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About

Yu Hao Chang is a robotics researcher whose work centers on intelligent control systems for mobile and manipulator robots, with a particular emphasis on visual servoing and uncalibrated robotic systems. His major contributions lie in developing control schemes that enable robots to operate effectively without precise calibration—a critical advancement for real-world deployment. Chang’s most-cited paper, “Intelligent Mobile Robot Controller Design for Hotel Room Service with Deep Learning Arm-Based Elevator Manipulator” (29 citations), introduces a low-cost, lightweight robot capable of navigating multi-floor buildings, addressing practical challenges in service robotics. He has also pioneered fast-convergence, calibration-free visual servoing control (13 citations) and adaptive cooperative control for networked robotic systems with time-varying delays (5 citations). His work on delay-dependent stability in uncalibrated visual servoing (4 citations) further underscores his focus on robustness in dynamic environments. Chang’s research bridges theoretical control theory and applied robotics, offering solutions that enhance robot autonomy and reliability in uncertain conditions. His achievements are particularly notable for advancing the feasibility of uncalibrated systems in real-time applications, making him a key figure in the evolution of intelligent, adaptive robotic control.