Papers
4
Total Citations
88
H-Index
3
About
Keyou Guo is a versatile researcher whose work spans intelligent transportation systems and advanced robotic control, two fields with profound real-world implications. His most widely recognized contribution is a 2022 study optimizing YOLOv5s for automated pavement distress identification, which has garnered 51 citations and addresses a critical challenge in road maintenance — enabling timely detection of surface degradation to prevent structural damage and reduce traffic hazards. This work exemplifies Guo's commitment to applying cutting-edge computer vision techniques to practical infrastructure problems. Equally significant is his growing body of research in robotic manipulator control, where he has developed sophisticated sliding mode control strategies enhanced by nonlinear disturbance observers. His 2023 paper introducing the NDO–NTSMC framework for trajectory tracking, cited 18 times, and a subsequent 2024 study refining this approach with 17 citations, demonstrate his systematic effort to tackle model uncertainties and external disturbances in robotic systems. His most recent 2025 work extends these ideas to adaptive control of spatial three-DOF manipulators under strong disturbances. Collectively, Guo's research reflects a rare interdisciplinary breadth, making meaningful contributions across autonomous infrastructure monitoring and precision robotic control.
Research Focus
Key Achievements
Top Papers
- 1A pavement distresses identification method optimized for YOLOv5s51 citations · 2022
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