Qingyong Ding

Papers

Total Citations

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About

Qingyong Ding is a robotics researcher whose work centers on the kinematic design, calibration, and precision control of parallel robotic systems. His primary contributions lie in advancing the accuracy and performance of 2-degree-of-freedom (DOF) planar parallel robots, particularly through innovative calibration techniques and optimal design methodologies. In his most cited work, Ding developed two methods—geometric error iteration and nonlinear fitting—for identifying the home position of a planar parallel robot, achieving a calibration approach that directly enhances real-world positioning accuracy. His research on optimal kinematic design established closed-form parametric relationships for link lengths, enabling robots to achieve superior dexterity, speed, and precision. Notably, Ding also pioneered the use of radial basis function (RBF) neural networks for nonparametric kinematic calibration, demonstrating that machine learning can outperform traditional model-based methods in correcting joint errors. While his citation counts are modest, reflecting a focused, early-career trajectory, his work has laid foundational groundwork for practical parallel robot implementation, with applications in automation and precision manufacturing. Ding’s contributions are particularly valuable for students and researchers interested in bridging theoretical kinematics with experimental validation in robotics.