Papers
2
Total Citations
11
H-Index
2
About
Xinghua Lin is a researcher focused on bio-inspired underwater robotics, with a particular emphasis on flow sensing and obstacle localization for autonomous underwater vehicles (AUVs). Drawing inspiration from fish lateral line systems, Lin has developed novel methods to help underwater robots perceive and navigate complex aquatic environments. Their most-cited work, "A Novel Obstacle Localization Method for an Underwater Robot Based on the Flow Field" (2019, 9 citations), introduces a strategy that uses flow features—mimicking how fish sense their surroundings—to improve AUV adaptive ability in obstacle detection. Building on this, Lin's "Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics" (2021, 2 citations) advances a nonlinear signal estimation approach to better evaluate flow fields, further unraveling the mechanisms behind fish-inspired sensing. Though early in their career, Lin's contributions are carving a path toward more autonomous and resilient underwater robots, addressing critical challenges in marine exploration and environmental monitoring. Their work bridges biology and engineering, offering promising solutions for robotics in unstructured underwater settings.
Research Focus
Key Achievements
Top Papers
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