A Review of Artificial Lateral Line in Sensor Fabrication and Bionic Applications for Robot Fish

A Highly Sensitive Deep-Sea Hydrodynamic Pressure Sensor Inspired by Fish Lateral Line

Hydrodynamic pressure sensors offer an auxiliary approach for ocean exploration by unmanned underwater vehicles (UUVs). However, existing hydrodynamic pressure sensors often lack the ability to monitor subtle hydrodynamic stimuli in deep-sea environments. In this study, we present the development of a deep-sea hydrodynamic pressure sensor (DSHPS) capable of operating over a wide range of water depths while maintaining exceptional hydrodynamic sensing performance. The DSHPS device was systematically optimized by considering factors such as piezoelectric polyvinylidene fluoride-trifluoroethylene/barium titanate [P(VDF-TrFE)/BTO] nanofibers, electrode configurations, sensing element dimensions, integrated circuits, and packaging strategies. The optimized DSHPS exhibited a remarkable pressure gradient response, achieving a minimum pressure difference detection capability of approximately 0.11 Pa. Additionally, the DSHPS demonstrated outstanding performance in the spatial positioning of dipole sources, which was elucidated through theoretical charge modeling and fluid-structure interaction (FSI) simulations. Furthermore, the integration of a high Young's modulus packaging strategy inspired by fish skull morphology ensured reliable sensing capabilities of the DSHPS even at depths of 1000 m in the deep sea. The DSHPS also exhibited consistent and reproducible positioning performance for subtle hydrodynamic stimulus sources across this wide range of water depths. We envision that the development of the DSHPS not only enhances our understanding of the evolutionary aspects of deep-sea canal lateral lines but also paves the way for the advancement of artificial hydrodynamic pressure sensors.

Touchless underwater wall-distance sensing via active proprioception of a robotic flapper

In this work, we explored a bioinspired method for underwater object sensing based on active proprioception. We investigated whether the fluid flows generated by a robotic flapper, while interacting with an underwater wall, can encode the distance information between the wall and the flapper, and how to decode this information using the proprioception within the flapper. Such touchless wall-distance sensing is enabled by the active motion of a flapping plate, which injects self-generated flow to the fluid environment, thus representing a form of active sensing. Specifically, we trained a long short-term memory (LSTM) neural network to predict the wall distance based on the force and torque measured at the base of the flapping plate. In addition, we varied the Rossby number (Ro, or the aspect ratio of the plate) and the dimensionless flapping amplitude (A∗) to investigate how the rotational effects and unsteadiness of self-generated flow respectively affect the accuracy of the wall-distance prediction. Our results show that the median prediction error is within 5% of the plate length for all the wall-distances investigated (up to 40 cm or approximately 2-3 plate lengths depending on theRo); therefore, confirming that the self-generated flow can enable underwater perception. In addition, we show that stronger rotational effects at lowerRolead to higher prediction accuracy, while flow unsteadiness (A∗) only has moderate effects. Lastly, analysis based on SHapley Additive exPlanations (SHAP) indicate that temporal features that are most prominent at stroke reversals likely promotes the wall-distance prediction.

The role of hydrodynamics in collective motions of fish schools and bioinspired underwater robots

Collective behaviour defines the lives of many animal species on the Earth. Underwater swarms span several orders of magnitude in size, from coral larvae and krill to tunas and dolphins. Agent-based algorithms have modelled collective movements of animal groups by use of social forces, which approximate the behaviour of individual animals. But details of how swarming individuals interact with the fluid environment are often under-examined. How do fluid forces shape aquatic swarms? How do fish use their flow-sensing capabilities to coordinate with their schooling mates? We propose viewing underwater collective behaviour from the framework of fluid stigmergy, which considers both physical interactions and information transfer in fluid environments. Understanding the role of hydrodynamics in aquatic collectives requires multi-disciplinary efforts across fluid mechanics, biology and biomimetic robotics. To facilitate future collaborations, we synthesize key studies in these fields.

