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About
Yajun Fan is a researcher in robotics and precision engineering, with a primary focus on the kinematic calibration and control of parallel robotic systems. Their most notable contribution is the development of a novel kinematic calibration method based on point cloud measurement for the 3-RPS (Revolute-Prismatic-Spherical) parallel robot, a widely used platform in machining and assembly tasks. This work, published in 2025, addresses critical challenges in improving the absolute positioning accuracy of parallel manipulators by leveraging advanced measurement techniques to identify and compensate for geometric errors. While still early in its citation impact, the paper represents a significant methodological advancement, offering a practical, high-precision solution for industrial applications where traditional calibration approaches fall short. Fan’s research bridges the gap between theoretical kinematics and real-world implementation, making their work valuable for engineers and researchers working on robot accuracy, metrology, and automation. As their citation count grows, Yajun Fan is positioned to become a key contributor to the field of parallel robot calibration, with potential applications in aerospace, medical robotics, and high-precision manufacturing.
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