Yejing Tang
Papers
1
Total Citations
14
H-Index
1
About
Yejing Tang is a robotics researcher whose work focuses on bio-inspired locomotion and control systems for legged robots, particularly in challenging, unstructured environments. Their most-cited paper, "CPG Modulates the Omnidirectional Motion of a Hexapod Robot in Unstructured Terrain" (2022, 14 citations), introduces a central pattern generator (CPG)-based framework that enables hexapod robots to achieve smooth, omnidirectional movement across uneven terrain. This contribution is pivotal for advancing adaptive robotic mobility, offering a robust alternative to traditional gait controllers by mimicking biological neural circuits. Tang’s research bridges computational neuroscience and practical robotics, demonstrating how CPG models can enhance stability and maneuverability without complex sensor feedback. While still early in their career, Tang’s work has already garnered attention for its potential applications in search-and-rescue, planetary exploration, and agricultural robotics. Their approach to integrating CPG modulation with real-time terrain adaptation represents a significant step toward more resilient autonomous systems. As the field of legged robotics evolves, Tang’s contributions provide a foundation for future studies on energy-efficient, versatile locomotion in unpredictable settings.
Research Focus
Key Achievements
Top Papers
- 1