Distributed flow estimation and closed-loop control of an underwater vehicle with a multi-modal artificial lateral line

Physical reservoir computing on a soft bio-inspired swimmer

Bio-inspired Autonomous Underwater Vehicles with soft bodies provide significant performance benefits over conventional propeller-driven vehicles; however, it is difficult to control these vehicles due to their soft underactuated bodies. This study investigates the application of Physical Reservoir Computing (PRC) in the swimmer's flexible body to perform state estimation. This PRC informed state estimation has potential to be used in vehicle control. PRC is a type of recurrent neural network that leverages the nonlinear dynamics of a physical system to predict a nonlinear spatiotemporal input-output relationship. By embodying the neural network into the physical structure, PRC can process the response to an environment input with high computational efficiency. This study uses a soft bio-inspired propulsor embodied as a physical reservoir. We evaluate its ability to predict different state estimation tasks including hydrodynamic forces and benchmark computational tasks in response to the forcing applied to the artificial muscles during actuation. The propulsor's nonlinear fluid-structural dynamics act as the physical reservoir and the kinematic feedback serves as the reservoir readouts. We show that the bio-inspired underwater propulsor can predict the hydrodynamic thrust and benchmark tasks with high accuracy under specific input frequencies. By analyzing the frequency spectrum of the input, readouts, and target signals, we demonstrate that the system's dynamic response determines the frequency contents relevant to the task being predicted. The propulsor's ability to process information stems from its nonlinearity, as it is responsible to transform the input signal into a broader spectrum of frequency content at the readouts. This broad band of frequency content is necessary to recreate the target signal within the PRC algorithm, thereby improving the prediction performance. The spectral analysis provides a unique perspective to analyze the nonlinear dynamics of a physical reservoir and serves as a valuable tool for examining other types of vibratory systems for PRC. This work serves as a first step towards embodying computation into soft bio-inspired swimmers.

Fish-inspired tracking of underwater turbulent plumes

Autonomous ocean-exploring vehicles have begun to take advantage of onboard sensor measurements of water properties such as salinity and temperature to locate oceanic features in real time. Such targeted sampling strategies enable more rapid study of ocean environments by actively steering towards areas of high scientific value. Inspired by the ability of aquatic animals to navigate via flow sensing, this work investigates hydrodynamic cues for accomplishing targeted sampling using a palm-sized robotic swimmer. As proof-of-concept analogy for tracking hydrothermal vent plumes in the ocean, the robot is tasked with locating the center of turbulent jet flows in a 13,000-liter water tank using data from onboard pressure sensors. To learn a navigation strategy, we first implemented RL on a simulated version of the robot navigating in proximity to turbulent jets. After training, the RL algorithm discovered an effective strategy for locating the jets by following transverse velocity gradients sensed by pressure sensors located on opposite sides of the robot. When implemented on the physical robot, this gradient following strategy enabled the robot to successfully locate the turbulent plumes at more than twice the rate of random searching. Additionally, we found that navigation performance improved as the distance between the pressure sensors increased, which can inform the design of distributed flow sensors in ocean robots. Our results demonstrate the effectiveness and limits of flow-based navigation for autonomously locating hydrodynamic features of interest.

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.

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.

A simple macro-scale artificial lateral line sensor for the detection of shed vortices

Underwater robot sensing is challenging due to the complex and noisy nature of the environment. The lateral line system in fish allows them to robustly sense their surroundings, even in turbid and turbulent environments, allowing them to perform tasks such as shoaling or foraging. Taking inspiration from the lateral line system in fish to design robot sensors could help to power underwater robots in inspection, exploration, or environmental monitoring tasks. Previous studies have designed systems that mimic both the design and the configuration of the lateral line and neuromasts, but at high cost or using complex procedures. Here, we present a simple, low cost, bio-inspired sensor, that can detect passing vortices shed from surrounding obstacles or upstream fish or robots. We demonstrate the importance of the design elements used, and show a minimum 20% reduction in residual error over sensors lacking these elements. Results were validated in reality using a prototype of the artificial lateral line sensor. These results mark an important step in providing alternate methods of control in underwater vehicles that are simultaneously inexpensive and simple to manufacture.

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.

