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About

Yin Yin Aye is a robotics and control systems researcher whose work sits at the intersection of computer vision, fuzzy logic, and autonomous mobile robotics. Her research has focused primarily on developing intelligent control frameworks for autonomous parking systems and human-following robots, leveraging image-based processing techniques to enable robots to navigate complex real-world environments without relying on explicit state information. Among her most significant contributions is a series of investigations into image-based fuzzy controllers for car-like mobile robots, where she employed Hough transform techniques and single-camera setups to detect parking frames and generate time-varying target lines — work that earned her most-cited publications and garnered over 10 citations for her 2016 study optimized through genetic algorithms. Her 2019 research on deep neural network-driven human-following robots, integrating fuzzy velocity control to maintain subject centering, reflects her expanding engagement with modern deep learning methodologies. She has also contributed to foundational control theory through work on invariant manifold-based stabilization of nonholonomic mobile robots. Collectively, her publications demonstrate a consistent commitment to bridging theoretical control design with practical autonomous robotic applications, making her work particularly valuable to researchers developing intelligent transportation and assistive robotic systems.