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About

Rafael Herrejon is a robotics researcher whose work focuses on the intersection of computer vision and robotic manipulation, with particular emphasis on real-time visual servoing and autonomous object catching systems. His most notable contributions center on developing sophisticated algorithms that enable robotic systems to intercept and catch three-dimensional flying objects using monocular camera input — a technically demanding challenge that requires seamless coordination between visual perception and mechanical control. Herrejon's most cited work, published in 2009, introduced position-based and composite visual servoing frameworks that leverage Recursive Least Squares (RLS) trajectory estimation to predict object trajectories from single-camera image sequences in real time. These contributions are significant because monocular vision systems present inherent depth ambiguity, making accurate 3D trajectory prediction considerably more complex than with stereo setups. His 2010 follow-up paper further demonstrated practical implementation of a ball-catching robot manipulator, validating the RLS approach in physical experiments. With a cumulative citation count across his key publications, Herrejon's research has influenced subsequent work in dynamic robotic grasping, predictive visual control, and human-robot interaction. His methodologies offer practical pathways for developing faster, more perceptually capable robotic systems in unstructured environments.