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About

Santiago Garrido is a prominent robotics researcher whose work spans mobile robot navigation, motion planning, and autonomous systems. He is perhaps best known for pioneering the integration of Voronoi diagrams with the Fast Marching Method (FMM) for robot path planning — an elegant two-step approach that first identifies the safest traversable areas in an environment and then computes optimal trajectories through them. His 2006 paper introducing this framework has garnered nearly 200 citations, establishing it as a foundational reference in the field. Garrido has consistently advanced FMM-based techniques across a remarkable range of applications, from single robot navigation and real-time feedback control to multi-robot formation planning under uncertainty, collectively accumulating hundreds of citations that underscore their broad adoption. His 2013 comprehensive survey of Fast Marching methods further cemented his role as a leading authority in the area. Beyond navigation, Garrido has made notable contributions to industrial robotics, including crane automation with anti-swinging control strategies and manipulator calibration, demonstrating versatility across both autonomous systems and industrial applications. His early work on genetic algorithm-based robot localization using ultrasonic sensors reflects a career-long commitment to practical, sensor-driven solutions. Altogether, his research portfolio represents a sustained and impactful contribution to the science of intelligent robotic motion.