Emergence of robust self-organized undulatory swimming based on local hydrodynamic force sensing

Undulatory swimming represents an ideal behavior to investigate locomotion control and the role of the underlying central and peripheral components in the spinal cord. Many vertebrate swimmers have central pattern generators and local pressure-sensitive receptors that provide information about the surrounding fluid. However, it remains difficult to study experimentally how these sensors influence motor commands in these animals. Here, using a specifically designed robot that captures the essential components of the animal neuromechanical system and using simulations, we tested the hypothesis that sensed hydrodynamic pressure forces can entrain body actuation through local feedback loops. We found evidence that this peripheral mechanism leads to self-organized undulatory swimming by providing intersegmental coordination and body oscillations. Swimming can be redundantly induced by central mechanisms, and we show that, therefore, a combination of both central and peripheral mechanisms offers a higher robustness against neural disruptions than any of them alone, which potentially explains how some vertebrates retain locomotor capabilities after spinal cord lesions. These results broaden our understanding of animal locomotion and expand our knowledge for the design of robust and modular robots that physically interact with the environment.

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.

A 3D underwater robotic collective called Blueswarm

A swarm of agile fish-robots uses vision-based implicit coordination to demonstrate self-organizing behaviors in a laboratory tank.

Dual-sensor fusion based attitude holding of a fin-actuated robotic fish

In nature, the lateral line system (LLS) is a critical sensor organ of fish for rheotaxis in complex environments. Inspired by the LLS, numbers of artificial lateral line systems (ALLSs) have been designed to the fish-like robots for flow field perception, assisting the robots to be stable in the face of flow disturbances. However, almost all pressure sensor based ALLSs face the challenge of the low signal to noise ratio (SNR), resulting in inaccurate perception information. To solve this problem, this paper describes a dual-sensor fusion method by integrating the ALLSs with the inertial measurement unit (IMU), and shows the excellent performance by a higher precision and lower latency attitude holding of robotic fish. First, low-pass filtering is performed on ALLS data with low-SNR. Second, the ALLS data is mapped to the angle of attack based on an artificial neural network. Finally, a fusion perception method is established based on the time correlation between ALLS and IMU. To demonstrate the efficacy of our proposed method, we compare the result of attitude holding by three methods (dual-sensor fusion method, IMU based method, and ALLS based method). Furthermore, dual-sensor fusion method is tested at varied flow velocities and varied desired angles of attack, indicating that the algorithm can enable the robotic fish to perform dynamic movements in the incoming flow. This work provides a method for the attitude control of autonomous underwater vehicles (AUVs) by fusing the sensory data of ALLS and IMU, which is also applicable to other flow sensors and IMU.

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.

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.

Flow field perception based on the fish lateral line system

Fish are able to perceive the surrounding weak flow and pressure variations with their mechanosensory lateral line system, which consists of a superficial lateral line for flow velocity detection and a canal lateral line for flow pressure gradient perception. Achieving a better understanding of the flow field perception algorithms of the lateral line can contribute not only to the design of highly sensitive flow sensors, but also to the development of underwater smart skin with good hydrodynamic imaging properties. In this review, we discuss highly sensitive flow-sensing mechanisms for superficial and canal neuromasts and flow field perception algorithms. Artificial lateral line systems with different transduction mechanisms are then described with special emphasis on the recent innovations in the field of polymer-based artificial flow sensors. Finally, we discuss our perspective of the technological challenges faced while improving flow sensitivity, durability, and sensing fusion schemes.

Canal neuromasts enhance foraging in zebrafish (Danio rerio)

Aquatic animals commonly sense flow using superficial neuromasts (SNs), which are receptors that extend from the body's surface. The lateral line of fishes is unique among these systems because it additionally possesses receptors, the canal neuromasts (CNs), that are recessed within a channel. The lateral line has inspired the development of engineered sensors and concepts in the analysis of flow fields for submersible navigation. The biophysics of CNs are known to be different from the SNs and thereby offer a distinct submodality. However, it is generally unclear whether CNs play a distinct role in behavior. We therefore tested whether CNs enhance foraging in the dark by zebrafish (Danio rerio), a behavior that we elicited with a vibrating rod. We found that juvenile fish, which have only SNs, bite at this rod at about one-third the rate and from as little as one-third the distance of adults for a high-frequency stimulus (50  <  f   <  100 Hz). We used novel techniques for manipulating the lateral line in adults to find that CNs offered only a modest benefit at a lower frequency (20 Hz) and that foraging was mediated entirely by cranial neuromasts. Consistent with our behavioral results, biophysical models predicted CNs to be more than an order of magnitude more sensitive than SNs at high frequencies. This enhancement helps to overcome the rapid spatial decay in high-frequency components in the flow around the stimulus. These findings contrast what has been previously established for fishes that are at least ten-times the length of zebrafish, which use trunk CNs to localize prey. Therefore, CNs generally enhance foraging, but in a manner that varies with the size of the fish and its prey. These results have the potential to improve our understanding of flow sensing in aquatic animals and engineered systems.

