Development of an artificial sensor for hydrodynamic detection inspired by a seal's whisker array

Biomimetic seal whisker sensors for high-sensitivity wake detection and localization

Pinnipeds, with highly sensitive whiskers, can detect instantaneous spatial hydrodynamic disturbances, crucial for tracking wakes and their sources. However, no existing engineering solution replicates this for intelligent passive flow perception. To bridge this gap, we propose a low-cost, whisker-inspired sensor designed for use in arrays for underwater sensing and tracking. The sensor integrates metal foil strain gages within a polydimethylsiloxane soft base, coupled with a 3D-printed biomimetic seal whisker model. It exhibits low self-noise in undisturbed flow and high sensitivity in wake detection, identifying flow speeds as low as 0.5 mm s-1-comparable to biological whiskers (∼0.25 mm s-1). The dual strain gage design, placed on adjacent perpendicular sides, allows precise measurement of whisker bending amplitude and direction. The sensor shows excellent linearity, repeatability, fatigue life, short response time and superior dynamic performance in the low-frequency range (⩽35 Hz). Despite its high performance, it is cost-effective and easy to fabricate, requiring no specialized facilities or extensive training, making it ideal for large-scale array deployment. To demonstrate its potential, we tested a nine-sensor array capable of predicting dipole source locations using an artificial neural network model. This work demonstrates the feasibility of whisker-inspired sensing for robust spatial flow perception in underwater environments.

Shape Classification Using a Single Seal-Whisker-Style Sensor Based on the Neural Network Method

Seals, sea lions, and other aquatic animals rely on their whiskers to identify and track underwater targets, offering valuable inspiration for the development of low-power, portable, and environmentally friendly sensors. Here, we design a single seal-whisker-like cylinder and conduct experiments to measure the forces acting on it with nine different upstream targets. Using sample sets constructed from these force signals, a convolutional neural network (CNN) is trained and tested. The results demonstrate that combining the seal-whisker-style sensor with a CNN enables the identification of objects in the water in most cases, although there may be some confusion for certain targets. Increasing the length of the signal samples can enhance the results but may not eliminate these confusions. Our study reveals that high frequencies (greater than 5 Hz) are irrelevant in our model. Lift signals present more distinct and distinguishable features than drag signals, serving as the primary basis for the model to differentiate between various targets. Fourier analysis indicates that the model's efficacy in recognizing different targets relies heavily on the discrepancies in the spectral features of the lift signals.

Deciphering the connection between upstream obstacles, wake structures, and root signals in seal whisker array sensing using interpretable neural networks

This study presents a novel method that combines a computational fluid-structure interaction model with an interpretable deep-learning model to explore the fundamental mechanisms of seal whisker sensing. By establishing connections between crucial signal patterns, flow characteristics, and attributes of upstream obstacles, the method has the potential to enhance our understanding of the intricate sensing mechanisms. The effectiveness of the method is demonstrated through its accurate prediction of the location and orientation of a circular plate placed in front of seal whisker arrays. The model also generates temporal and spatial importance values of the signals, enabling the identification of significant temporal-spatial signal patterns crucial for the network's predictions. These signal patterns are further correlated with flow structures, allowing for the identification of important flow features relevant for accurate prediction. The study provides insights into seal whiskers' perception of complex underwater environments, inspiring advancements in underwater sensing technologies.

Artificial Whisker Sensor with Undulated Morphology and Self-Spread Piezoresistors for Diverse Flow Analyses

Harbor seal whiskers possess an undulated surface morphology that can effectively modify the vortex street behind the whiskers and suppress vortex-induced vibrations (VIVs). In this study, we propose a novel piezoresistive flow sensor that mimics the function of seal whiskers. The sensor consists of a bionic whisker with an undulated morphology and integrated out-of-plane piezoresistors. The piezoresistors are formed using a novel directional liquid spreading method to deliver a conductive nanocomposite ink into four Ω-shaped microchannels. Steady flow experiments indicate that the undulated morphology of the artificial whisker significantly reduces the drag forces and VIVs of the whisker at an angle of attack of 0°. Moreover, the whisker sensor can measure the oscillatory flow, which reaches a threshold detection limit of 8 mm/s. In addition, we demonstrate the function of the artificial whisker sensor to distinguish various wakes induced by upstream cylinders. Therefore, the facile fabrication and preliminary experiments of the artificial whisker sensor demonstrate its potential application in diverse flow analyses.

Morphological peculiarities of a harbour seal (Phoca vitulina) whisker revealed by normal skeletonisation

Of all mammalian vibrissae, those of certain species of pinnipeds are exceptional. Researchers believe that their curious undulating form evolved for hydrodynamic detection. Our understanding of how these whiskers work depends on a geometrical model that captures the crucial pertinent features of the natural vibrissae including its tapering and curvature. It should also account for the form of the whisker when it flexes under external loading. We introduce and study a normal skeleton of a two-dimensional projection of a harbour seal whisker. The normal skeleton is a complete shape descriptor that involves reduction to the centreline equipped with a thickness function of the orthogonal cross-section. The contours of the whisker shape are extracted from a 2D greyscale scan. Our analysis reveals correspondence between the undulations of the width and oscillations of the centreline curvature as functions of arc length. We discuss two possible explanations for that remarkable feature: one based on consideration of growth and the other of plastic deformation. For the latter we employ a mechanical model to demonstrate appearance of curvature oscillations caused by extensive deflection of the undulating whisker due to external loading.

