Papers
3
Total Citations
8
H-Index
2
About
Guizhi Yang is a robotics researcher whose work focuses on bio-inspired control systems and adaptive sensing for autonomous robots. Her key research areas include central pattern generator (CPG) models for snake-like robot locomotion, monocular vision-based positioning, and noise-adaptive filtering. Yang’s major contribution is the development of a Hierarchical Connectionist Central Pattern Generator (HCCPG) model, which addresses the challenge of generating phase-coordinated, multi-degree-of-freedom control signals for three-dimensional gaits in snake-like robots. This hierarchical structure—comprising rhythm generation, pattern formation, and motor signal adjustment layers—enables more natural and adaptable locomotion, overcoming limitations of earlier CCPG models. Her work on adaptive filtering structures for monocular vision further advances robotic positioning in real-world workshops where noise parameters are unknown. While her most-cited papers have modest citation counts (2–3 each), they represent foundational steps in bio-inspired robotics and practical sensor integration. Yang’s research bridges biological neural mechanisms and robotic control, offering insights for students and researchers interested in CPG-based locomotion, snake robotics, and vision-guided autonomy.
Research Focus
Key Achievements
Top Papers
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