Design and simulation of artificial fish lateral line

Fishes can avoid colliding with obstacles and track baits depending on the lateral distributed sense nodes which can sense the pressure variances of the surrounding flow field. Fish often uses the lateral-line system as their only means for navigation, especially under poor visual conditions. This sensing mechanism provides a new perspective for researchers and engineers to build such a sensing system that could be applied to control and near field navigation for underwater robots and vehicles. In this article, a pressure-sensing-based is proposed, with 10 pressure sensors acting as lateral line and use the three-dimensional printer to print the fish structure and install the artificial lateral line on it. Through preliminary experiments and numerical simulation, we obtain the pressure sensor data. By comparing the experimental data with the numerical simulation data, it can be verified that the pressure variation of the pressure sensor in the numerical simulation data is consistent with that in the experimental data. The artificial lateral line provides a new sense to man-made underwater vehicles and marine robots, so that they can sense like fish.

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.

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.

Head width influences flow sensing by the lateral line canal system in fishes

The architecture of the cephalic lateral line canal system, with distinct lines for the supraorbital, infraorbital and mandibular canals, is highly conserved among fish species. Because these canals lie on a cranial platform, the sensory input they receive is expected to change based on how flow interacts with the head and how the canal pores are spatially distributed. In this study, we explored how head width, a trait that can vary greatly between species and across ontogeny, affects flow sensing. We inserted pressure sensors into physical fish head models of varying widths (narrow, intermediate and wide) and placed these models in steady and vortical flows. We measured sensory performance in terms of detecting flow parameters (flow speed, vortex shedding frequency and cylinder diameter), sensitivity (change in pressure gradient as a function of flow speed) and signal-to-noise ratio (SNR; strength of vortex shedding frequency with respect to background). Our results show that in all model heads the amount of hydrodynamic information was maximized at the anterior region regardless of what metric we used to evaluate the sensory performance. In addition, we discovered that all model heads had the highest SNR for vortices at the intermediate flow speeds but that each head width passively optimized the SNR for different sized vortices, which may have implications for refuge and prey seeking. Our results provide insight into the sensory ecology of fishes and have implications for the design of autonomous underwater vehicles.

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.

Model-based observer and feedback control design for a rigid Joukowski foil in a Kármán vortex street

Obstacles and swimming fish in flow create a wake with an alternating left/right vortex pattern known as a Kármán vortex street and reverse Kármán vortex street, respectively. An energy-efficient fish behavior resembling slaloming through the vortex street is called Kármán gaiting. This paper describes the use of a bioinspired array of pressure sensors on a Joukowski foil to estimate and control flow-relative position in a Kármán vortex street using potential flow theory, recursive Bayesian filtering, and trajectory-tracking feedback control. The Joukowski foil is fixed in downstream position in a flowing water channel and free to move on air bearings in the cross-stream direction by controlling its angle of attack to generate lift. Inspired by the lateral-line neuromasts found in fish, the sensing and control scheme is validated using off-the-shelf pressure sensors in an experimental testbed that includes a flapping device to create vortices. We derive a potential flow model that describes the flow over a Joukowski foil in a Kármán vortex street and identify an optimal path through a Kármán vortex street using empirical observability. The optimally observable trajectory is one that passes through each vortex in the street. The estimated vorticity and location of the Kármán vortex street are used in a closed-loop control to track either the optimally observable path or the energetically efficient gait exhibited by fish. Results from the closed-loop control experiments in the flow tank show that the artificial lateral line in conjunction with a potential flow model and Bayesian estimator allow the robot to perform fish-like slaloming behavior in a Kármán vortex street. This work is a precursor to an autonomous robotic fish sensing the wake of another fish and/or performing pursuit and schooling behavior.

Research on Flow Field Perception Based on Artificial Lateral Line Sensor System

In nature, the lateral line of fish is a peculiar and important organ for sensing the surrounding hydrodynamic environment, preying, escaping from predators and schooling. In this paper, by imitating the mechanism of fish lateral canal neuromasts, we developed an artificial lateral line system composed of micro-pressure sensors. Through hydrodynamic simulations, an optimized sensor structure was obtained and the pressure distribution models of the lateral surface were established in uniform flow and turbulent flow. Carrying out the corresponding underwater experiment, the validity of the numerical simulation method is verified by the comparison between the experimental data and the simulation results. In addition, a variety of effective research methods are proposed and validated for the flow velocity estimation and attitude perception in turbulent flow, respectively and the shape recognition of obstacles is realized by the neural network algorithm.

