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Chin-Kai Chang is a leading researcher in biologically-inspired autonomous mobile robotics, with a focus on vision-based navigation and localization. His work bridges computational neuroscience and practical robotics, drawing on human visual capabilities to enable robots to perceive and navigate complex outdoor environments. Chang’s most influential contribution is a navigation system that integrates two key models: the Gist model, which captures the holistic layout of a scene, and the Saliency model, which emulates visual attention to detect salient features. This approach, detailed in his highly cited 2010 paper (70 citations), allows robots to localize and navigate without GPS. He further advanced monocular vision navigation by combining road region segmentation with boundary estimation (46 citations), enabling robust path following in unstructured settings. His 2013 work on road recognition using contour-based line extrapolation (35 citations) improved heading control by centering the vanishing point. Chang also contributed to the development of Beobot 2.0, an autonomous platform designed for long-range urban travel. With over 150 total citations, his research has significantly influenced the fields of robot vision, autonomous navigation, and biologically-inspired perception.