Shurong Ning

Beijing Union University, Shandong University

Papers

Total Citations

H-Index

About

Shurong Ning is a researcher whose work bridges computer vision and robotics, focusing on intelligent systems that perceive and act in the real world. In computer vision, Ning made a significant contribution with the highly cited 2017 paper "Pedestrian Detection Method Based on Faster R-CNN" (48 citations), which advanced object recognition for critical applications including intelligent monitoring, autonomous driving, and robotics. This work addressed the persistent challenge of detecting pedestrians amidst complex, cluttered backgrounds—a fundamental problem for safe autonomous navigation. In robotics, Ning tackled the equally demanding problem of precise control under uncertainty. The 2014 paper "Computed-torque plus robust adaptive compensation control for robot manipulator with structured and unstructured uncertainties" (15 citations) introduced a novel control scheme that combines computed-torque control with adaptive fuzzy algorithms and robust H∞ control. This hybrid approach effectively compensates for both predictable and unpredictable dynamic uncertainties, enabling more reliable trajectory tracking for robotic manipulators. Together, these contributions demonstrate Ning’s dual expertise in perception and control, addressing core challenges in making autonomous systems both aware and responsive.