Papers
2
Total Citations
3
H-Index
1
About
Dr. Zhenglin Yu is a robotics researcher whose work bridges the critical gap between human-machine collaboration and intelligent agricultural automation. His research focuses on collaborative robotic systems, dynamic target detection, and the application of advanced perception algorithms to real-world harvesting challenges. Dr. Yu’s foundational work, “Movement Characteristics Analysis and Dynamic Simulation of Collaborative Measuring Robot” (2017), established key methodologies for modeling and simulating collaborative robots—specifically the UR10—using D-H coordinate transformations, providing a framework that has informed subsequent human-robot interaction studies. More recently, his 2025 paper “TransSSA: Invariant Cue Perceptual Feature Focused Learning for Dynamic Fruit Target Detection” introduces a novel deep learning approach that tackles the core challenges of fruit recognition and localization in automated harvesting. By focusing on invariant perceptual features, this work promises to significantly enhance the precision and efficiency of harvesting robots, directly addressing a bottleneck in agricultural robotics. With a growing citation footprint, Dr. Yu’s contributions are shaping the future of both collaborative manufacturing and smart agriculture, demonstrating a clear trajectory from foundational simulation to cutting-edge, application-driven perception systems.
Research Focus
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Top Papers
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