Papers
1
Total Citations
19
H-Index
1
About
Qinyi Xu is a researcher whose work sits at the intersection of computer vision, robotics, and intelligent control, with a particular focus on enabling robots to perceive and interact with dynamic environments. Their most-cited paper, "Improved Kernel Correlation Filter Based Moving Target Tracking for Robot Grasping" (2022, 19 citations), addresses a critical challenge in industrial and human-robot collaboration: the real-time tracking and grasping of moving objects. By integrating kernel correlation filter techniques with vision-based 3D reconstruction, Xu proposed a robust visual tracking and grasping method that enhances a robot’s ability to handle non-stationary targets. This contribution is significant for advancing automation in dynamic settings, such as assembly lines or collaborative workspaces. While still early in their career, Xu’s work demonstrates a clear impact in the niche of vision-guided robotic manipulation, laying a foundation for future innovations in autonomous systems. Their research holds promise for improving the dexterity and responsiveness of robots in real-world applications.
Research Focus
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Top Papers
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