Papers
12
Total Citations
234
H-Index
8
About
A. Soriano is a researcher whose work sits at the intersection of robotics, control education, and intelligent systems, with a particular focus on resource-constrained mobile robots. Their most significant contributions lie in developing novel localization and sensor fusion techniques for robots with limited computational power, as demonstrated in their highly cited papers on event-based localization and multi-sensor fusion frameworks. Soriano pioneered the use of event-based Kalman filters to efficiently combine global sensor data with inertial measurements, dramatically improving localization accuracy while minimizing processing demands—a critical advancement for low-cost, real-world robotic platforms. Their work on low-cost platforms for automatic control education, which has garnered 58 citations, has made hands-on robotics and mechatronics learning accessible to students worldwide, bridging the gap between theory and practice. Soriano has also contributed to multi-agent systems for collision avoidance and the BRAIN-IoT framework for dependable IoT systems, showcasing a breadth of expertise from hardware to software. With over 200 total citations across their most influential papers, Soriano’s research continues to shape how limited-resource robots navigate and interact with their environments, making them a key figure in accessible, efficient robotics.
Research Focus
Key Achievements
Top Papers
- 1Low Cost Platform for Automatic Control Education Based on Open Hardware.58 citations · 2014
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- 6Multi-Agent Systems Platform for Mobile Robots Collision Avoidance13 citations · 2013
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- 8Application and evaluation of Lego NXT tool for Mobile Robot Control8 citations · 2011
- 9Collision Avoidance of Mobile Robots Using Multi-Agent Systems7 citations · 2013
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