Bio-inspired all-optical artificial neuromast for 2D flow sensing

A biomimetic orthogonal flow sensor based on an asymmetric optical fiber sensory structure for marine sensing

With increasing attention on the world's oceans, a significant amount of research has been focused on the sensing of marine-related parameters in recent years. In this paper, a bioinspired flow sensor with corrosion resistance, anti-interference capability, a portable design structure, easy integration, and directional sensing ability is presented to realize flow speed sensing in open water. The sensor is realized by a flexible artificial cupula that seals one side of an optical fiber acting as an artificial kinocilium. Below the artificial kinocilium, an encapsulated s-tapered optical fiber mimics the fish neuromast sensory mechanism and is supported by a 3D-printed structure that acts as the artificial supporting cell. To characterize the sensor, the optical transmission spectra of the sensory fiber under a set of water flow velocities and four orthogonal directions were monitored. The sensor's peak intensity responses were found to demonstrate flow sensing ability for velocity and direction, proving that this biomimetic portable sensing structure is a promising candidate for flow sensing in marine environments.

A study on flow field characteristics of a self-propelled robot fish approaching static obstacles based on artificial lateral line

To perceive the static obstacles in still water, the flow field characteristics of a self-propelled robot fish approaching static obstacles were studied based on artificial lateral line (ALL). The pressure distribution on the fish body surface was calculated with different separation between the robot fish and the obstacle boundary, obstacle size and undulating frequency. Subsequently, an ALL system was established and five obstacle perception models were studied to analyze the perceptual characteristics of the ALL. Finally, the experiments were conducted to further reveal the effects of obstacles and motion parameters on the body surface pressure of robot fish. The results indicate that the obstacles have a significant effect on the pressure distribution of the surface of the fish body. Namely the parameters of separation, obstacle size and undulating frequency will affect the peak value of the amplitude envelope of the pressure signals. The obstacle size and distance between the obstacles can be predicted using the time parameters of the amplitude envelope of the pressure signals. Moreover, the self-propelled robot fish with a medium undulating frequency approach to the large obstacles with small separation has better perceptual performance. The findings could offer some insight into understanding the perception of complex underwater environment based on ALL.

Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method

The lateral line system is an essential mechanosensory organ for organisms such as fish; it perceives the fluid environment in the near-field through the neuromasts on the lateral line system, supporting behaviors (e.g., obstacle avoidance and predation in fish). Inspired by the near-field perception ability of fish, we propose an artificial lateral line system composed of pressure sensors that respond to a target’s relative position by measuring the pressure change of the target vibration near the lateral line. Based on the shortcomings of the idealized constrained modeling approach, a multilayer perceptron network was built in this paper to process the pressure signal and predict the coordinates on a two-dimensional plane. Previous studies primarily focused on the localization of a single dipole source and rarely considered the localization of multiple vibration sources. In this paper, we explore the localization of numerous dipole sources of the same and different frequency vibrations based on the prediction of the two-dimensional coordinates of double dipoles. The experimental results show that the mutual interference of two vibration sources causes an increase in the localization error. Compared with multiple sources of vibration at the same frequency, the positioning accuracies of various vibration sources at different frequencies are higher. In addition, we explored the effects of the number of sensors on the localization results.

Advances in Multicore Fiber Grating Sensors

In recent years, multicore fiber (MCF) has attracted increasing interest for sensing applications, due to its unique fiber structure of multiple parallel cores in a single fiber cladding, which offers a flexible configurable platform to establish diverse functional fiber devices for sensing applications. So far, a variety of discrete fiber sensors using MCF have been developed, among which one of the major categories is the MCF grating sensors. The most distinct characteristic of MCF that differs from the normal single mode fibers is that the off-center cores of a MCF are sensitive to bending, which is caused by the bending induced tangential strain in off-center waveguides through either compression or stretching. The bending sensitivity has been widely developed for bending/curvature sensing or measuring physical parameters that are associated with bending. In this paper, we review the research progress on MCF-based fiber grating sensors. MCF-based diverse fiber grating sensors will be introduced, whose working principles will be discussed, and various types of applications of the MCF grating sensors will be summarized. Finally, the challenges and prospects of MCF grating for sensing applications will be presented.

