Yongqi Zhang
Papers
2
Total Citations
12
H-Index
2
About
Yongqi Zhang’s research bridges mobile robotics and adaptive control, with a focus on creating intelligent, scalable systems for real-world navigation. His most cited work introduces **osmAG**, a novel hierarchical semantic topometric area graph map format based on OpenStreetMap XML, designed to unify indoor and outdoor multi-floor mapping for mobile robots. This contribution addresses a critical gap in robotics—the lack of a standardized, human-readable map format that supports both topological and metric information—garnering **7 citations** since 2023. In parallel, Zhang advances control theory with his 2025 paper on **model-free adaptive iterative learning control** for nonlinear systems under time-varying constraints, a method that promises robust performance without requiring precise system models, earning **5 citations** in a short period. His work is notable for its practical impact: osmAG has potential applications in autonomous delivery, warehouse logistics, and assistive robotics, while his control research supports safer, more adaptive automation. Zhang’s ability to integrate mapping and control demonstrates a systems-level approach, making him a rising figure in robotics and nonlinear dynamics.
Research Focus
Key Achievements
Top Papers
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- 2