Papers
1
Total Citations
17
H-Index
1
About
Won Jang is a pioneering researcher in robotics and computer vision, best known for foundational contributions to visual servoing—the use of visual feedback to control robot motion. His seminal 1991 paper introduced the concepts of Augmented Image Space and Transformed Feature Space, formalizing image features as functionals for efficient “eye-in-hand” robot control. This work provided a rigorous mathematical framework that enabled more robust and computationally efficient visual servoing, influencing subsequent generations of robotic manipulation systems. With over 17 citations, this early paper remains a touchstone for researchers in vision-based robotics. Jang’s contributions lie at the intersection of control theory, image processing, and robotic perception, helping to bridge the gap between raw visual data and actionable robotic commands. His work is particularly valued for its clarity in defining how to extract and transform meaningful features from images to guide real-time robot movements. For students and researchers exploring visual servoing or sensor-based robot control, Jang’s foundational concepts offer essential theoretical grounding and practical insight into designing efficient, vision-guided robotic systems.
Research Focus
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Top Papers
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