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About

Liyue Fu is a leading researcher in the field of intelligent robotic sensing, with a primary focus on the design, characterization, and performance optimization of six-axis force/torque (F/T) sensors. Her work addresses critical challenges in robot applications, from industrial machining to joint force measurement. Fu pioneered the use of advanced materials, notably introducing a novel Polyetheretherketone (PEEK) six-axis F/T sensor, which offers significant advantages over traditional metal-based designs. To overcome the inherent coupling errors in multidimensional sensors, she developed sophisticated decoupling algorithms, including a genetic algorithm-optimized BP neural network (GA-BP) that dramatically improves measurement precision. Her research also extends to dynamic performance, where she has applied model identification methods and innovative optimization techniques, such as the multistrategy improved sparrow search algorithm, for dynamic compensation. With her most-cited works accumulating over 70 citations, including a foundational 2019 paper on the PEEK sensor (28 citations) and a 2018 study on dynamic characteristics (20 citations), Fu’s contributions are essential for advancing the accuracy and reliability of force sensing in next-generation robotics and automated manufacturing.