A novel whisker sensor with variable detection range for object positioning

The design of a whisker sensor, inspired by mammalian whisker characteristics, is presented in this paper. It uses a novel spring structure to transfer the deformation generated by the whisker tip when it touches an object at the base, which drives the permanent magnet installed at the base to change its position. It achieves precise positioning of the object by using the magnetic induction intensity data output from the Hall sensor MLX90393. Based on the results of the finite element model analysis, the detection range of the whisker sensor can be expanded by replacing the artificial whisker material and selecting a permanent magnet of a suitable size. Calibration experiments and positioning tests were conducted on the sensor. The experimental results showed that the detection radius of the sensor was 24, 30, 33, and 39 mm for the carbon fiber, acrylic, acrylonitrile butadiene styrene plastic (ABS), and nylon whiskers, respectively, when they were matched with a NdFeB annular permanent magnet with an aperture of 3 mm and a thickness of 3 mm. The sensor is small and simple to manufacture with good sensitivity, linearity, hysteresis, and repeatability. The maximum positioning errors of the X and Y positions in the detection plane of the sensor were within ±1.3 mm, and the positioning was accurate. The sensor can be used to identify the shape of an object.

Flow-signal correlation in seal whisker array sensing

Phocid seals detect and track artificial or biogenic hydrodynamic trails based on mechanical signals of their whisker arrays. In this paper, we investigated the correlations between flow structures and whisker array signals using a simplified numerical model of fluid-structure interaction (FSI). Three-dimensional (3D) wakes of moving paddles in three different shapes (rectangular plate, undulated plate, and circular cylinder) were simulated using an in-house immersed-boundary-method-based computational fluid dynamics solver. One-way FSI was then simulated to obtain the dynamic behavior and root signal of each whisker in the two whisker arrays on a seal head in each wake. The position, geometry, and material properties of each whisker were modeled based on the measurements reported in literatures. The correlations between the wake structures and whisker array signals were analyzed. It was found that the patterns of the signals on the whisker arrays can reflect the strength, timing, and moving trajectories of the jets induced by the vortices in the wakes. Specifically, the rectangular plate generates the strongest starting vortex ring as well as the strongest jets, while the undulated plate generates the weakest ones. These flow features are fully reflected by the largest whisker signal magnitude in the rectangular plate sensing and the smallest one in the undulated plate sensing. Moreover, the timing of the signal initiation and the maximum signal agree well with the timing of the jet reaching the arrays and the maximum flow speed, respectively. The correlation coefficient between the moving trajectories of the jet and the movement of the high signal level region in the array was found to be higher than 0.9 in the rectangular plate case. The results provide a physical insight into the mechanisms of seal whisker flow sensing.

Creating underwater vision through wavy whiskers: a review of the flow-sensing mechanisms and biomimetic potential of seal whiskers

Seals are known to use their highly sensitive whiskers to precisely follow the hydrodynamic trail left behind by prey. Studies estimate that a seal can track a herring that is swimming as far as 180 m away, indicating an incredible detection apparatus on a par with the echolocation system of dolphins and porpoises. This remarkable sensing capability is enabled by the unique undulating structural morphology of the whisker that suppresses vortex-induced vibrations (VIVs) and thus increases the signal-to-noise ratio of the flow-sensing whiskers. In other words, the whiskers vibrate minimally owing to the seal's swimming motion, eliminating most of the self-induced noise and making them ultrasensitive to the vortices in the wake of escaping prey. Because of this impressive ability, the seal whisker has attracted much attention in the scientific community, encompassing multiple fields of sensory biology, fluid mechanics, biomimetic flow sensing and soft robotics. This article presents a comprehensive review of the seal whisker literature, covering the behavioural experiments on real seals, VIV suppression capabilities enabled by the undulating geometry, wake vortex-sensing mechanisms, morphology and material properties and finally engineering applications inspired by the shape and functionality of seal whiskers. Promising directions for future research are proposed.

A Triboelectric-Based Artificial Whisker for Reactive Obstacle Avoidance and Local Mapping

Since designing efficient tactile sensors for autonomous robots is still a challenge, this paper proposes a perceptual system based on a bioinspired triboelectric whisker sensor (TWS) that is aimed at reactive obstacle avoidance and local mapping in unknown environments. The proposed TWS is based on a triboelectric nanogenerator (TENG) and mimics the structure of rat whisker follicles. It operates to generate an output voltage via triboelectrification and electrostatic induction between the PTFE pellet and copper films (0.3 mm thickness), where a forced whisker shaft displaces a PTFE pellet (10 mm diameter). With the help of a biologically inspired structural design, the artificial whisker sensor can sense the contact position and approximate the external stimulation area, particularly in a dark environment. To highlight this sensor's applicability and scalability, we demonstrate different functions, such as controlling LED lights, reactive obstacle avoidance, and local mapping of autonomous surface vehicles. The results show that the proposed TWS can be used as a tactile sensor for reactive obstacle avoidance and local mapping in robotics.

