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About

Xianping Fu is a leading researcher in underwater robotics and marine intelligence, whose work spans tactile sensing, computer vision, autonomous control, and deep learning-based perception systems. His research addresses some of the most pressing challenges in ocean exploration, including reliable object detection in degraded underwater environments, autonomous manipulation, and multimodal sensing for subaquatic robots. Fu's most recognized contributions include a pioneering underwater 3D tactile tensegrity system combining triboelectric nanogenerators with deep learning (44 citations), and a series of progressively refined YOLO-based underwater object detection frameworks incorporating novel attention mechanisms—collectively accumulating over 100 citations. His early work on real-time detection of marine small objects (26 citations) helped establish foundational approaches for robotic seafood harvesting, bridging academic research with practical aquaculture applications. Beyond perception, Fu has advanced underwater robot autonomy through model predictive control for fully vectored propulsion systems and developed vignetting-correction methods that significantly improve image quality for AUV-captured footage. His consistent focus on closing the loop between sensing, perception, and autonomous action reflects a systems-level vision for intelligent marine robotics. With growing citation impact across multiple research threads, Fu represents an influential voice in the rapidly expanding field of ocean-oriented autonomous systems.