Papers
5
Total Citations
38
H-Index
4
About
Xiaojuan Lan is a robotics researcher whose work centers on the design, hydrodynamics, and intelligent control of underwater spherical robots, as well as the application of deep learning to marine biology. Her most impactful contribution is the development of a deep convolutional neural network for fish image classification (2020, 16 citations), a tool critical for studying marine species composition and distribution. Lan’s foundational research on the BYSQ-2 spherical underwater exploration robot includes detailed experimental and simulation analyses of coupling hydrodynamic forces (2014, 7 citations) and the robot’s unique heavy-pendulum attitude adjustment system (2014, 6 citations). She has also advanced path planning for robotic manipulators using an improved RRT algorithm (2019). Her work on spherical hull hydrodynamics (2009, 7 citations) established key insights into the maneuverability and drag characteristics of these novel platforms. With over 38 total citations, Lan’s research bridges mechanical design, fluid dynamics, and AI, offering practical solutions for underwater exploration and marine conservation.
Research Focus
Key Achievements
Top Papers
- 1Fish Image Classification Using Deep Convolutional Neural Network16 citations · 2020
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