About

Fayong Guo is a robotics researcher whose work spans humanoid robot locomotion, kinematic modeling, and bio-inspired motion planning. His research career has centered on advancing the capabilities of biped and humanoid robots, with particular emphasis on energy-efficient gait generation, obstacle navigation, and robust kinematic frameworks. Among his most influential contributions is his bio-inspired approach to gait planning and control for biped robots, drawing directly from human locomotion analysis to produce more energy-efficient movement strategies — a paper that has garnered 17 citations since 2016. He has also made meaningful strides in robot kinematic modeling, proposing the Enhanced D-H convention to resolve longstanding ambiguities in the widely used Denavit–Hartenberg framework, earning 14 citations. His work on humanoid robots dynamically stepping over consecutive large obstacles addresses critical challenges in rescue robotics, while his research on hopping robots and slope-walking extends the versatility of legged systems in unstructured environments. Guo's broader portfolio — spanning SCARA welding robots to center-of-mass height compensation in gait generation — reflects a researcher committed to bridging theoretical rigor with practical application, making his work particularly valuable for students and engineers working at the frontier of robot motion planning and control.

Research Focus

Key Achievements

5
H-Index
9
Papers
61
Total Citations
7
Avg Citations/Paper
🏆 Most Cited Paper
Energy-efficient bio-inspired gait planning and control for biped robot based on human locomotion analysis
17 citations · 2016
📈 Most Prolific Year: 2016 (3 Papers)
🤝 Key Collaborators: 19
🏛 Institutions: Chinese Academy of Sciences, University of Science and Technology of China, Hefei Institutes of Physical Science, Changzhou Vocational Institute of Engineering

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago