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About

Qiang Ling is a robotics researcher whose work bridges mechanical design, control systems, and intelligent automation. His primary research areas include robotic manipulator design, control theory for uncertain systems, and machine learning applications in robotics. Ling's most impactful contribution is the kinematic modeling and simulation of an economical SCARA manipulator (13 citations), demonstrating a complete mechanical design process optimized for industrial precision and rigidity. He has also advanced control strategies for delta robots handling uncertain loads, proposing a variable structure compensator to improve conventional computed-torque control stability. Ling's work extends to mobile robotics, where he designed dynamic controllers for differential drive wheeled robots, and to addressing nonlinear challenges like backlash in drive systems through novel switched control strategies. More recently, he has explored dual attention networks for point cloud classification and segmentation, applying deep learning to autonomous driving and robotic perception. His research on XiaoA, a robot editor for online news popularity prediction using ensemble learning, showcases his versatility in applying robotics concepts to data-driven tasks. With publications spanning from 2011 to 2022, Ling's work consistently addresses practical challenges in robotic systems, from mechanical design to intelligent control.