Yingying Wang

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About

Yingying Wang is a robotics and control systems researcher whose work centers on bio-inspired locomotion algorithms and autonomous robotic systems. Her most recognized contribution is the development of a Central Pattern Generator (CPG) control algorithm for climbing worm robots, in which she designed locomotion frameworks based on neural oscillator networks (NON). This research introduced innovative gait definitions — specifically trapezoidal wave and triangular wave gaits — that mimic biological movement patterns to enable stable, adaptive climbing behavior in robotic systems. By drawing on principles of neuroscience and applying them to mechanical locomotion, Wang's work bridges the gap between biological motion intelligence and practical robotic engineering. Her research has garnered 10 citations, reflecting its value as a foundational reference within the specialized field of bio-inspired robotics and worm-like locomotion control. This work is particularly noteworthy for students and researchers exploring how cyclic inhibitory neural networks can be harnessed to generate rhythmic, coordinated movement in non-traditional robot morphologies — an area of growing relevance as robotics expands into confined-space inspection, search-and-rescue, and medical applications.