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About

Yuehua Wan is a multidisciplinary robotics researcher whose work spans soft robotics, robotic exoskeletons, parallel robot design, and control systems. Beginning with foundational contributions to parallel robot optimization in the late 2000s, Wan developed systematic methodologies for determining optimal kinematic parameters and dexterous workspaces in parallel manipulators — work that laid important groundwork for compact, high-performance robotic systems. His subsequent pivot toward emerging technologies demonstrated remarkable breadth: his 2018 bibliometric analysis of soft robotics garnered over 200 citations, establishing itself as a landmark reference for researchers navigating this rapidly evolving field. Following this success, his 2019 comprehensive review of robotic exoskeletons accumulated 136 citations, cementing his reputation as a trusted synthesizer of complex research landscapes. More recently, Wan has turned his analytical lens toward artificial muscles and input saturation in control systems, reflecting his sustained commitment to charting frontier research areas. With a career spanning foundational mechanical design to cutting-edge bio-inspired robotics, Wan's cumulative contributions — exceeding 400 citations across his most influential works — make him an invaluable guide for students and researchers seeking both historical context and forward-looking perspectives in advanced robotics.