Xuqing Fan
Papers
1
Total Citations
2
H-Index
1
About
Dr. Xuqing Fan is a researcher at the forefront of intelligent robotics and sensor systems, with a particular focus on fault diagnosis and safety enhancement in bio-inspired underwater vehicles. In their most-cited work, Fan introduced a novel depth sensor fault diagnosis method for robotic fish, employing Gramian Angular Field Fusion and Convolutional Neural Networks (GAFF-CNN). This approach transforms raw sensor signals into spatial domain images, enabling a convolutional neural network to detect and classify faults with high accuracy. The work is pivotal for improving the operational reliability of autonomous underwater robots, addressing a critical challenge in real-world marine applications. While still early in their career, Fan’s contributions have already garnered attention, with their flagship paper accumulating citations that underscore its relevance to the growing field of AI-driven robotic maintenance. By merging signal processing, image fusion, and deep learning, Fan is helping to pave the way for safer, more resilient robotic systems in unpredictable aquatic environments.
Research Focus
Key Achievements
Top Papers
- 1