical modeling with practical, real-world applications.
Research Focus
Key Achievements
Top Papers
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- 2An Open-source Bio-inspired Solution to Underwater SLAM★63 citations · 2015
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- 8Vision-Based Obstacle Avoidance Using Deep Learning40 citations · 2016
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- 10Autonomous Agricultural Sprayer using Machine Vision and Nozzle Control35 citations · 2021