Fiber Bragg Grating Whiskers for Bioinspired Hydrodynamic Perception on Underwater Robots

Hao Li; Tianyu Tu; Siyue Yao; Ziyang Chang; Juhyun Jung; Xiaochi Xie; Long Yin Chung; Tian-Ao Ren

cs.ROAug 25, 2026

Fiber Bragg Grating Whiskers for Bioinspired Hydrodynamic Perception on Underwater Robots

Authors: Hao Li, Tianyu Tu, Siyue Yao, Ziyang Chang, Juhyun Jung, Xiaochi Xie, Long Yin Chung, Tian-Ao Ren,

Organizations: Stanford University. · Shanghai Jiao Tong University. · Tsinghua University.

Abstract

Harbor seals track hydrodynamic trails with their vibrissae, enabling passive perception of moving targets in dark or turbid water. Inspired by this capability, we present compact fiber Bragg grating (FBG) whiskers for underwater robots. Like seal whiskers, they have a non-uniform taper and elliptical cross-section. Controlled towing experiments show a monotonic relative-flow response from 0.1 to 0.6 m/s, a strong reduction of self-induced oscillation relative to a cylindrical baseline, and a pronounced dependence on angle of attack. Experiments with a pitching foil show that the whiskers can detect the characteristic vortices shed by a stationary or moving source, detectable several seconds after the source has passed. Using this information, a single front-mounted whisker enabled a small underwater robot to distinguish between continuing straight and executing a turn, selecting the correct branch in 17 of 20 trials (85.0%) from whisker signals alone. These results connect bioinspired hydrodynamic sensing to robot action and suggest the utility of whiskers for tracking underwater objects.

Figures & tables

Fig. 1: Bioinspired whisker design and integration onto a robot in the field. ( A ) Hydrodynamic trail following: vortices shed by a swimming fish persist in water and may be encountered by a trailing seal after $\delta t$ . This delay depends on the relative position and motion of source and observer, as well as on how long coherent vortices remain detectable. ( B ) Harbor seal whisker morphology and the biomimetic whisker module. (Top) Seal whiskers exhibit an elliptical cross-section and undulating taper from base to tip [adapted from Murphy et al. [ 5 ] , CC BY 4.0.] (Bottom-left) Detail of whisker shaft in this work. (Right) Whisker sensor based on Fiber Bragg Gratings (FBGs). Whisker shaft and bridge structure are designed to transfer bending moments to FBGs embedded in the base. ( C ) Detail of a whisker mounted to the nose-piece of a ROV. ( D ) Ocean field deployment. The ROV (carrying whisker sensors on both sides of a plastic seal-model housing) follows a human diver while recording FBG wavelength shifts $\Delta\lambda_{1}$ and $\Delta\lambda_{2}$ (lower-left trace). $\delta t$ denotes the elapsed time between a fin stroke and the arrival of the corresponding wake-induced spike at the whisker. The diver’s fin oscillates with frequency $f$ and angular amplitude $\theta$ (measured from the neutral position to peak excursion). $v_{\text{d}}$ and $v_{\text{r}}$ denote the swimming speeds of the diver and the ROV, respectively, and $d$ is the standoff distance between the diver’s fin and the whisker sensor.

Fig. 2: Whisker morphology, fabrication process, and characterization. ( A ) Following [ 4 ] , the whisker profile is described by the semi-axis lengths of the peak (

a

b

) and trough (

k

l

) elliptical cross-sections, the inclination angles

\alpha

and

\beta

, and the undulation wavelength

\Lambda

. ( B ) Photograph of an assembled whisker sensor. One or two optical fibers carrying FBG 1 and FBG 2 are seated in orthogonal grooves on a cross-bridge structure atop a 3D-printed base. The NiTi core of the whisker shaft is bonded at two points: near the cross-bridge center and at the base surface. ( C ) Molding workflow used to fabricate the whisker shaft. ( D ) Recovery characterization under large underwater deflection. Three video frames are overlaid on the FBG 1 wavelength trace, with the whisker shaft outlined in red for visibility.

Fig. 3: Parametric design for the whisker. ( A ) The testing setup uses a linear stage to apply motions and a load cell to acquire the contact force magnitude. ( B ) A typical comparison between the captured photo and the simulated result of the whisker under a specific load. The inset shows simulated results under different loads. ( C ) Successive stages of a deflected whisker and plots of contact forces and wavelength shifts. Dotted vertical lines denote states (I-IV) corresponding to photos. ( D, E ) Sensitivity curves of designs with different bridge lengths and heights.

Fig. 4: Flow-sensing experiments. ( A ) A three-axis gantry is used for controlled towing experiments in a pool. The whisker sensor was mounted on the $z$ -axis end-effector and translated along the $x$ -axis to generate prescribed relative flow. ( B ) Schematic of mean drag-induced bending and cross-flow oscillation caused by vortex-induced vibration (VIV). Angle of attack (AoA) is defined by the orientation of the whisker’s narrow side relative to the incoming flow. ( C ) Three whisker geometries tested—wavy tapered, wavy non-tapered, and cylindrical—with representative mean-centered FBG 2 wavelength-shift traces at 0.6 m/s. ( D ) Mean baseline-referenced wavelength shift of the wavy tapered whisker versus towing speed. Error bars denote standard deviation across 5 replicate runs after the outlier-screening procedure described in Appendix B . ( E ) Representative mean-centered two-channel whisker signals at different AoAs while towing at 0.6 m/s.

