Guan Lu
Papers
4
Total Citations
40
H-Index
3
About
Guan Lu is an emerging researcher specializing in fiber Bragg grating (FBG) sensing technology, robotic tactile perception, and intelligent sensing systems. His work sits at the intersection of photonic sensing, mechanical engineering, and machine learning, with a focused mission to advance the tactile capabilities of intelligent robotic systems. Lu's most significant contributions center on the design and optimization of FBG-based tactile sensors for robot fingers. His pioneering double-layer sliding tactile sensor (2022, 18 citations) addressed longstanding challenges in flexible sliding tactile sensing during robotic grasping, while his square-hole-structured pressure tactile sensor (2023, 15 citations) innovatively tackled lateral force interference and non-uniform strain — persistent obstacles in real-world robotic manipulation. These designs demonstrate his ability to translate fundamental sensing principles into practical, application-ready solutions. More recently, Lu has expanded into AI-driven perception, developing a convolutional neural network approach for FBG tactile array shape recognition (2024), reflecting a forward-thinking integration of deep learning with physical sensing hardware. His convex fiber grating sliding sensor further advances composite tactile sensing for mechanical fingers. With over 40 cumulative citations across just a few years of publication, Guan Lu represents a promising voice in the rapidly evolving field of robotic haptic sensing.
Research Focus
Key Achievements
Top Papers
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