Flow separation sensing on airfoil using a 3D printed biomimetic artificial hair sensor

Small-scale unmanned air vehicles require lightweight, compact, and low-power sensors that encompass a variety of sensing modalities to enable flight control and navigation in challenging environments. Flow sensing is one such modality that has attracted much interest in recent years. In this paper, a micro-scale artificial hair sensor is developed to resolve both the direction and magnitude of airflow. The sensor structure employs a high-aspect ratio hair structure and a thin flexible membrane to facilitate the transduction of directional airflow to membrane deflection. The sensor readout is based on capacitive sensing and two pairs of electrodes orthogonal to each other are used to obtain airflow directional information. The sensor structure was fabricated using two-photon polymerization and integration onto a miniature printed circuit board to enable simple measurement. The sensor's responses to static displacement loading from different directions were characterized. The experimental results are in good agreement with the simulation results. Furthermore, the sensor's capability to measure the direction and magnitude of flow was demonstrated. Finally, the sensor was mounted on an airfoil and its ability to detect flow separation was verified.

A biomimetic fish finlet with a liquid metal soft sensor for proprioception and underwater sensing

Finlets have a unique overhanging structure at the back, similar to a flag. They are located between the dorsal/anal fin and the caudal fin on the sides of the body. Until now, the sensing ability of finlets has not been well understood. In this paper, we design and manufacture a biomimetic soft robotic finlet (48.5 mm long, 30 mm high) with mechanosensation based on printed stretchable liquid metal sensors. The robotic finlet's posterior fin ray can achieve side-to-side movement orthogonal to the anterior fin ray. A flow sensor encapsulating a liquid metal sensor network enables the biomimetic finlets to sense the direction and flow intensity. The stretchable liquid metal sensors mounted on micro-actuators are utilized to perceive the swing motion of the fin ray. We found that the finlet prototype can sense the flapping amplitudes and frequency of the fin ray. The membrane between the two orthogonal fin rays can amplify the sensor output. Our results indicate that the overhanging structure endows the biomimetic finlet with the ability to sense external stimuli from stream-wise, lateral and vertical directions. We further demonstrate, through digital particle image velocimetry experiments, that the finlet can detect a Kármán vortex street. This study lays the foundations for exploring the environmental perception of biological fish fins and provides a new approach for the perception of complex flow environments by future underwater robots.

Design of Flow Velocity and Direction Monitoring Sensor Based on Fiber Bragg Grating

The real-time monitoring of the flow environment parameters, such as flow velocity and direction, helps to accurately analyze the effect of water scour and provide technical support for the maintenance of pier and abutment foundations in water. Based on the principle of the Fiber Brag Grating sensor, a sensor for monitoring the flow velocity and direction in real-time is designed in this paper. Meanwhile, the theoretical calculation formulas of flow velocity and direction are derived. The structural performance of the sensor is simulated and analyzed by finite element analysis. The performance requirements of different parts of the sensor are clarified. After a sample of the sensor is manufactured, calibration experiments are conducted to verify the function and test the accuracy of the sensor, and the experimental error is analyzed. The experimental results indicate that the sensor designed in this paper achieves a high accuracy for the flow with a flow velocity of 0.05–5 m/s and the flow velocity monitoring error is kept within 7%, while the flow direction monitoring error is kept within 2°. The sensor can meet the actual monitoring requirements of the structures in water and provide reliable data sources for water scour analysis.