Recent progress on underwater soft robots: adhesion, grabbing, actuating, and sensing

The research on biomimetic robots, especially soft robots with flexible materials as the main structure, is constantly being explored. It integrates multi-disciplinary content, such as bionics, material science, mechatronics engineering, and control theory, and belongs to the cross-disciplinary field related to mechanical bionics and biological manufacturing. With the continuous development of various related disciplines, this area has become a hot research field. Particularly with the development of practical technologies such as 3D printing technology, shape memory alloy, piezoelectric materials, and hydrogels at the present stage, the functions and forms of soft robots are constantly being further developed, and a variety of new soft robots keep emerging. Soft robots, combined with their own materials or structural characteristics of large deformation, have almost unlimited degrees of freedom (DoF) compared with rigid robots, which also provide a more reliable structural basis for soft robots to adapt to the natural environment. Therefore, soft robots will have extremely strong adaptability in some special conditions. As a type of robot made of flexible materials, the changeable pose structure of soft robots is especially suitable for the large application environment of the ocean. Soft robots working underwater can better mimic the movement characteristics of marine life in the hope of achieving more complex underwater tasks. The main focus of this paper is to classify different types of underwater organisms according to their common motion modes, focusing on the achievements of some bionic mechanisms in different functional fields that have imitated various motion modes underwater in recent years (e.g., the underwater sucking glove, the underwater Gripper, and the self-powered soft robot). The development of various task types (e.g., grasping, adhesive, driving or swimming, and sensing functions) and mechanism realization forms of the underwater soft robot are described based on this article.

Physiological responses of mechanosensory systems in the head of larval zebrafish (Danio rerio)

The lateral line system of zebrafish consists of the anterior lateral line, with neuromasts distributed on the head, and the posterior lateral line, with neuromasts distributed on the trunk. The sensory afferent neurons are contained in the anterior and posterior lateral line ganglia, respectively. So far, the vast majority of physiological and developmental studies have focused on the posterior lateral line. However, studies that focus on the anterior lateral line, especially on its physiology, are very rare. The anterior lateral line involves different neuromast patterning processes, specific distribution of synapses, and a unique role in behavior. Here, we report our observations regarding the development of the lateral line and analyze the physiological responses of the anterior lateral line to mechanical and water jet stimuli. Sensing in the fish head may be crucial to avoid obstacles, catch prey, and orient in water current, especially in the absence of visual cues. Alongside the lateral line, the trigeminal system, with its fine nerve endings innervating the skin, could contribute to perceiving mechanosensory stimulation. Therefore, we compare the physiological responses of the lateral line afferent neurons to responses of trigeminal neurons and responsiveness of auditory neurons. We show that anterior lateral line neurons are tuned to the velocity of mechanosensory ramp stimulation, while trigeminal neurons either only respond to mechanical step stimuli or fast ramp and step stimuli. Auditory neurons did not respond to mechanical or water jet stimuli. These results may prove to be essential in designing underwater robots and artificial lateral lines, with respect to the spectra of stimuli that the different mechanosensory systems in the larval head are tuned to, and underline the importance and functionality of the anterior lateral line system in the larval fish head.

A comprehensive review on fish-inspired robots

Recently, the increasing interest in underwater exploration motivates the development of aquatic unmanned vehicles. To execute hazardous tasks in an unknown or even hostile environment, researchers have directed on developing biomimetic robots inspired by the extraordinary maneuverability, cruising speed, and propulsion efficiency of fish. Nevertheless, the performance of current prototypes still has gaps compared with that of real fishes. In this review, recent approaches in structure designs, actuators, and sensors are presented. In addition, the theoretical methods for modeling the robotic fishes are consolidated, and the control strategies are offered. Finally, the current challenges are summarized, and possible future directions are deeply discussed. It is expected that the emergence of new engineering and biological technologies will enhance the field of robotic fish for further advancement.

