Yixiao Zhang
Papers
1
Total Citations
23
H-Index
1
About
Yixiao Zhang is a robotics researcher whose work centers on bio-inspired locomotion control for legged robots, particularly quadruped systems. Their most-cited paper, “A CPG-based gait planning and motion performance analysis for quadruped robot” (2022, 23 citations), introduces a novel motion control strategy that integrates central pattern generators (CPGs) with back-propagation neural networks (BPNN). By leveraging the Kuramoto phase oscillator, Zhang’s approach achieves stable, adaptive gait planning that significantly enhances motion performance—a critical advancement for robots navigating complex, unstructured terrains. This work bridges computational neuroscience and practical robotics, offering a robust framework for generating rhythmic, energy-efficient locomotion without relying on complex sensor feedback. Zhang’s contributions are particularly impactful in the fields of bio-inspired robotics and control systems, where their methodology has been cited as a foundation for further studies in adaptive gait generation. With a growing citation record, Yixiao Zhang is establishing themselves as a key contributor to the next generation of autonomous, animal-like robotic systems.
Research Focus
Key Achievements
Top Papers
- 1