Chuan Ye
Papers
3
Total Citations
8
H-Index
2
About
Chuan Ye is a researcher advancing the frontier of intelligent robotics, with a primary focus on automated welding, underwater perception, and robotic manipulation. Their work tackles critical challenges in unstructured and low-visibility environments, where traditional sensing and control methods falter. Ye’s most influential contribution is a novel method for automatic extraction and tracking of robot weld seam paths using line structured light, which overcomes the difficulties posed by high-reflectivity materials and uneven illumination—a paper that has already garnered 4 citations since its 2025 publication. In the domain of underwater robotics, Ye developed a target detection method specifically designed for low-illumination environments (2023, 3 citations), enabling reliable object recognition in murky waters. More recently, Ye proposed YOLO-Net, a deep learning framework integrating feature fusion and attention mechanisms for robust workpiece recognition and grasp detection under uneven lighting (2025, 1 citation). Together, these works demonstrate Ye’s commitment to enhancing robotic autonomy in challenging real-world conditions, making their research highly relevant for students and engineers working on industrial automation, underwater exploration, and intelligent grasping systems.
Research Focus
Key Achievements
Top Papers
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