Estimation System of Disturbance Force and Torque for Underwater Robot Based on Artificial Lateral Line

The motion-control precision of a shallow-sea underwater robot is seriously affected by external disturbances such as wind, waves and ocean currents. Due to the lack of a specialized disturbance-sensor system, the disturbance force and torque cannot be sensed effectively. Inspired by bionics, an artificial lateral-line system for estimating external disturbances of an underwater robot is presented in this paper. In the system, the pressure of water is first collected through the pressure-sensor array. Then, the pressure data is processed by a series of algorithms, and the disturbance force and torque are observed from the data. Both multiple linear regression and the artificial neural network method are used to fit the mathematical models of the disturbances. Finally, the system is validated experimentally to be effective and practical. The underwater robot senses the disturbance force and torque from the water indirectly through the artificial lateral-line system, which will improve the accuracy of motion control.

Optimal Sensor Placement of the Artificial Lateral Line for Flow Parametric Identification

The multi-sensor artificial lateral line system (ALLS) can identify the flow-field’s parameters to realize the closed-loop control of the underwater robotic fish. An inappropriate sensor placement of ALLS may result in inaccurate flow-field parametric identification. Therefore, this paper proposes a method to optimize the sensor placement configuration of the ALLS, which mainly included three algorithms, the feature importance algorithm based on mean and variance (MVF), the feature importance algorithm based on distance evaluation (DF), and the information redundancy (IR) algorithm. The optimal sensor placement performance selected by this method is verified by simulation. In addition, further experimental verification was conducted using the ALLS. Compared with the uniform sensor placement configuration mentioned in recent studies, the experimental results suggest that the optimal sensor placement method can achieve a more effective prediction of the flow-field parameters, therefore strengthening the underwater robotic fish’s perception and control function.

Soft Robots for Ocean Exploration and Offshore Operations: A Perspective

The ocean and human activities related to the sea are under increasing pressure due to climate change, widespread pollution, and growth of the offshore energy sector. Data, in under-sampled regions of the ocean and in the offshore patches where the industrial expansion is taking place, are fundamental to manage successfully a sustainable development and to mitigate climate change. Existing technology cannot cope with the vast and harsh environments that need monitoring and sampling the most. The limiting factors are, among others, the spatial scales of the physical domain, the high pressure, and the strong hydrodynamic perturbations, which require vehicles with a combination of persistent autonomy, augmented efficiency, extreme robustness, and advanced control. In light of the most recent developments in soft robotics technologies, we propose that the use of soft robots may aid in addressing the challenges posed by abyssal and wave-dominated environments. Nevertheless, soft robots also allow for fast and low-cost manufacturing, presenting a new potential problem: marine pollution from ubiquitous soft sampling devices. In this study, the technological and scientific gaps are widely discussed, as they represent the driving factors for the development of soft robotics. Offshore industry supports increasing energy demand and the employment of robots on marine assets is growing. Such expansion needs to be sustained by the knowledge of the oceanic environment, where large remote areas are yet to be explored and adequately sampled. We offer our perspective on the development of sustainable soft systems, indicating the characteristics of the existing soft robots that promote underwater maneuverability, locomotion, and sampling. This perspective encourages an interdisciplinary approach to the design of aquatic soft robots and invites a discussion about the industrial and oceanographic needs that call for their application.

A Soft Material Flow Sensor for Micro Air Vehicles

To control and navigate micro air vehicles (MAVs) efficiently, there is a need for small, lightweight, durable, sensitive, fast, and low-power airspeed sensors. When designing sensors to meet these requirements, soft materials are promising alternatives to more traditional materials due to the large deformations they can withstand. In this article, a new concept of a soft material flow sensor is presented based on elastic filament velocimetry, which fulfills all necessary criteria. This technique measures flow velocity by relating it to the strain of a soft ribbon suspended between two static supports and subjected to a flow of interest. The ribbon is manufactured from polydimethylsiloxane and can be made piezoresistive by the addition of silver nanowires. With the described manufacturing method, the sensor can be made using common laboratory tools, outside of a clean room, significantly reducing its complexity. Furthermore, it can be operated using a simple and lightweight circuit, making it a convenient alternative for MAVs. Using a piezoresistive material allows for the flow velocity to be calibrated to the resistance change of the strained ribbon. Although certain challenges remain unsolved, such as polymer creep, the sensor has demonstrated its ability to measure flow velocities down to 4 m/s in air through experiments. A time-dependent analytical model is also provided. The model shows that the current sensor has a bandwidth of 480 Hz. Most importantly, the sensitivity and the bandwidth of the sensor can be varied strictly by modifying the geometry and the material properties of the ribbon.

