Papers
3
Total Citations
22
H-Index
2
About
Yingzhuo Cao is a rising researcher in bioinspired robotics, with a focused expertise in the design and control of biomimetic underwater vehicles, particularly manta ray robots. His work bridges biology and engineering, aiming to replicate the extraordinary swimming efficiency of marine animals. Cao’s major contributions center on developing advanced control systems that enable autonomous, agile, and energy-efficient locomotion. He pioneered the use of Central Pattern Generators (CPGs)—neural-inspired circuits—to produce coordinated, rhythmic fin motions for robotic manta rays, achieving closed-loop control for autonomous swimming. His research further extends to optimizing gliding and flapping propulsion through online algorithms, allowing real-time adaptation to dynamic underwater environments. Notably, Cao introduced a NSGA-II optimization-based CPG phase transition method, enhancing maneuverability by smoothly shifting between swimming gaits. His most-cited paper, “Bioinspired Closed-loop CPG-based Control of a Robotic Manta for Autonomous Swimming” (2023), has garnered 12 citations, reflecting growing interest in his approach. By prioritizing bionic swimming postures over mere visual mimicry, Cao’s work pushes the boundaries of robotic fish performance, offering promising applications in ocean exploration, environmental monitoring, and marine biology studies.
Research Focus
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Top Papers
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