Papers
6
Total Citations
43
H-Index
4
About
Fen Liu is a pioneering researcher in surgical robotics and autonomous mobile systems, with a focus on enhancing precision and safety in robotically assisted minimally invasive surgery (RAMIS). Liu’s work addresses critical challenges such as physiological tremor suppression and vibration control, introducing innovative solutions like the zero phase adaptive fuzzy Kalman filter (ZPAFKF) and a fuzzy neural network sliding mode controller—both of which have garnered significant attention (16 and 13 citations, respectively). These contributions enable smoother, high-precision motion control, directly improving surgical outcomes. Liu also developed a kinematics rapid modeling method for tendon-driven robotic mechanisms, leveraging the tendon-routing matrix and equivalent radius matrix to streamline design processes. More recently, Liu has explored resistance torque compensation using a Luenberger observer to enhance backdrivability in surgical robots, and advanced trajectory planning for multi-axle autonomous mobile robots (AMRs) to minimize swept volume during turns. With a growing citation impact and a state-of-the-art review on laparoscopic surgical robots, Liu’s work bridges foundational theory and practical application, making significant strides in both medical robotics and autonomous navigation.
Research Focus
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Top Papers
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