Papers
11
Total Citations
420
H-Index
8
About
Daqi Zhu is a leading researcher in autonomous underwater vehicle (AUV) systems, with a primary focus on multi-AUV coordination, formation control, and intelligent path planning in complex ocean environments. His most impactful work includes the development of a bio-inspired neural network (GBNN) algorithm for complete coverage path planning (122 citations), which has become a foundational approach in the field. Zhu has made significant contributions to formation reconfiguration and obstacle avoidance, notably through affine transformation and improved artificial potential field methods that account for ocean current disturbances—a critical challenge distinguishing underwater robotics from ground-based systems. His research on multi-AUV hunting algorithms in unknown environments (56 citations) and distributed adaptive formation control in 3D ocean spaces demonstrates his leadership in cooperative robotics. More recently, Zhu has expanded into environmental applications, developing YOLOv7t-CEBC networks for underwater litter detection (2024). With over 400 total citations across his most-cited papers, his work bridges theoretical control algorithms with practical underwater navigation challenges, establishing him as a key figure in advancing autonomous marine robotics for both military and environmental monitoring applications.
Research Focus
Key Achievements
Top Papers
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- 7YOLOv7t-CEBC Network for Underwater Litter Detection18 citations · 2024
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- 9A multi-AUV hunting algorithm with ocean current effect6 citations · 2015
- 10A survey of cooperative hunting control algorithms for multi-AUV systems6 citations · 2013