The Quadrature Method: A Novel Dipole Localisation Algorithm for Artificial Lateral Lines Compared to State of the Art

The lateral line organ of fish has inspired engineers to develop flow sensor arrays—dubbed artificial lateral lines (ALLs)—capable of detecting near-field hydrodynamic events for obstacle avoidance and object detection. In this paper, we present a comprehensive review and comparison of ten localisation algorithms for ALLs. Differences in the studied domain, sensor sensitivity axes, and available data prevent a fair comparison between these algorithms from their original works. We compare them with our novel quadrature method (QM), which is based on a geometric property specific to 2D-sensitive ALLs. We show how the area in which each algorithm can accurately determine the position and orientation of a simulated dipole source is affected by (1) the amount of training and optimisation data, and (2) the sensitivity axes of the sensors. Overall, we find that each algorithm benefits from 2D-sensitive sensors, with alternating sensitivity axes as the second-best configuration. From the machine learning approaches, an MLP required an impractically large training set to approach the optimisation-based algorithms’ performance. Regardless of the data set size, QM performs best with both a large area for accurate predictions and a small tail of large errors.

Materials, Actuators, and Sensors for Soft Bioinspired Robots

Biological systems can perform complex tasks with high compliance levels. This makes them a great source of inspiration for soft robotics. Indeed, the union of these fields has brought about bioinspired soft robotics, with hundreds of publications on novel research each year. This review aims to survey fundamental advances in bioinspired soft actuators and sensors with a focus on the progress between 2017 and 2020, providing a primer for the materials used in their design.

Artificial lateral line based relative state estimation between an upstream oscillating fin and a downstream robotic fish

The lateral line enables fish to efficiently sense the surrounding environment, thus assisting flow-related fish behaviors. Inspired by this phenomenon, varieties of artificial lateral line systems (ALLSs) have been developed and applied to underwater robots. This article focuses on using the pressure sensor arrays based ALLS-measured hydrodynamic pressure variations (HPVs) for estimating the relative states between an upstream oscillating fin and a downstream robotic fish. The HPVs and relative states are measured in flume experiments in which the oscillating fin and the robotic fish have been locate with upstream-downstream formation in a flume. The relative states include the relative oscillating frequency, amplitude, and offset of the upstream oscillating fin to the downstream robotic fish, the relative vertical distance, the relative yaw angle, the relative pitch angle, and the relative roll angle between the upstream oscillating fin and the downstream robotic fish. Regression models between the ALLS-measured and the mentioned relative states are investigated, and regression models-based relative state estimations are conducted. Specifically, two criteria are proposed firstly to investigate not only the sensitivity of each pressure sensor to the variations of relative state but also the insufficiency and redundancy of the pressure sensors. And thus the pressure sensors used for regression analysis are determined. Then four typical regression methods, including random forest (RF) algorithm, support vector regression, back propagation neural network, and multiple linear regression method are used for establishing regression models between the ALLS-measured HPVs and the relative states. Then regression effects of the four methods are compared and discussed. Finally, the RF-based method, which has the best regression effect, is used to estimate the relative yaw angle and oscillating amplitude using the ALLS-measured HPVs and exhibits excellent estimation performance.

Optimal Flow Sensing for Schooling Swimmers

Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other fish. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with numerical simulations of the two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and the number of the leading swimmers using surface only information.

Flow Field Perception of a Moving Carrier Based on an Artificial Lateral Line System

At present, autonomous underwater vehicles (AUVs) cannot perceive local environments in complex marine environments, where fish can obtain hydrodynamic information about the surrounding environment through a lateral line. Inspired by this biological function, an artificial lateral line system (ALLS) was built on a moving bionic carrier using the pressure sensor in this paper. When the carrier operated with different speeds in the flow field, the pressure distribution characteristics surrounding the carrier were analyzed by numerical simulation, where the effect of the flow angle between the fluid velocity direction and the carrier navigation direction was considered. The flume experiment was carried out in accordance with the simulation conditions, and the analysis results of the experiment were consistent with those in the simulation. The relationship between pressure and fluid velocity was established by a fitting method. Subsequently, the pressure difference method was investigated to establish a relationship model between the pressure difference on both sides of the carrier and the flow angle. Finally, a back propagation neural network model was used to predict the fluid velocity, flow angle, and carrier speed successfully in the unknown fluid environment. The local fluid environment perception by moving carrier carrying ALLS was studied which may promote the engineering application of the artificial lateral line in the local perception, positioning, and navigation on AUVs.

