Zhengwei Bao

Nanjing University of Information Science and Technology, Tianjin University

Papers

Total Citations

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About

Zhengwei Bao is a researcher whose work bridges the critical domains of underwater robotics, computer vision, and advanced manufacturing. His primary research areas include underwater target detection, robotic milling, and surface topography prediction. Bao’s major contribution lies in developing a parallel high-resolution network (HRNet) for underwater target detection, which addresses the persistent challenges of complex underwater scenes and limited feature extraction capabilities. This work, published in 2023, has already garnered 13 citations, highlighting its immediate impact on the field. Additionally, Bao has pioneered a novel surface topography prediction method for hybrid robot milling, accounting for the dynamic displacement of the end effector—a significant advancement for precision manufacturing. This 2024 publication has accumulated 5 citations, demonstrating its relevance to industry and academia. Bao’s research not only enhances autonomous underwater vehicle perception but also improves robotic machining accuracy, showcasing his ability to solve real-world engineering problems. His work is particularly notable for its interdisciplinary approach, combining deep learning with mechanical dynamics. For students and researchers, Bao’s contributions offer a compelling example of how computational methods can revolutionize traditional engineering challenges, making him a rising figure in intelligent robotics and manufacturing.