Papers
3
Total Citations
6
H-Index
1
About
Xiaona Song’s research focuses on the intersection of marine robotics, computer vision, and deep learning, with a particular emphasis on developing intelligent systems for underwater and human-robot interaction applications. Her most notable contribution is the optimization design of a winch suction underwater dredging robot, where she employed orthogonal experimental design to enhance dredging efficiency in marine engineering and water conservancy projects—a critical innovation for addressing silt accumulation in water ecosystems. This work has garnered 4 citations since 2024. Song has also advanced human-robot interaction through a thermal imaging-based lightweight gesture recognition system for mobile robots, achieving real-time control without traditional physical buttons, and has authored a comprehensive review of deep learning techniques for point cloud tasks. While her citation counts are still growing, her work demonstrates a clear trajectory toward practical, deployable solutions in environmental engineering and autonomous systems. Song’s interdisciplinary approach—bridging mechanical design, thermal sensing, and deep learning—positions her as an emerging researcher with potential to impact both industrial dredging operations and intuitive robot control interfaces.
Research Focus
Key Achievements
Top Papers
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- 3Deep learning techniques for point cloud tasks: a review1 citations · 2025