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About

Ke-Jun Xu is a researcher whose work sits at the intersection of robotics, sensor technology, and intelligent systems. His primary research areas include force/torque sensing for robotic manipulators, dynamic system modeling and compensation, and smart sensor networks. Xu’s most significant contribution is his pioneering work on the dynamic modeling and compensation of six-axis wrist force/torque sensors for robots, where he applied system identification methods to build mathematical models from step-response calibration data. This work, which has garnered 40 citations, established critical performance benchmarks in the frequency domain and addressed the challenge of slow dynamic response in sensors. He further advanced the field by exploring data fusion techniques, using finger force sensors to estimate wrist forces, and by integrating neural networks for improved estimation accuracy. Notably, Xu also contributed to the development of networked smart sensor systems based on the IEEE 1451 standard, enabling standardized communication for robotic grippers. His research on dynamic nonlinearity compensation tackled the persistent problem of nonlinear sensor characteristics, proposing solutions to enhance compensation effectiveness. Through these contributions, Xu has helped lay the groundwork for more responsive, accurate, and intelligent robotic sensing systems.