Qingquan Na

Nanjing University of Science and Technology

Papers

Total Citations

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About

Qingquan Na is a pioneering researcher at the intersection of assistive robotics and brain-computer interfaces, with key contributions in navigation systems for the visually impaired and neural control of mobile robots. His most impactful work, "Improving Walking Path Generation Through Biped Constraint in Indoor Navigation System for Visually Impaired Individuals" (2024, 11 citations), introduces a novel walking path generation method for the Smart Cane—a Robotic Navigation Assistance device. By integrating the Linear Inverse Pendulum Model (LIPM) with bipedal constraints, Na significantly enhances indoor navigation safety and efficiency for visually impaired users, addressing a critical gap in assistive technology. In a complementary vein, his paper "A Brain-Controlled Mobile Robot System Integrating Deep Neural Networks and Model Predictive Control" (2024, 1 citation) advances neural control paradigms by fusing Task-Related Component Analysis filtering, deep learning, and Model Predictive Control (MPC). This work demonstrates how MPC can bridge brain-computer interfaces and robotic actuators, enabling smoother, more responsive control. Na’s research not only pushes the boundaries of human-robot interaction but also holds profound potential for improving quality of life, making him a notable figure in rehabilitation engineering and intelligent systems.