Feiyan Min
Papers
6
Total Citations
132
H-Index
4
About
Feiyan Min is a robotics researcher whose work focuses on making robot manipulators safer, more perceptive, and more reliable in dynamic, real-world environments. Her primary research areas include collision detection and identification, robust visual SLAM, and active compliance control for human–robot interaction. Min’s most influential contribution is a model-independent collision detection method based on vibration analysis (62 citations), which enables robots to quickly identify physical contact without relying on complex torque models—a critical advancement for industrial safety. She further advanced this line of work by introducing a collision classification system using variational mode decomposition, capable of distinguishing between impacts with humans, objects, or specific robot components. In parallel, Min developed COEB-SLAM (33 citations), a robust visual SLAM system that integrates object detection, epipolar geometry, and blur filtering to maintain accurate localization even in environments crowded with moving objects. Her work on active compliance control, using an extended Kalman filter to fuse motor current and harmonic reducer data for precise torque estimation, has been cited 21 times and is foundational for force-sensorless, human-safe robot operation. Min’s research is at the forefront of enabling next-generation smart factories and collaborative robotics.
Research Focus
Key Achievements
Top Papers
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- 3Active Compliance Control Based on EKF Torque Fusion for Robot Manipulators21 citations · 2023
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