发表机构
Stanford University; Shanghai Jiao Tong University（斯坦福大学; 上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出荧光增强须阵列系统，通过视觉变形分析实现水下流体动力源定位，实验误差88毫米，适用于低光动态场景。
AI 中文摘要
深水生物观测对于理解海洋生物及其与环境的相互作用至关重要。然而，传统的光学和声学方法可能引入刺激，改变动物行为并导致生物观测偏差。本文提出了一种荧光增强须阵列传感系统，通过局部光学读出而非直接源成像来定位水下流体动力源。该系统集成了五个空间定向的须，由镍钛诺芯和荧光聚氨酯外壳制成，并配备紫外激发和单目相机。应用图像增强和分割来跟踪须的变形。一个轻量级卷积神经网络从2秒序列中捕获时间和跨须特征以估计源位置。水池实验在600毫米且±30°的测试区域内实现了88毫米的平均空间定位误差，其中半径误差为73.5毫米，角度误差为2.5°。对移动推进器的实时定位展示了所提方法在动态场景中的能力，突显了其集成到水下机器人中用于低光环境下流体动力源检测、定位和跟踪的潜力。
英文摘要
Deep-water biological observation is essential for understanding marine organisms and their interactions with the environment. However, conventional optical and acoustic approaches can introduce stimuli that alter animal behavior and bias biological observations. This paper proposes a fluorescence-enhanced whisker array sensing system that pinpoints underwater hydrodynamic sources through local optical readout rather than direct source imaging. Five spatially oriented whiskers, fabricated with nitinol cores and fluorescent urethane shells, are integrated with ultraviolet excitation and a monocular camera. Image enhancement and segmentation are applied to track the whisker deformation. A lightweight convolutional neural network captures temporal and cross-whisker features from 2 s sequences to estimate source localization. Pool experiments achieve a mean spatial localization error of 88 mm, with 73.5 mm in radius and $2.5^\circ$ in angle, across a test region of 600 mm with $\pm30^\circ$. Real-time localization of a moving thruster demonstrates the capability of the proposed method in dynamic scenarios, highlighting its potential for integration into underwater robots for hydrodynamic source detection, localization, and tracking in low-light environments.
Comments8 pages, 8 figures
