Xuedong Jing

Papers

Total Citations

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About

Xuedong Jing is a researcher focused on advancing robotics and automation, with key contributions in parallel mechanism dynamics, industrial inspection, and service robot navigation. Their work on "Dynamic Modeling and Solution of 6-DOF Parallel Mechanism" (2022, 10 citations) addresses critical challenges in force control and precision positioning for parallel robots, providing foundational insights for applications requiring rapid attitude adjustments. In industrial automation, Jing proposed the YOLOX-CAlite algorithm (2023, 5 citations), a lightweight detection method that significantly improves pointer meter identification in pumping stations and substations—a vital step for inspection robot deployment. Earlier research on "Path Planning for Family Service Robot Based on Improved Genetic Algorithm" (2019, 2 citations) demonstrates their long-standing interest in optimizing robot navigation for domestic environments. While their citation counts are modest, Jing’s work bridges theoretical modeling and practical deployment, particularly in integrating AI-driven detection with mechanical systems. Their research holds promise for enhancing robot autonomy in both industrial and service settings, reflecting a commitment to solving real-world automation challenges through innovative algorithmic and mechanical solutions.