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About

Jipeng Huang is a researcher at the forefront of underwater robotics and computer vision, with a focus on enhancing autonomous perception in aquatic environments. His work spans underwater image enhancement, animal behavior analysis, and robotic navigation. Huang’s most cited paper, “Underwater Image Enhancement via Modeling White Degradation” (2024, 11 citations), addresses the critical challenge of light absorption and scattering in water, improving imaging quality for underwater robots. He also developed a pose estimation-based visual perception system for analyzing fish swimming (2024, 9 citations), leveraging deep learning to revolutionize animal movement research. His earlier work on AGV navigation using AprilTags2 auxiliary positioning (2019, 3 citations) demonstrates his versatility in robotics. Notably, Huang contributed to the LUO-V2 and MSPerception project (2024, 2 citations), a multi-fin co-drive robotic fish with a multi-sensor experimental system, showcasing his ability to integrate hardware and software for bio-inspired robotics. With a growing citation impact, Huang’s research is pivotal for advancing autonomous underwater vehicles and ecological monitoring, offering practical solutions for real-world aquatic challenges.