Mengfan Gui

Nanjing University of Science and Technology

Papers

Total Citations

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About

Mengfan Gui is a rising researcher at the intersection of assistive robotics and brain-computer interfaces (BCIs), whose work focuses on enhancing mobility and autonomy for individuals with disabilities. Gui’s key research areas include robotic navigation assistance, human-robot interaction, and neural signal processing. In their most cited work, “Improving Walking Path Generation Through Biped Constraint in Indoor Navigation System for Visually Impaired Individuals” (2024, 11 citations), Gui developed a novel path generation method for the Smart Cane—a robotic navigation assistance device—by integrating a Linear Inverse Pendulum Model (LIPM) to account for bipedal constraints, significantly improving indoor navigation safety and efficiency for visually impaired users. Another notable contribution, “A Brain-Controlled Mobile Robot System Integrating Deep Neural Networks and Model Predictive Control” (2024, 1 citation), demonstrates Gui’s innovation in combining Task-Related Component Analysis (TRCA) filtering with deep learning and Model Predictive Control (MPC) to create a seamless, real-time brain-controlled mobile robot system. This work bridges BCI technology with predictive control, enabling more responsive and reliable assistive devices. Though early in their career, Gui’s research has already garnered attention for its practical, user-centered approach, promising to advance both assistive robotics and neural control systems.