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About

Xiulan Wen is a robotics researcher whose work spans intelligent control systems, robot calibration, and bio-inspired locomotion. With expertise ranging from manipulator kinematics to snake-like robot design, Wen has made meaningful contributions to both industrial and service robotics over more than a decade of research. Wen's early work applied swarm intelligence to robotic control, developing hybrid Particle Swarm Optimization approaches for solving complex manipulator inverse kinematics problems — work that has accumulated 15 citations and remains among her most recognized contributions. Her research then expanded into precision calibration of serial industrial robots, where she systematically compared error compensation methodologies for 6-DOF systems, advancing the field of robot positioning accuracy and uncertainty estimation under new-generation GPS standards. A particularly distinctive thread in Wen's research is her development of central pattern generator-based controllers for snake-like robots, producing multiple publications exploring 3D locomotion, terrain adaptation, and compliant mechanical design. Her 2017 studies on triple-layered CPG architectures and sigmoid transition approaches each garnered 10–11 citations, reflecting genuine interest from the robotics community. More applied contributions include a multifunctional greenhouse agricultural robot, demonstrating her commitment to translating research into practical solutions. Across her portfolio, Wen has established herself as a versatile and technically rigorous contributor to modern robotics.