Fig. 5: Wake sensing with a pitching foil. ( A ) Setup for pitching foil wake sensing. A positioning gantry mounted to the $z$ -axis of the main gantry adjusted the whisker position relative to a NACA0012 foil. Whisker measurements were collected over a prescribed scanning grid, with one whisker sensor placement corresponding to each grid location. ( B ) Co-translating foil-whisker configuration used to approximate sensing of the wake left by a moving source. ( C ) Whisker response to foil pitching at different amplitudes and frequencies, measured at $x=-100$ mm and $y=100$ mm relative to the foil tip at its initial position. Displayed traces are mean-centered for visual comparison. ( D ) Whisker response map for a pitching foil and a stationary whisker in quiescent water. Each arrow represents the sensor signal from a single test with the foil at the corresponding angle. Hence, the vectors are synchronized in terms of phase but are not simultaneous measurements. ( E ) Reconstructed whisker-response map in the co-translating finite- $\mathrm{St}$ regime. The map was assembled from repeated single-location trials; the full displayed domain was completed from two acquisition batches using the reflection procedure described in Appendix B , which assumes approximate symmetry about the trajectory centerline. ( F ) Representative two-channel FBG signals at the location marked, $\circ$ , in panel E. In (D) and (E), arrows show the two-channel response vector formed from FBG 1 and FBG 2, and color encodes the vector magnitude. These arrows represent reconstructed FBG response vectors at sampled or mirrored relative locations, not simultaneous velocity vectors or direct measurements of vorticity.

Fig. 6: Whisker-guided branch selection using a delayed foil-generated trail. ( A ) Commercial ROV with a front-mounted whisker; the visual position tracker was used for vehicle localization and evaluation, but not as an input to the whisker classifier. ( B ) Control and decision pipeline: localization supported low-level vehicle motion, while a logistic-regression classifier selected the target branch from whisker-derived features. ( C ) Mean two-channel FBG signals within the 2 s decision window for straight and turning runs, with shaded $\pm$ one standard deviation envelopes computed over held-out test windows. ( D ) Pool experiment showing gantry-mounted foil, fixed start point, decision region, and candidate branches. The ROV first moved forward to the decision point, classified the branch using the logistic-regression classifier, and then moved toward the selected target position. The start-to-decision distance is denoted by $D_{1}\approx 0.40~\mathrm{m}$ . ( E ) Receiver-operating-characteristic and precision–recall curves for the offline classifier on held-out test windows. ( F ) Online branch-selection success rates for straight and turning trials.

Reference	Principle	$L\times d_{1}\times d_{2}$ (mm)	Flow speed (m/s)	Wake source	Wake distance	System demonstration
Beem et al. [ 15 , 16 ]	Resistive	275 $\times$ 35.7 $\times$ 14.4	0.2–0.8	Cylinder	$160\,d_{w}$	Benchtop wake sensing
Liu et al. [ 17 ]	Resistive	35 $\times$ 2.14 $\times$ 1.06	0.048–0.25	Cylinder	250 mm ( $\sim 235\,d_{w}$ )	Benchtop wake sensing
Zheng et al. [ 19 ]	Resistive	150 $\times$ 11.6 $\times$ 5.0	0.1	Cylinder	80 mm ( $\sim 10\,d_{w}$ )	Benchtop wake sensing
Guo et al. [ 44 ]	Piezoelectric	210 $\times$ 15 $\times$ 7.5	0.25–0.5	Cylinder	240 mm ( $\sim 16\,d_{w}$ )	Benchtop wake sensing
Wang et al. [ 22 ]	TENG	150 $\times$ N/A $\times$ N/A	N/A	Fishtail	750 mm	Benchtop wake sensing
Dai et al. [ 23 ]	Magnetic	10.92 $\times$ N/A $\times$ N/A	max $0.6^{\dagger}$	Fishtail	150 mm ( $\sim 95\,d_{w}$ )	Array sensing

TABLE I: Representative underwater whisker sensors. Flow speed lists nonzero validation conditions; wake distance is reproduced as reported and, when available, normalized by

d_{w}

. APPENDIX A SUMMARIZES THE COMPARISON CONVENTIONS.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Parameter	Symbol	Value
Peak semi-axes	$a\times b$	$1.575\times 0.534$ mm
Trough semi-axes	$k\times l$	$1.248\times 0.657$ mm
Inclination angles	$\alpha,\beta$	$15.27^{\circ},17.6^{\circ}$
Undulation period	$\Lambda$	$3.5$ mm
Taper offset	$t_{0}$	$0.029$
Total length	$L_{t}$	$\sim 100$ mm

Appendix

TABLE II: Whisker geometry used in the experiments. Peak and trough dimensions are elliptical semi-axes at the base.

Fig. 7: Cyclic-loading durability of the selected whisker design. FBG 1 wavelength shifts recorded over 500 consecutive indentation cycles. Insets compare representative early and late cycles, showing no obvious loss of mechanical-to-optical response over the tested sequence.

Fig. 8: Discretization-based analytical model. The whisker and the four base-bridge branches are divided into elastic segments. Each segment is represented by an equivalent six-DoF serial mechanism comprising three prismatic and three revolute joints, with stiffness assigned from local geometry and material properties. Coordinate frames are defined for the whisker and individual bridge branches, yielding a coupled parallel–serial hybrid mechanism subject to external loads $\bm{F}_{i}$ and interaction loads $\bm{F}_{B,i}$ .

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Robotic perception enables robots to interpret scenes, objects, motion, and contact from cameras and other sensors to guide action.

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

Xiaochi Xie, Hao Li, Shixuan Zhao, Siyue Yao+2

Department of Mechanical Engineering, Stanford University, USA · State Key Laboratory of Mechanical Systems and Vibration; META Robotics Institute, Shanghai Jiao Tong University, Shanghai 200240, China · Department of Electrical Engineering, Stanford University, USA

Vision-Based Tactile Sensing

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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.

Abstract

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