Yuqiang Jin
Papers
2
Total Citations
35
H-Index
2
About
Yuqiang Jin is a robotics researcher whose work centers on geometric control and optimization for mobile robotic systems, with a particular focus on trajectory tracking and motion planning. His major contribution lies in developing the Geometric Model Predictive Control (GMPC) framework, which addresses a critical limitation in conventional approaches: the failure to respect the manifold constraints inherent in robot configurations. By explicitly accounting for the continuous transformation groups that define robotic systems, Jin’s GMPC method achieves more accurate and stable trajectory tracking for wheeled mobile robots. This work has garnered significant attention, with his most-cited paper accumulating 32 citations within a single year, underscoring its immediate impact on the field. Jin’s research bridges the gap between theoretical geometry and practical control, offering a principled alternative to naive vector-space optimization. His achievements highlight the importance of leveraging differential geometry in robotics, and his work is already influencing how researchers approach motion control for nonholonomic systems. For students and researchers, Jin’s contributions serve as a compelling example of how mathematical rigor can directly enhance real-world robotic performance.
Research Focus
Key Achievements
Top Papers
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- 2