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About

C.C. Chang is a robotics and autonomous systems researcher whose work has made significant contributions to the field of mobile robot navigation, particularly in dynamic and unstructured environments. Best known for developing intelligent navigation frameworks that enable robots to operate safely among moving obstacles, Chang's research addressed one of the fundamental challenges in robotics: real-time obstacle prediction without prior knowledge of obstacle velocities. His landmark papers from the late 1990s and early 2000s, each garnering up to 45 citations, introduced multisensor-based obstacle predictors and environment prediction systems that estimate future obstacle positions, allowing robots to plan paths proactively rather than reactively. Chang pioneered the application of artificial neural networks for sensor data fusion, notably solving the beam-opening angle problem in ultrasonic sensors to improve environment recognition accuracy. His Virtual Force Guidance system further advanced dynamic motion planning by integrating predicted environmental data with on-board sensing. Across his body of work, Chang consistently bridged theoretical innovation with practical implementation, demonstrating working navigation systems that combine sensor fusion, path planning, and real-time adaptability — foundational contributions that continue to inform modern autonomous robot design.