Bio-inspired Flexible Lateral Line Sensor Based on P(VDF-TrFE)/BTO Nanofiber Mat for Hydrodynamic Perception

Fish and some amphibians can perform a variety of behaviors in confined and harsh environments by employing an extraordinary mechanosensory organ, the lateral line system (LLS). Inspired by the form-function of the LLS, a hydrodynamic artificial velocity sensor (HAVS) was presented in this paper. The sensors featured a polarized poly (vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)]/barium titanate (BTO) electrospinning nanofiber mat as the sensing layer, a polyimide (PI) film with arrays of circular cavities as the substrate, and a poly(methyl methacrylate) (PMMA) pillar as the cilium. The P(VDF-TrFE)/BTO electrospinning nanofiber mat demonstrated enhanced crystallinity and piezoelectricity compared with the pure P(VDF-TrFE) nanofiber mat. A dipole source was employed to characterize the sensing performance of the fabricated HAVS. The HAVS achieved a velocity detection limit of 0.23 mm/s, superior to the conventional nanofiber mat-based flow sensor. In addition, directivity was feasible for the HAVS, which was in accordance with the simulation results. The proposed bio-inspired flexible lateral line sensor with hydrodynamic perception ability shows promising applications in underwater robotics for real-time flow analysis.

Constriction canal assisted artificial lateral line system for enhanced hydrodynamic pressure sensing

With the assistance of mechanosensory lateral line system, fish can perceive minute water motions in complex underwater environments. Inspired by the constriction within canal nearby canal neuromast in fish lateral line system, we proposed a novel canal artificial lateral line (CALL) device with constriction in canal nearby the sensing element. The designed CALL device consisted of a poly(vinylidene fluoride-trifluoroethylene)/polyimide cantilever as the sensing element and a polydimethylsiloxane (PDMS) microfluid canal. Two types of CALL devices, i.e., CALL with straight canal (S-CALL) and CALL with constriction canal (C-CALL), were developed and characterized employing a dipole source. Experimental results showed that the proposed C-CALL device achieved a pressure gradient detection limit of 0.64 Pa m^-1, which was much lower than the S-CALL device. It indicates that the constriction in the canal nearby the sensing element could enhance the hydrodynamic pressure sensing performance of the CALL device. In addition, the constriction could modify the frequency response of the CALL device, and the C-CALL device achieved higher voltage output than S-CALL in high-frequency domain.

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.

Sensory ecology of the fish lateral-line system: Morphological and physiological adaptations for the perception of hydrodynamic stimuli

Fishes are able to detect and perceive the hydrodynamic and physical environment they inhabit and process this sensory information to guide the resultant behaviour through their mechanosensory lateral-line system. This sensory system consists of up to several thousand neuromasts distributed across the entire body of the animal. Using the lateral-line system, fishes perceive water movements of both biotic and abiotic origin. The anatomy of the lateral-line system varies greatly between and within species. It is still a matter of debate as to how different lateral-line anatomies reflect adaptations to the hydrodynamic conditions to which fishes are exposed. While there are many accounts of lateral-line system adaptations for the detection of hydrodynamic signals in distinct behavioural contexts and environments for specific fish species, there is only limited knowledge on how the environment influences intra and interspecific variations in lateral-line morphology. Fishes live in a wide range of habitats with highly diverse hydrodynamic conditions, from pools and lakes and slowly moving deep-sea currents to turbulent and fast running rivers and rough coastal surf regions. Perhaps surprisingly, detailed characterisations of the hydrodynamic properties of natural water bodies are rare. In particular, little is known about the spatio-temporal patterns of the small-scale water motions that are most relevant for many fish behaviours, making it difficult to relate environmental stimuli to sensory system morphology and function. Humans use bodies of water extensively for recreational, industrial and domestic purposes and in doing so often alter the aquatic environment, such as through the release of toxicants, the blocking of rivers by dams and acoustic noise emerging from boats and construction sites. Although the effects of anthropogenic interferences are often not well understood or quantified, it seems obvious that they change not only water quality and appearance but also, they alter hydrodynamic conditions and thus the types of hydrodynamic stimuli acting on fishes. To date, little is known about how anthropogenic influences on the aquatic environment affect the morphology and function of sensory systems in general and the lateral-line system in particular. This review starts out by briefly describing naturally occurring hydrodynamic stimuli and the morphology and neurobiology of the fish lateral-line system. In the main part, adaptations of the fish lateral-line system for the detection and analysis of water movements during various behaviours are presented. Finally, anthropogenic influences on the aquatic environment and potential effects on the fish lateral-line system are discussed.

