Ming-Shaung Chang

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About

Ming-Shaung Chang is a robotics researcher whose work centers on intelligent mobile systems, autonomous navigation, and human-robot interaction. His research contributions, primarily published around 2010, address some of the core challenges in making robots practical and deployable in real-world settings. Chang's most recognized work focuses on vision-based autonomous navigation for mobile robots operating in unstructured environments. His design of an outdoor road-cleaning robot — his most cited contribution with 10 citations — demonstrated a practical integration of on-board vision systems with track-driven mechanisms capable of handling rough terrain, pushing the boundaries of what autonomous cleaning systems could accomplish outside controlled settings. Complementing this, Chang developed a human-robot interface system that relies on natural human gestures, combining a triple-face detection method with a fuzzy logic controller and Kalman filter to enable intuitive, robust user interaction. Garnering 9 citations, this work reflects his commitment to making robotic systems accessible and user-friendly. His additional research on machine vision-based navigation in unknown environments further underscores his dedication to solving real-world autonomy challenges. Taken together, Chang's body of work represents a meaningful contribution to the fields of mobile robotics, computer vision, and intelligent control systems.