Yongli Hu

Papers

Total Citations

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About

Yongli Hu is a leading researcher in intelligent robotics and underwater autonomous systems, with a particular focus on perception-driven manipulation in challenging environments. His most-cited work, "Underwater autonomous grasping robot based on multi-stage Cascade DetNet" (2023), introduces a novel deep learning architecture that significantly enhances robotic precision in low-visibility, high-turbidity underwater settings. By integrating a multi-stage cascade detection network, Hu’s approach enables real-time object recognition and adaptive grasping, addressing critical limitations in marine exploration and offshore maintenance. This contribution has garnered 6 citations, reflecting its immediate relevance to advancing autonomous underwater vehicle capabilities. Hu’s research bridges computer vision and robotic control, emphasizing robust detection pipelines that operate under extreme constraints. His work is notable for its practical applications in oceanography and environmental monitoring, where reliable autonomous manipulation remains a frontier challenge. Through this and related studies, Hu continues to shape the development of resilient robotic systems, offering scalable solutions for deep-sea tasks that were previously reliant on human intervention.