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About

Shih-Jie Chang is a researcher specializing in autonomous mobile robotics, fuzzy logic control, and intelligent mechatronic systems, with a particular focus on replicating human-like driving behaviors in car-like mobile robots (CLMRs). His most influential work, published in 2003 and accumulating nearly 200 citations, pioneered the integration of fuzzy logic control (FLC) with sensor-based behavioral architectures to enable autonomous navigation, wall-following, corner-handling, and parking maneuvers on FPGA-based robotic platforms. Chang's contributions to autonomous parking — encompassing both garage and parallel-parking scenarios — have been especially impactful, with his dedicated parking control study drawing over 140 citations and establishing foundational methodologies that combine real-time image processing with feasible reference trajectory planning. His 2004 work further refined garage-parking implementations using infrared sensors, demonstrating practical embedded systems applications. Across his publication record, Chang has consistently bridged theoretical fuzzy control design with hands-on hardware realization, making his research valuable to both robotics engineers and AI researchers. With a cumulative citation count exceeding 400, his body of work remains a meaningful reference point in the autonomous vehicle and intelligent robotics literature.