Papers
8
Total Citations
218
H-Index
6
About
Danilo Alves de Lima is a Brazilian researcher whose work sits at the intersection of autonomous robotics, computer vision, and intelligent vehicle navigation. His research has made significant contributions to vision-based control systems, particularly in developing practical frameworks that enable autonomous vehicles to operate safely in complex urban environments. Lima's most influential contribution — cited 69 times — demonstrated innovative applications of Pearson's correlation coefficient for image processing in autonomous robotics, addressing the critical challenge of computational efficiency when handling redundant sensor data. Building on this foundation, he developed hybrid control architectures that combine visual servoing for lane following with dynamic window approaches for real-time obstacle avoidance, work that has accumulated over 54 citations and represents a meaningful advance in bridging deliberative and reactive robot control paradigms. His 2013 navigation framework using vector fields further solidified his reputation in autonomous vehicle guidance, earning 45 citations. Beyond autonomous vehicles, Lima has extended his expertise into Advanced Driver Assistance Systems (ADAS) focused on human-vehicle interaction, and more recently into agricultural machinery telemetry, reflecting a broader interest in intelligent systems across industries. With a career spanning over a decade of productive research, Lima's cumulative impact — totaling more than 200 citations — marks him as a notable contributor to applied robotics and autonomous systems engineering.
Research Focus
Key Achievements
Top Papers
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- 5A Visual Servoing approach for road lane following with obstacle avoidance16 citations · 2014
- 6Agricultural Machinery Telemetry: A Bibliometric Analysis8 citations · 2022
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- 8Sensor-based navigation applied to intelligent electric vehicles4 citations · 2015