Capacitive Bio-Inspired Flow Sensing Cupula

Submersible robotics have improved in efficiency and versatility by incorporating features found in aquatic life, ranging from thunniform kinematics to shark skin textures. To fully realize these benefits, sensor systems must be incorporated to aid in object detection and navigation through complex flows. Again, inspiration can be taken from biology, drawing on the lateral line sensor systems and neuromast structures found on fish. To maintain a truly soft-bodied robot, a man-made flow sensor must be developed that is entirely complaint, introducing no rigidity to the artificial “skin.” We present a capacitive cupula inspired by superficial neuromasts. Fabricated via lost wax methods and vacuum injection, our 5 mm tall device exhibits a sensitivity of 0.5 pF/mm (capacitance versus tip deflection) and consists of room temperature liquid metal plates embedded in a soft silicone body. In contrast to existing capacitive examples, our sensor incorporates the transducers into the cupula itself rather than at its base. We present a kinematic theory and energy-based approach to approximate capacitance versus flow, resulting in equations that are verified with a combination of experiments and COMSOL simulations.

Underwater Robot Detection System Based on Fish’s Lateral Line

This paper introduces the near-field detection system of an underwater robot based on the fish lateral line. Inspired by the perception mechanism of fish’s lateral line, the aim is to add near-field detection functionality to an underwater vehicle. To mimic the fish’s lateral line, an array of pressure sensors is developed and installed on the surface of the underwater vehicle. A vibrating sphere is simulated as an underwater pressure source, and the moving mechanism is built to drive the sphere to vibrate at a certain frequency near the lateral line. The calculation of the near-field pressure generated by the vibrating sphere is derived by linearizing the kinematics and dynamics conditions of the free surface wave equation. Structurally, the geometry shape of the detection system is printed by a 3D printer. The pressure data are sent to the computer and analyzed immediately to obtain information of the pressure source. Through the experiment, the variation law of the pressure is generated when the source vibrates near the body, and is consistent with the simulation results of the derived pressure calculation formula. It is found that the direction of the near-field pressure source can distinguished. The pressure amplitude of the sampled signals are extracted to be prepared for the next step to estimate the vertical distance between the center of the pressure source and the lateral line.

Form-function relationship in artificial lateral lines

We examined the form-function relationship of laboratory-constructed artificial lateral line canals. These biomimetic flow sensors consisted of a transparent silicone bar located inside a fluid filled canal equipped with canal pores. The silicone bar guided the light from a LED towards a position- sensitive photodiode. Fluid motion inside the canal deflected the silicone bar which was detected by the photodiode. We found that the resonance frequency of the silicone bar determined the resonance frequency of the artificial lateral line (frequency at which the sensor was most sensitive). The thickness and length of the silicone bar influenced both, the resonance frequency and the sensitivity (across all tested frequencies) of the artificial lateral line sensor. Sensitivity was also influenced by the length and diameter of the artificial lateral line canals. The distance between canal pores determined the spatial resolution of the sensor. The functionality of the sensor in detecting oscillatory fluid motions remained when the canal pores were covered with flexible membranes. Tension, diameter and thickness of the membranes altered the temporal filter properties of the artificial lateral line neuromast. The density and viscosity of the fluid inside the artificial lateral line canals also influenced the sensitivity and temporal filter properties of the artificial lateral line. The acquired knowledge will allow us to optimize artificial lateral line systems for specific technical applications.

