Zuojun Zhu
Papers
3
Total Citations
37
H-Index
3
About
Zuojun Zhu is a robotics and computer vision researcher whose work bridges intelligent control, perception, and environmental sustainability. His primary research areas include dual-arm robotic coordination, simultaneous localization and mapping (SLAM), and deep learning-based object detection for smart city applications. Zhu’s most impactful contribution is a dual-arm coordinated control strategy based on a modified sliding mode impedance controller (MSMIC(tanh)), which achieves high-accuracy position and force control for handling target objects—a critical challenge in collaborative robotics. This work has garnered 23 citations, reflecting its significance in advancing robotic manipulation. He also proposed LFM, a lightweight loop closure detection algorithm that uses binary classification and feature matching between similar key frames to improve SLAM accuracy, earning 8 citations. Additionally, Zhu developed a novel trash segregation algorithm based on an improved YOLOV4 architecture, addressing the pressing need for smart waste management in urban environments, with 6 citations. His research demonstrates a clear trajectory from fundamental robotic control to applied AI solutions for environmental challenges, showcasing his versatility and commitment to real-world impact.
Research Focus
Key Achievements
Top Papers
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