Imaging dipole flow sources using an artificial lateral-line system made of biomimetic hair flow sensors

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.

Three-dimensional multi-source localization of underwater objects using convolutional neural networks for artificial lateral lines

This research focuses on the signal processing required for a sensory system that can simultaneously localize multiple moving underwater objects in a three-dimensional (3D) volume by simulating the hydrodynamic flow caused by these objects. We propose a method for localization in a simulated setting based on an established hydrodynamic theory founded in fish lateral line organ research. Fish neurally concatenate the information of multiple sensors to localize sources. Similarly, we use the sampled fluid velocity via two parallel lateral lines to perform source localization in three dimensions in two steps. