Papers
4
Total Citations
71
H-Index
3
About
Guojin Li is a leading researcher in intelligent robotics and automation, with a focus on computer vision, adaptive control, and autonomous navigation. His work spans agricultural robotics, underwater engineering, and deformable object manipulation. Li’s most cited paper, “Lemon‐YOLO: An efficient object detection method for lemons in the natural environment” (42 citations), introduces a lightweight deep learning model that overcomes challenges like variable lighting and occlusion—a key step toward automated harvesting. In underwater engineering, his adaptive robotic welding system using laser vision sensing (17 citations) enables real-time seam tracking for V-groove joints, improving precision in harsh environments. Li also advanced mobile robot navigation with an interval type-2 fuzzy neural network fitting Q-learning algorithm (11 citations), which combines fuzzy reasoning with reinforcement learning for complex, unknown settings. Most recently, his 2025 work on physics-informed graph learning for shape prediction of deformable linear objects (1 citation) addresses a critical challenge in manipulating cables and wires for manufacturing and medical devices. With a career spanning nearly two decades, Li’s contributions demonstrate a consistent drive to integrate sensing, learning, and control for practical robotic systems.
Research Focus
Key Achievements
Top Papers
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