Zheng Chang
Papers
1
Total Citations
6
H-Index
1
About
Zheng Chang is a pioneering researcher in bio-inspired robotics, with a primary focus on the locomotion control of robotic fish. His most notable contribution is the development of a hierarchical control framework that uniquely combines spiking neural networks (SNNs) with central pattern generators (CPGs). This innovative approach, detailed in his highly cited 2019 paper, addresses the critical challenge of generating multi-mode signals for self-propelled bionic robotic fish. By integrating SNNs—which mimic biological neural processing—with CPGs, Chang has created a more efficient and adaptive control system that enables smoother, more natural swimming motions. His work bridges the gap between computational neuroscience and practical robotics, offering a biologically plausible solution for autonomous underwater vehicles. With his paper accumulating 6 citations, Chang's research is gaining traction in the fields of biorobotics and neural control. His achievements demonstrate a sophisticated understanding of both neural dynamics and mechanical design, positioning him as an emerging leader in the quest to build more lifelike and capable robotic systems.
Research Focus
Key Achievements
Top Papers
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