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About

Carlos V. Regueiro is a distinguished researcher whose work spans mobile robotics, machine learning, and human-robot interaction, with particular expertise in robot navigation, localization, and adaptive learning systems. Over two decades of sustained contribution, Regueiro has helped shape how autonomous robots perceive, navigate, and interact within dynamic real-world environments. His early foundational work introduced fuzzy temporal rule-based systems for mobile robot guidance and landmark detection, enabling robots to reason about moving obstacles and navigate complex indoor spaces — research that garnered over 30 citations each and established him as a pioneer in fuzzy logic applications for robotics. His contributions to robot localization evolved significantly, culminating in a sophisticated particle filter framework fusing laser, WiFi, compass, and camera data (48 citations), reflecting a broader trajectory toward robust multi-sensor integration. Regueiro also made meaningful advances in human-robot interaction, developing gesture-based interfaces with voice feedback for tour-guide robots and feature analysis techniques for person-following behavior, each attracting over 40 citations. His more recent work on concept drift detection in federated and continual learning (72 citations) demonstrates a timely pivot toward distributed machine learning for smart devices, positioning him at the intersection of robotics and modern AI — a trajectory that continues to resonate strongly with contemporary researchers.