Fangmeng Zhu

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About

Fangmeng Zhu is a pioneering researcher at the intersection of soft robotics and biomedical sensing, with a primary focus on developing novel shape-sensing technologies for highly deformable systems. Their most-cited work, "Electrical Impedance Tomographic Shape Sensing for Soft Robots" (2023, 23 citations), addresses a critical challenge in soft robotics: how to accurately perceive the continuous deformation of robots with theoretically infinite degrees of freedom. Zhu’s key contribution lies in adapting electrical impedance tomography (EIT)—a technique traditionally used for medical imaging—to reconstruct the 3D shape of soft robotic structures in real time. This approach offers a scalable, cost-effective alternative to conventional sensor arrays, enabling robots to sense their own body configuration without bulky hardware. By solving the inverse problem of mapping impedance changes to geometric deformation, Zhu has opened new pathways for closed-loop control in soft manipulators and wearable devices. Their work is particularly notable for bridging the gap between medical imaging principles and robotic proprioception, earning recognition from both the soft robotics and sensing communities. With growing citations reflecting its foundational impact, Zhu’s research continues to inspire innovations in autonomous soft systems, where self-awareness is key to dexterous interaction with unstructured environments.