Ting-Yu Chang
Papers
1
Total Citations
9
H-Index
1
About
Ting-Yu Chang is a leading researcher in robotics and control systems, with a primary focus on visual servoing, sensor fusion, and nonlinear dynamics. Her most impactful work, "Dynamic visual servoing with Kalman filter-based depth and velocity estimator" (2021), addresses critical challenges in robotic vision—such as camera calibration errors, vision latency, and system nonlinearities—by integrating Kalman filtering to estimate depth and velocity in real time. This contribution has garnered 9 citations and stands out for its practical approach to improving the stability and accuracy of vision-based robotic control. Chang’s research bridges the gap between theoretical control design and real-world implementation, offering robust solutions for autonomous systems. Her work is particularly notable for tackling the often-overlooked dynamic complexities in visual servoing, making her a key figure in advancing reliable, high-performance robotic perception and motion control. For students and researchers, Chang’s studies provide essential insights into overcoming the limitations of traditional visual servoing in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1