Xianfeng Yuan
Papers
28
Total Citations
334
H-Index
10
About
Xianfeng Yuan is a prominent robotics and intelligent systems researcher whose work sits at the intersection of fault diagnosis, autonomous robot control, and deep learning. His most significant contributions center on developing advanced diagnostic frameworks for wheeled and mobile robots, leveraging graph convolutional networks (GCNs) to model complex spatial-temporal relationships among multi-sensor data — an approach that has garnered substantial attention, with his top papers accumulating nearly 50 citations each. Yuan has pioneered the integration of prior fault knowledge with data-driven methods, producing hybrid architectures that outperform conventional deep learning techniques in real-world reliability scenarios. Beyond fault diagnosis, Yuan has made notable strides in robot motion planning and control, including deep reinforcement learning strategies for dual-arm collaborative robots and an enhanced Grey Wolf Optimization algorithm for path planning. His work on quadruped robot balance control and object goal navigation further demonstrates the breadth of his expertise. His 2015 early-career paper on Mittag-Leffler kernel-based fault diagnosis reflects a long-standing commitment to this field. Collectively, Yuan's research addresses critical challenges in robotic safety, autonomy, and intelligence, making his work essential reading for students and engineers advancing next-generation robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7Balance Control of a Quadruped Robot Based on Foot Fall Adjustment14 citations · 2022
- 8
- 9
- 10