Xinyuan Ying

Papers

Total Citations

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About

Xinyuan Ying is a researcher whose work addresses fundamental challenges in robotic perception and manipulation, particularly the visual recognition of targets in complex, cluttered environments. His key research areas include computer vision for robotics, sub-pixel image processing, and autonomous systems. Ying’s most notable contribution is his work on the sub-pixel Harris corner detection algorithm, which enhances a robot’s ability to identify and interact with objects that are partially obscured, in dead corners, or surrounded by other targets—a persistent problem in real-world robotics. His 2021 paper on this topic, which has garnered 4 citations, demonstrates a practical approach by using V-REP and MATLAB for joint simulation, bridging the gap between theoretical vision algorithms and deployable robotic systems. This work is particularly valuable for applications in manufacturing, logistics, and service robotics, where precise object recognition under non-ideal conditions is critical. By tackling the “complex state” problem, Ying contributes to making robots more reliable and autonomous in dynamic, unpredictable settings. His research continues to push the boundaries of how machines perceive and interact with their environment, offering solutions that are both computationally efficient and practically robust.