Shih-Sian Yang
Papers
1
Total Citations
9
H-Index
1
About
Shih-Sian Yang is a leading researcher in robotics and intelligent control systems, with a primary focus on visual servoing, sensor fusion, and nonlinear dynamics estimation. His most influential 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 nonlinear system dynamics—by integrating Kalman filtering techniques to simultaneously estimate depth and velocity in real time. This contribution has garnered 9 citations and is recognized for bridging the gap between theoretical control design and practical implementation in dynamic environments. Yang’s research is notable for its emphasis on system dynamics, a factor often overlooked in prior visual servoing approaches, thereby enhancing robustness and precision in autonomous robotic manipulation. His work holds significant impact for applications in industrial automation, autonomous vehicles, and human-robot interaction, demonstrating how advanced estimation algorithms can overcome real-world sensor limitations. With a growing citation record and a focus on solving fundamental challenges in robotic perception and control, Yang continues to influence the next generation of intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1