Papers
1
Total Citations
19
H-Index
1
About
Maoxi Zheng is a robotics researcher whose work bridges computer vision and autonomous manipulation, with a particular focus on enabling robots to track and grasp moving objects in dynamic environments. His 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 settings. Zheng’s key contribution lies in integrating kernel correlation filter tracking with vision-based 3D reconstruction, creating a robust method that allows robots to visually track and intercept moving targets in real time. This work has significant implications for manufacturing automation and collaborative robotics, where safe and precise interaction with moving objects is essential. By improving tracking accuracy and computational efficiency, Zheng’s approach enhances the reliability of robotic grasping systems. His research is particularly valuable for students and engineers working on visual servoing, object tracking, and robot manipulation. With a growing citation record, Zheng is establishing himself as a promising voice in applied robotics, advancing the practical deployment of vision-guided systems in real-world industrial settings.
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Top Papers
- 1