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About

Yunjian Ge is a robotics researcher whose work spans robot kinematics, sensor technology, and assistive exoskeleton systems. His most recognized contribution lies in the geometric analysis of robot motion: his 2015 paper on inverse kinematics for six-degree-of-freedom serial robots using the Denavit-Hartenberg framework has accumulated 21 citations, offering practical simulation-backed solutions for industrial manipulators like the Motoman platform. Equally notable is his earlier work on wrist force sensors, where he pioneered a neural network-based nonlinear decoupling method to significantly improve measurement precision over traditional approaches — a contribution that has drawn 17 citations and reflects his interest in intelligent sensing. Ge has also made meaningful strides in human-assistive robotics. His development and dynamic analysis of the Wearable Power Assist Leg (WPAL), designed to augment mobility for elderly and disabled individuals, demonstrates a commitment to socially impactful engineering. Building on this, he proposed a time series-based sensing forecasting algorithm to enhance the dynamic responsiveness of exoskeleton systems. Together, these works position Ge as a versatile robotics engineer bridging industrial automation, intelligent sensing, and rehabilitation technology — with contributions that continue to inform both academic research and real-world robotic applications.