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.

Behavior, Electrophysiology, and Robotics Experiments to Study Lateral Line Sensing in Fishes

The lateral line system is a sensory system unique to fishes and amphibians. It is composed of distributed mechanosensory hair cell organs on the head and body (neuromasts), which are sensitive to pressure gradients and water movements. Over the last decade, we have pursued an interdisciplinary approach by combining behavioral, electrophysiology, and robotics experiments to study this fascinating sensory system. In behavioral and electrophysiology experiments, we have studied the larval lateral line system in the model genetic organism, zebrafish (Danio rerio). We found that the lateral line system, even in 5-day-old larvae, is involved in an array of behaviors that are critical to survival, and the deflection of a single neuromast can elicit a swimming response. In robotics experiments, we used a range of physical models with distributed pressure sensors to better understand the hydrodynamic environments from the local perspective of a fish or robot. So far, our efforts have focused on extracting control-related information for a range of application scenarios including characterizing unsteady flows such as Kármán vortex streets for station holding. We also used robot models to test biological hypotheses on how morphology and movement of fishes affect lateral line sensing. Overall, with this review we aim to increase the visibility and accessibility of this multi-disciplinary research approach.

A Computing Method to Determine the Performance of an Ionic Liquid Gel Soft Actuator

A new type of soft actuator material—an ionic liquid gel (ILG) that consists of BMIMBF₄, HEMA, DEAP, and ZrO₂—is polymerized into a gel state under ultraviolet (UV) light irradiation. In this paper, we first propose that the ILG conforms to the assumptions of hyperelastic theory and that the Mooney-Rivlin model can be used to study the properties of the ILG. Under the five-parameter and nine-parameter Mooney-Rivlin models, the formulas for the calculation of the uniaxial tensile stress, plane uniform tensile stress, and 3D directional stress are deduced. The five-parameter and nine-parameter Mooney-Rivlin models of the ILG with a ZrO₂ content of 3 wt% were obtained by uniaxial tensile testing, and the parameters are denoted as c₁₀, c₀₁, c₂₀, c₁₁, and c₀₂ and c₁₀, c₀₁, c₂₀, c₁₁, c₀₂, c₃₀, c₂₁, c₁₂, and c₀₃, respectively. Through the analysis and comparison of the uniaxial tensile stress between the calculated and experimental data, the error between the stress data calculated from the five-parameter Mooney-Rivlin model and the experimental data is less than 0.51%, and the error between the stress data calculated from the nine-parameter Mooney-Rivlin model and the experimental data is no more than 8.87%. Hence, our work presents a feasible and credible formula for the calculation of the stress of the ILG. This work opens a new path to assess the performance of a soft actuator composed of an ILG and will contribute to the optimized design of soft robots.

Man-made flows from a fish's perspective: autonomous classification of turbulent fishway flows with field data collected using an artificial lateral line

The lateral line system provides fish with advanced mechanoreception over a wide range of flow conditions. Inspired by the abilities of their biological counterparts, artificial lateral lines have been developed and tested exclusively under laboratory settings. Motivated by the lack of flow measurements taken in the field which consider fluid-body interactions, we built a fish-shaped lateral line probe. The device is outfitted with 11 high-speed (2.5 kHz) time-synchronized pressure transducers, and designed to capture and classify flows in fish passage structures. A total of 252 field measurements, each with a sample size of 132 000 discrete sensor readings were recorded in the slots and across the pools of vertical slot fishways. These data were used to estimate the time-averaged flow velocity (R²  =  0.952), which represents the most common metric to assess fishway flows. The significant contribution of this work is the creation and application of hydrodynamic signatures generated by the spatial distribution of pressure fluctuations on the fish-shaped body. The signatures are based on the collection of the pressure fluctuations' probability distributions, and it is shown that they can be used to automatically classify distinct flow regions within the pools of three different vertical slot fishways. For the first time, field data from operational fishway measurements are sampled and classified using an artificial lateral line, providing a completely new source of bioinspired flow information.