Piezoresistive Carbon Nanofiber-Based Cilia-Inspired Flow Sensor

Evolving over millions of years, hair-like natural flow sensors called cilia, which are found in fish, crickets, spiders, and inner ear cochlea, have achieved high resolution and sensitivity in flow sensing. In the pursuit of achieving such exceptional flow sensing performance in artificial sensors, researchers in the past have attempted to mimic the material, morphological, and functional properties of biological cilia sensors, to develop MEMS-based artificial cilia flow sensors. However, the fabrication of bio-inspired artificial cilia sensors involves complex and cumbersome micromachining techniques that lay constraints on the choice of materials, and prolongs the time taken to research, design, and fabricate new and novel designs, subsequently increasing the time-to-market. In this work, we establish a novel process flow for fabricating inexpensive, yet highly sensitive, cilia-inspired flow sensors. The artificial cilia flow sensor presented here, features a cilia-inspired high-aspect-ratio titanium pillar on an electrospun carbon nanofiber (CNF) sensing membrane. Tip displacement response calibration experiments conducted on the artificial cilia flow sensor demonstrated a lower detection threshold of 50 µm. Furthermore, flow calibration experiments conducted on the sensor revealed a steady-state airflow sensitivity of 6.16 mV/(m s⁻¹) and an oscillatory flow sensitivity of 26 mV/(m s⁻¹), with a lower detection threshold limit of 12.1 mm/s in the case of oscillatory flows. The flow sensing calibration experiments establish the feasibility of the proposed method for developing inexpensive, yet sensitive, flow sensors; which will be useful for applications involving precise flow monitoring in microfluidic devices, precise air/oxygen intake monitoring for hypoxic patients, and other biomedical devices tailored for intravenous drip/urine flow monitoring. In addition, this work also establishes the applicability of CNFs as novel sensing elements in MEMS devices and flexible sensors.

Recurrent neural networks for hydrodynamic imaging using a 2D-sensitive artificial lateral line

The lateral line is a mechanosensory organ found in fish and amphibians that allows them to sense and act on their near-field hydrodynamic environment. We present a 2D-sensitive artificial lateral line (ALL) comprising eight all-optical flow sensors, which we use to measure hydrodynamic velocity profiles along the sensor array in response to a moving object in its vicinity. We then use the measured velocity profiles to reconstruct the object's location, via two types of neural networks: feed-forward and recurrent. Several implementations of feed-forward neural networks for ALL source localisation exist, while recurrent neural networks may be more appropriate for this task. The performance of a recurrent neural network (the long short-term memory, LSTM) is compared to that of a feed-forward neural network (the online-sequential extreme learning machine, OS-ELM) via localizing a 6 cm sphere moving at 13 cm s^-1. Results show that, in a 62 cm [Formula: see text] 9.5 cm area of interest, the LSTM outperforms the OS-ELM with an average localisation error of 0.72 cm compared to 4.27 cm, respectively. Furthermore, the recurrent network is relatively less affected by noise, indicating that recurrent connections can be beneficial for hydrodynamic object localisation.

