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About

Javier Garrido is a robotics and autonomous systems researcher whose work spans indoor localization, mobile robot navigation, and collaborative robotic control. His research has made meaningful contributions to the development of practical, resource-constrained robotic systems, with a particular focus on making advanced capabilities accessible through low-cost hardware. Garrido's most influential work centers on indoor positioning and mapping, including an ultrasonic positioning system (34 citations) and a simplified Occupancy Grid mapping approach optimized for low-cost robots (24 citations). Complementing these, his HCTNav path planning algorithm — designed explicitly for robots with limited computational power and energy — has attracted 24 citations and spawned comparative studies evaluating it against established algorithms like A* and Dijkstra. His ALO ultrasound orientation system further demonstrates his commitment to affordable, practical localization solutions. Beyond terrestrial robotics, Garrido contributed to an 8mm fibre robot positioner for massive astrophysical spectroscopy surveys (19 citations), showcasing his versatility across domains. His later work explores energy-efficient mobile manipulators and heterogeneous multi-robot cooperation, reflecting a broadening research vision. With over 160 cumulative citations, Garrido's body of work offers valuable, accessible solutions for researchers and engineers working within real-world hardware constraints.