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

We present the design, fabrication and testing of a novel all-optical 2D flow velocity sensor, inspired by a fish lateral line neuromast. This artificial neuromast consists of optical fibres inscribed with Bragg gratings supporting a fluid force recipient sphere. Its dynamic response is modelled based on the Stokes solution for unsteady flow around a sphere and found to agree with experimental results. Tuneable mechanical resonance is predicted, allowing a deconvolution scheme to accurately retrieve fluid flow speed and direction from sensor readings. The optical artificial neuromast achieves a low frequency threshold flow sensing of 5 mm s^-1 and 5 μm s^-1 at resonance, with a typical linear dynamic range of 38 dB at 100 Hz sampling. Furthermore, the optical artificial neuromast is shown to determine flow direction within a few degrees.

Sensing the flow beneath the fins

Flow sensing, maneuverability, energy efficiency and vigilance of surroundings are the key factors that dictate the performance of marine animals. Be it swimming at high speeds, attack or escape maneuvers, sensing and survival hydrodynamics are a constant feature of life in the ocean. Fishes are capable of performing energy efficient maneuvers, including capturing energy from vortical structures in water. These impressive capabilities are made possible by the uncanny ability of fish to sense minute pressure and flow variations on their body. This is achieved by arrays of biological neuromast sensors on their bodies that 'feel' the surroundings through 'touch at a distance' sensing. The main focus of this paper is to review the various biomimetic material approaches in developing superficial neuromast inspired ultrasensitive MEMS sensors. Principals and methods that translate biomechanical filtering properties of canal neuromasts to benefit artificial MEMS sensors have also been discussed. MEMS sensors with ultrahigh flow sensitivity and accuracy have been developed mainly through inspiration from the hair cell and cupula structures in the neuromast. Canal-inspired packages have proven beneficial in hydrodynamic flow filtering in artificial sensors enabling signal amplification and noise attenuation. A special emphasis has been placed on the recent innovations that closely mimic the structural and material designs of stereocilia of neuromasts by exploring soft polymers.

Cupula-Inspired Hyaluronic Acid-Based Hydrogel Encapsulation to Form Biomimetic MEMS Flow Sensors

Blind cavefishes are known to detect objects through hydrodynamic vision enabled by arrays of biological flow sensors called neuromasts. This work demonstrates the development of a MEMS artificial neuromast sensor that features a 3D polymer hair cell that extends into the ambient flow. The hair cell is monolithically fabricated at the center of a 2 μm thick silicon membrane that is photo-patterned with a full-bridge bias circuit. Ambient flow variations exert a drag force on the hair cell, which causes a displacement of the sensing membrane. This in turn leads to the resistance imbalance in the bridge circuit generating a voltage output. Inspired by the biological neuromast, a biomimetic synthetic hydrogel cupula is incorporated on the hair cell. The morphology, swelling behavior, porosity and mechanical properties of the hyaluronic acid hydrogel are characterized through rheology and nanoindentation techniques. The sensitivity enhancement in the sensor output due to the material and mechanical contributions of the micro-porous hydrogel cupula is investigated through experiments.

Nitride-Based Materials for Flexible MEMS Tactile and Flow Sensors in Robotics

The response to different force load ranges and actuation at low energies is of considerable interest for applications of compliant and flexible devices undergoing large deformations. We present a review of technological platforms based on nitride materials (aluminum nitride and silicon nitride) for the microfabrication of a class of flexible micro-electro-mechanical systems. The approach exploits the material stress differences among the constituent layers of nitride-based (AlN/Mo, Si x N y /Si and AlN/polyimide) mechanical elements in order to create microstructures, such as upwardly-bent cantilever beams and bowed circular membranes. Piezoresistive properties of nichrome strain gauges and direct piezoelectric properties of aluminum nitride can be exploited for mechanical strain/stress detection. Applications in flow and tactile sensing for robotics are described.