Fluorescent whisker sensor array locates underwater sources accurately

Fluorescence-enhanced Whisker Array with Vision-based Deformation Analysis for Underwater Source Localization

Robotics

Summary

Detecting underwater creatures and things moving in the water often changes their behavior because of bright lights or sonar. This paper describes a new way to find underwater sources by using tiny, flexible whiskers coated with glowing material that lights up under UV light. As water flows hit these whiskers, they bend, and a camera tracks the bending to figure out where the source is. The researchers built a smart system to understand these movements and could find sources within about 9 centimeters in a 60-centimeter area, even when the source was moving. This could help underwater robots sense things quietly without disturbing marine life.

What this means in practice

  • For underwater robot developers: Add the fluorescent whisker array to robots for detecting and tracking underwater flow sources without disturbing marine life in low-light environments.
  • For marine monitoring teams: Use the system to pinpoint flow sources affecting marine animals while avoiding stimuli that change natural behavior during observation.

Authors

Xiaochi Xie, Hao Li, Shixuan Zhao, Siyue Yao, Shuran Song, Mark R. Cutkosky

Abstract

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.
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