Ming-Kan Wu

National Yunlin University of Science and Technology

Papers

Total Citations

H-Index

About

Ming-Kan Wu is a robotics researcher whose work centers on vision-based control systems and human-robot interaction. His most cited paper, “Control of a Movable Robot Head Using Vision-Based Object Tracking” (2019, 7 citations), introduces a visual tracking system that enables a robot head to detect and follow objects in real time. By employing image processing for object identification and position estimation, Wu’s system allows the robot head to move in four directions, enhancing its ability to interact dynamically with its environment. This contribution is particularly valuable for applications in assistive robotics and autonomous navigation, where precise, responsive visual tracking is essential. Wu’s research demonstrates a practical integration of computer vision and mechanical control, offering a foundation for more intuitive robotic behaviors. His work has been cited in studies exploring advanced tracking algorithms and robotic perception, underscoring its relevance to ongoing developments in intelligent systems. For students and researchers, Wu’s approach exemplifies how targeted vision-based solutions can improve robotic autonomy and interaction.