Yupei Huang
Papers
6
Total Citations
58
H-Index
4
About
Yupei Huang is a leading researcher in biomimetic underwater robotics, specializing in autonomous navigation, sensor fusion, and learning-based control for robotic fish and manta systems. Their major contributions center on developing real-world, deployable solutions for underwater exploration, where traditional GPS and communication signals are unavailable. Huang pioneered the use of tightly coupled sensor fusion—integrating monocular cameras, inertial measurement units, pressure sensors, and Doppler velocity logs—to dramatically improve localization accuracy for bio-inspired robots. Their work on visual-acoustic and visual-DVL fusion, detailed in papers with 13 and 6 citations respectively, addresses the critical challenge of drift in underwater SLAM systems. Notably, Huang’s 2022 study on real-world deep reinforcement learning for a biomimetic robotic shark (27 citations) introduced methods to enhance data quality and sampling efficiency, enabling more effective autonomous control in complex aquatic environments. They also designed a fish-like binocular vision system inspired by biological eye arrangements, expanding the perceptual field for underwater robots. With recent work on active SLAM with dynamic viewpoint optimization (2025), Huang continues to push the boundaries of robust visual navigation, making their research essential for students and engineers advancing autonomous underwater vehicles.
Research Focus
Key Achievements
Top Papers
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