Qinghui Song

Shandong University of Science and Technology

Papers

Total Citations

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About

Qinghui Song is a leading researcher in robotics, specializing in exoskeleton technology and mobile robot locomotion. His work bridges the gap between intelligent control systems and mechanical design, with a particular focus on enhancing human–robot interaction and autonomous navigation in complex environments. Song’s most influential contribution is his 2019 study on motion pattern recognition for exoskeleton robots, which has garnered 49 citations. In this work, he pioneered the use of multimodal machine learning models to decode human intent and adapt robotic assistance in real time—a critical advancement for rehabilitation and assistive devices. More recently, Song has tackled the challenges of tracked mobile robots operating in extreme terrain. His 2024 paper investigates the stability of obstacle-crossing and applies multi-objective optimization to balance structural integrity with dynamic performance. By systematically analyzing how center-of-gravity position affects posture during traversal, he provides a framework for designing more resilient field robots. Song’s research is widely recognized for its practical impact, offering engineers and roboticists actionable insights for developing safer, more capable autonomous systems.