Deying Gu
Papers
2
Total Citations
10
H-Index
2
About
Deying Gu is a researcher whose work sits at the intersection of robotics, computer vision, and intelligent control systems. Her research primarily focuses on enhancing the performance and adaptability of robotic platforms, with key contributions in binocular stereo matching for robot vision and adaptive control for flying robots. In her most cited work, Gu tackled a critical challenge in robotics—optimizing the SURF algorithm on embedded CUDA platforms to improve the real-time performance and precision of binocular stereo matching, a cornerstone technology for robot positioning. This work, which has garnered 7 citations, addresses the practical limitations of portability and accuracy in existing algorithms. Additionally, Gu developed a sliding mode adaptive control algorithm integrating a recurrent cerebellar model articulatory controller (CMAC) for uncertain nonlinear systems, such as flying robots. This approach, cited 3 times, offers a robust solution for systems where disturbance thresholds are difficult to measure. Through these contributions, Gu has advanced the practical deployment of vision-based and adaptive control systems in robotics, demonstrating a commitment to bridging theoretical algorithms with real-world application challenges.
Research Focus
Key Achievements
Top Papers
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- 2