Bioinspired Cilia Sensors with Graphene Sensing Elements Fabricated Using 3D Printing and Casting

Sensor designs found in nature are optimal due to their evolution over millions of years, making them well-suited for sensing applications. However, replicating these complex, three-dimensional (3D), biomimetic designs in artificial and flexible sensors using conventional techniques such as lithography is challenging. In this paper, we introduce a new processing paradigm for the simplified fabrication of flexible sensors featuring complex and bioinspired structures. The proposed fabrication workflow entailed 3D-printing a metallic mold with complex and intricate 3D features such as a micropillar and a microchannel, casting polydimethylsiloxane (PDMS) inside the mold to obtain the desired structure, and drop-casting piezoresistive graphene nanoplatelets into the predesigned microchannel to form a flexible strain gauge. The graphene-on-PDMS strain gauge showed a high gauge factor of 37 as measured via cyclical tension-compression tests. The processing workflow was used to fabricate a flow sensor inspired by hair-like ‘cilia’ sensors found in nature, which comprised a cilia-inspired pillar and a cantilever with a microchannel that housed the graphene strain gauge. The sensor showed good sensitivity against both tactile and water flow stimuli, with detection thresholds as low as 12 µm in the former and 58 mm/s in the latter, demonstrating the feasibility of our method in developing flexible flow sensors.

A miniaturized piezoresistive flow sensor for real-time monitoring of intravenous infusion

Drug overdose (DO) is considered one of the current issues of intravenous (IV) infusion particularly resulting in serious injuries and deaths. Malfunction of infusion pumps is reported as the main cause of the drug overdose. Live monitoring and flow rate calculation by health professionals have been practicing to avoid DO. However, human errors and miscalculations are inevitable. A secondary measurement tool is required to avoid the risk of OD when infusion pump malfunctions cannot be detected immediately. Here, inspired by nature, we developed a real-time monitoring device through which an administrator can review, evaluate, and modify the IV infusion process. Our flow sensor possesses an erected polymer hair cell on a multi-layered silicon base forming from a patterned gold strained gauge layer on a piezoresistive liquid crystal polymer (LCP) membrane. Gold strain gauges on an LCP membrane have been used instead of a piezoresistive silicon membrane as the sensing element. The combination of gold strain gauges and LCP membrane provides better sensitivity than a piezoresistive silicon membrane of the same dimensions and thickness. We also miniaturized our biocompatible sensor such that it can be possible to install it inside the IV tube in contact with the liquid providing an in-suite online flow monitoring. The proposed LCP membrane sensor is compared with two commercially available IV sensors to validate its flow sensing ability. The experimental results demonstrate that the proposed sensor provides a low threshold detection limit of 5 mL/hr, which betters the performance of other commercial sensors at low flow rates.

Bidirectional biomimetic flow sensing with antiparallel and curved artificial hair sensors

Background: Flow stimuli in the natural world are varied and contain a wide variety of directional information. Nature has developed morphological polarity and bidirectional arrangements for flow sensing to filter the incoming stimuli. Inspired by the neuromasts found in the lateral line of fish, we present a novel flow sensor design based on two curved cantilevers with bending orientation antiparallel to each other. Antiparallel cantilever pairs were designed, fabricated and compared to a single cantilever based hair sensor in terms of sensitivity to temperature changes and their response to changes in relative air flow direction. Results: In bidirectional air flow, antiparallel cantilever pairs exhibit an axially symmetrical sensitivity between 40 μV/(m s^-1) for the lower air flow velocity range (between ±10-20 m s^-1) and 80 μV/(m s^-1) for a higher air flow velocity range (between ±20-32 m s^-1). The antiparallel cantilever design improves directional sensitivity and provides a sinusoidal response to flow angle. In forward flow, the single sensor reaches its saturation limitation, flattening at 67% of the ideal sinusoidal curve which is earlier than the antiparallel cantilevers at 75%. The antiparallel artificial hair sensor better compensates for temperature changes than the single sensor. Conclusion: This work demonstrated the successive improvement of the bidirectional sensitivity, that is, improved temperature compensation, decreased noise generation and symmetrical response behaviour. In the antiparallel configuration, one of the two cantilevers always extends out into the free stream flow, remaining sensitive to directional flow and preserving a sensitivity to further flow stimuli.