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About

Chia-Jun Yu is a robotics researcher whose work centers on intelligent control systems for robot manipulators and autonomous mobile robots. His primary research areas include visual servo control, path planning, and machine learning-based motion control. Yu’s most impactful contribution is his 2015 paper on hybrid switched reactive-based visual servo control for 5-DOF robot manipulators, which has garnered 63 citations. This work introduced a novel sensing-and-reaction control structure that eliminates the need for inverse interaction matrix computation, significantly simplifying pick-and-place tasks. He has also advanced path planning for mobile robots by modifying the A* algorithm with a weighted cost function that balances distance and safety, and developed a batch learning architecture combining fuzzy inference systems with Genetic Algorithms for two-wheeled robot motion control. More recently, Yu integrated Ant Colony Optimization with image-based segmentation for 6-DOF manipulators, demonstrating his ongoing commitment to combining bio-inspired algorithms with practical robotics. His research consistently bridges theoretical control methods with real-world applications, making his work valuable for students and researchers in autonomous systems and industrial robotics.