Lightscapes of fear: How mesopredators balance starvation and predation in the open ocean

Like landscapes of fear, animals are hypothesized to strategically use lightscapes based on intrinsic motivations. However, longitudinal evidence of state-dependent risk aversion has been difficult to obtain in wild animals. Using high-resolution biologgers, we continuously measured body condition, time partitioning, three-dimensional movement, and risk exposure of 71 elephant seals throughout their 7-month foraging migrations (N = 16,000 seal days). As body condition improved from 21 to 32% fat and daylength declined from 16 to 10 hours, seals rested progressively earlier with respect to sunrise, sacrificing valuable nocturnal foraging hours to rest in the safety of darkness. Seals in superior body condition prioritized safety over energy conservation by resting >100 meters deeper where it was 300× darker. Together, these results provide empirical evidence that marine mammals actively use the three-dimensional lightscape to optimize risk-reward trade-offs based on ecological and physiological factors.

A Deep-Learning Model for Underwater Position Sensing of a Wake's Source Using Artificial Seal Whiskers

Various marine animals possess the ability to track their preys and navigate dark aquatic environments using hydrodynamic sensing of the surrounding flow. In the present study, a deep-learning model is applied to a biomimetic sensor for underwater position detection of a wake-generating body. The sensor is composed of a bundle of spatially-distributed optical fibers that act as artificial seal-like whiskers and interact with the body's wake in the form of time-variant (bending) deflections. Supervised learning is employed to relate the vibrations of the artificial whiskers to the position of an upstream cylinder. The labeled training data are prepared based on the processing and reduction of the recorded bending responses of the artificial whiskers while the cylinder is placed at various locations. An iterative training algorithm is performed on two neural-network models while using the 10-fold cross-validation technique. The models are able to predict the coordinates of the cylinder in the two-dimensional (2D) space with a high degree of accuracy. The current implementation of the sensor can passively sense the wake generated by the cylinder at Re ≃ 6000 and estimate its position with an average error smaller than the characteristic diameter D of the cylinder and for inter-distances (in the water tunnel) up to 25-times D.

An Artificial Vibrissa-Like Sensor for Detection of Flows

In nature, there are several examples of sophisticated sensory systems to sense flows, e.g., the vibrissae of mammals. Seals can detect the flow of their prey, and rats are able to perceive the flow of surrounding air. The vibrissae are arranged around muzzle of an animal. A vibrissa consists of two major components: a shaft (infector) and a follicle-sinus complex (receptor), whereby the base of the shaft is supported by the follicle-sinus complex. The vibrissa shaft collects and transmits stimuli, e.g., flows, while the follicle-sinus complex transduces them for further processing. Beside detecting flows, the animals can also recognize the size of an object or determine the surface texture. Here, the combination of these functionalities in a single sensory system serves as paragon for artificial tactile sensors. The detection of flows becomes important regarding the measurement of flow characteristics, e.g., velocity, as well as the influence of the sensor during the scanning of objects. These aspects are closely related to each other, but, how can the characteristics of flow be represented by the signals at the base of a vibrissa shaft or by an artificial vibrissa-like sensor respectively? In this work, the structure of a natural vibrissa shaft is simplified to a slender, cylindrical/tapered elastic beam. The model is analyzed in simulation and experiment in order to identify the necessary observables to evaluate flows based on the quasi-static large deflection of the sensor shaft inside a steady, non-uniform, laminar, in-compressible flow.

Seal and Sea lion Whiskers Detect Slips of Vortices Similar as Rats Sense Textures

Pinnipeds like seals and sea lions use their whiskers to hunt their prey in dark and turbid situations. There is currently no theoretical model or hypothesis to explain the interaction between whiskers and hydrodynamic fish trails. The current study, however, provides a theoretical and experimental insight into the mechanism behind the detection of the Strouhal frequency from a Von-Karman vortex street, similar to that of the inverted hydrodynamic fish trail. Herein the flow around a 3D printed sea lion head, with integrated whiskers of comparable geometry and material properties to a real seal lion, is investigated when exposed to vortex streets generated by cylindrical bluff bodies. The whiskers respond to the vortices with a jerky motion, analogous to the stick-slip response of rat whiskers; this motion is found to be the time derivative of the Gaussian function. Compared to the displacement response, the time-derivative of the whisker response decodes the Strouhal frequency of the Von-Karman wake, which improves the sensing efficiency in noisy environments. The study hypothesizes that the time derivative of the whisker bending moment is the best physical variable that can be used as the input to the pinnipeds neural system.