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About

Yilong Zhang is a leading researcher in the fields of brain-machine interfaces (BMI), robotic control, and multi-agent systems, with a particular focus on snake robots and augmented reality. His most impactful work introduces a machine-vision fused BMI that leverages dynamic augmented reality visual stimulation (AR-VS), achieving a 64% faster brain-controlled hybrid task performance—including self-moving and object grabbing—compared to traditional steady-state visual evoked potential paradigms. This breakthrough, cited 15 times, demonstrates significant potential for real-world robotic applications. Zhang has also made substantial contributions to snake robot locomotion, developing anti-disturbance path-following control methods for spiral motion (14 citations) and trajectory tracking control that eliminates time-varying interferences (11 citations). His recent work on collaborative line-of-sight guidance-based robust formation control for multi-snake robot systems (8 citations) enables fixed formation tracking of multiple parametric paths, addressing challenges in uncertain environments. With over 48 citations across his top papers, Zhang’s research advances autonomous robotics, enhancing adaptability and precision in complex terrains, and holds promise for applications in search-and-rescue, exploration, and assistive technologies.