Artificial lateral line based local sensing between two adjacent robotic fish

The lateral line system (LLS) is a mechanoreceptive organ system with which fish and aquatic amphibians can effectively sense the surrounding flow field. The reverse Kármán vortex street (KVS), known to be a typical thrust-producing wake, is commonly observed in fish-like locomotion and is known to be generated by fish's tails. The vortex street generally reflects the motion information of the fish. A fish can use LLS to detect such vortex streets generated by its neighboring fish, thus sensing its own state and the states of its neighbors in a school of fish. Inspired by this typical biological phenomenon, we design a robotic fish with an onboard artificial lateral line system (ALLS) composed of pressure sensor arrays and use it to detect the reverse KVS-like vortex wake generated by its adjacent robotic fish. Specifically, the vortex wake results in hydrodynamic pressure variations (HPVs) in the flow field. By measuring the HPV using the ALLS and extracting meaningful information from the pressure sensor readings, the oscillating frequency/amplitude/offset of the adjacent robotic fish, the relative vertical distance and the relative yaw/pitch/roll angle between the robotic fish and its neighbor are sensed efficiently. This work investigates the hydrodynamic characteristics of the reverse KVS-like vortex wake using an ALLS. Furthermore, this work demonstrates the effectiveness and practicability of an artificial lateral line in local sensing for adjacent underwater robots, indicating the potential to improve close-range interaction and cooperation within a group of underwater vehicles through the application of ALLSs in the near future.

Performance of neural networks for localizing moving objects with an artificial lateral line

Fish are able to sense water flow velocities relative to their body with their mechanoreceptive lateral line organ. This organ consists of an array of flow detectors distributed along the fish body. Using the excitation of these individual detectors, fish can determine the location of nearby moving objects. Inspired by this sensory modality, it is shown here how neural networks can be used to extract an object's location from simulated excitation patterns, as can be measured along arrays of stationary artificial flow velocity sensors. The applicability, performance and robustness with respect to input noise of different neural network architectures are compared. When trained and tested under high signal to noise conditions (46 dB), the Extreme Learning Machine architecture performs best with a mean Euclidean error of 0.4% of the maximum depth of the field D, which is taken half the length of the sensor array. Under lower signal to noise conditions Echo State Networks, having recurrent connections, enhance the performance while the Multilayer Perceptron is shown to be the most noise robust architecture. Neural network performance decreased when the source moves close to the sensor array or to the sides of the array. For all considered architectures, increasing the number of detectors per array increased localization performance and robustness.

Reliable underwater dipole source characterization in 3D space by an optimally designed artificial lateral line system

Inspired by the lateral line of aquatic vertebrates, an artificial lateral line (ALL) system can localize and track an underwater moving object by analyzing the ambient flow caused by its motion. There are several studies on object detection, localization and tracking by ALL systems, but only a few have investigated the optimal design of the ALL system, the one that on average provides the highest characterization accuracy. Design optimization is particularly important because the uncertainties in the employed flow model and in sensor measurements deteriorate the reliability of sensing. This study investigates the optimal design of the ALL system in three-dimensional (3D) space for dipole source characterization. It highlights some challenges specific to the 3D setting and demonstrates the shortcomings of the designs in which all sensors and their sensing directions are in the same plane. As an alternative, it proposes two design concepts, called 'Offset Strategy' and 'Angle Strategy' to overcome these shortcomings. It investigates potentials of having a swarm of cooperative ALLs as well. It performs design optimization in the presence of sensor and model uncertainties and analyzes the trade-off between the number of sensors and characterization accuracy. The obtained solutions are analyzed to reveal their strategies in solving the problem efficiently. The dependency of the optimized solutions on the uncertainties is also demonstrated.

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

Lateral line is a system of sense organs that can aid fishes to maneuver in a dark environment. Artificial lateral line (ALL) imitates the structure of lateral line in fishes and provides invaluable means for underwater-sensing technology and robot fish control. This paper reviews ALL, including sensor fabrication and applications to robot fish. The biophysics of lateral line are first introduced to enhance the understanding of lateral line structure and function. The design and fabrication of an ALL sensor on the basis of various sensing principles are then presented. ALL systems are collections of sensors that include carrier and control circuit. Their structure and hydrodynamic detection are reviewed. Finally, further research trends and existing problems of ALL are discussed.

