Min Woo Jang
Papers
1
Total Citations
2
H-Index
1
About
Min Woo Jang is a researcher in robotics and computer vision, with a focus on developing adaptive control systems for robots operating in uncertain environments. His work addresses the critical challenge of enabling robots to perform precise manipulation tasks—such as thin-rod placement—when obstacles appear unpredictably during motion. Jang’s key contribution is a robot vision control scheme based on the Newton-Raphson method, which estimates six camera parameters (C1–C6) to dynamically adjust joint angles and maintain accurate trajectory planning despite environmental discontinuities. This approach integrates batch processing for parameter estimation with real-time visual feedback, allowing robots to handle obstacles that create disjointed paths between start, intermediate, and target regions. While his most-cited paper has garnered 2 citations, the work lays foundational groundwork for robust visual servoing in cluttered settings. Jang’s research is particularly relevant for industrial automation and autonomous navigation, where adaptability to unforeseen obstacles is critical. His methodology offers a computationally efficient alternative to more complex sensor fusion techniques, making it accessible for practical deployment in manufacturing and service robotics.
Research Focus
Key Achievements
Top Papers
- 1