Yufang Wen
Papers
3
Total Citations
31
H-Index
3
About
Yufang Wen is a robotics researcher whose work focuses on advancing autonomous navigation, perception, and manipulation for mobile robots operating in complex, real-world environments. Her key research areas include path planning under GPS-denied conditions, robust object recognition, and dynamic target grasping. Wen’s major contributions include developing a path-planning method for wall surface inspection robots using an improved genetic algorithm, which enhances positioning accuracy when GPS signals are unavailable—a critical safety improvement for infrastructure inspection. She has also pioneered a glass recognition and map optimization method that uses boundary guidance to help robots accurately perceive transparent obstacles, a common challenge in commercial and domestic settings. Additionally, Wen proposed a dynamic target grasping method based on an affine group improved Gaussian resampling particle filter, significantly boosting the real-time performance and robustness of visual servo control for tracking moving objects. Her most-cited work, with 17 citations, addresses a practical safety gap in inspection robotics. Through these contributions, Wen is helping to make mobile robots more reliable, perceptive, and capable in environments where traditional sensors fail, advancing the field toward truly autonomous operation.
Research Focus
Key Achievements
Top Papers
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