Crocodile-inspired dome-shaped pressure receptors for passive hydrodynamic sensing

Passive mechanosensing is an energy-efficient and effective recourse for autonomous underwater vehicles (AUVs) for perceiving their surroundings. The passive sensory organs of aquatic animals have provided inspiration to biomimetic researchers for developing underwater passive sensing systems for AUVs. This work is inspired by the 'integumentary sensory organs' (ISOs) which are dispersed on the skin of crocodiles and are equipped with slowly adapting (SA) and rapidly adapting (RA) receptors. ISOs assist crocodiles in locating the origin of a disturbance, both on the water surface and under water, thereby enabling them to hunt prey even in a dark environment and turbid waters. In this study, we construct SA dome receptors embedded with microelectromechanical systems (MEMS) piezoresistive sensors to measure the steady-state pressures imparted by flows and RA dome receptors embedded with MEMS piezoelectric sensors to detect oscillatory pressures in water. Experimental results manifest the ability of SA and RA dome receptors to sense the direction of steady-state flows and oscillatory disturbances, respectively. As a proof of concept, the SA domes are tested on the hull of a kayak under various pressure variations owing to different types of movements of the hull. Our results indicate that the dome receptors are capable of discerning the angle of attack and speed of the flow.

A multiple-shape memory polymer-metal composite actuator capable of programmable control, creating complex 3D motion of bending, twisting, and oscillation

Development of biomimetic actuators has been an essential motivation in the study of smart materials. However, few materials are capable of controlling complex twisting and bending deformations simultaneously or separately using a dynamic control system. Here, we report an ionic polymer-metal composite actuator having multiple-shape memory effect, and is able to perform complex motion by two external inputs, electrical and thermal. Prior to the development of this type of actuator, this capability only could be realized with existing actuator technologies by using multiple actuators or another robotic system. This paper introduces a soft multiple-shape-memory polymer-metal composite (MSMPMC) actuator having multiple degrees-of-freedom that demonstrates high maneuverability when controlled by two external inputs, electrical and thermal. These multiple inputs allow for complex motions that are routine in nature, but that would be otherwise difficult to obtain with a single actuator. To the best of the authors' knowledge, this MSMPMC actuator is the first solitary actuator capable of multiple-input control and the resulting deformability and maneuverability.

Design and application of a fish-shaped lateral line probe for flow measurement

We introduce the lateral line probe (LLP) as a measurement device for natural flows. Hydraulic surveys in rivers and hydraulic structures are currently based on time-averaged velocity measurements using propellers or acoustic Doppler devices. The long-term goal is thus to develop a sensor system, which includes spatial gradients of the flow field along a fish-shaped sensor body. Interpreting the biological relevance of a collection of point velocity measurements is complicated by the fact that fish and other aquatic vertebrates experience the flow field through highly dynamic fluid-body interactions. To collect body-centric flow data, a bioinspired fish-shaped probe is equipped with a lateral line pressure sensing array, which can be applied both in the laboratory and in the field. Our objective is to introduce a new type of measurement device for body-centric data and compare its output to estimates of conventional point-based technologies. We first provide the calibration workflow for laboratory investigations. We then provide a review of two velocity estimation workflows, independent of calibration. Such workflows are required as existing field investigations consist of measurements in environments where calibration is not feasible. The mean difference for uncalibrated LLP velocity estimates from 0 to 50 cm/s under in a closed flow tunnel and open channel flume was within 4 cm/s when compared to conventional measurement techniques. Finally, spatial flow maps in a scale vertical slot fishway are compared for the LLP, direct measurements, and 3D numerical models where it was found that the LLP provided a slight overestimation of the current velocity in the jet and underestimated the velocity in the recirculation zone.

Distributed flow sensing for closed-loop speed control of a flexible fish robot

Flexibility plays an important role in fish behavior by enabling high maneuverability for predator avoidance and swimming in turbulent flow. This paper presents a novel flexible fish robot equipped with distributed pressure sensors for flow sensing. The body of the robot is molded from soft, hyperelastic material, which provides flexibility. Its Joukowski-foil shape is conducive to modeling the fluid analytically. A quasi-steady potential-flow model is adopted for real-time flow estimation, whereas a discrete-time vortex-shedding flow model is used for higher-fidelity simulation. The dynamics for the flexible fish robot yield a reduced model for one-dimensional swimming. A recursive Bayesian filter assimilates pressure measurements to estimate flow speed, angle of attack, and foil camber. The closed-loop speed-control strategy combines an inverse-mapping feedforward controller based on an average model derived for periodic actuation of angle-of-attack and a proportional-integral feedback controller utilizing the estimated flow information. Simulation and experimental results are presented to show the effectiveness of the estimation and control strategy. The paper provides a systematic approach to distributed flow sensing for closed-loop speed control of a flexible fish robot by regulating the flapping amplitude.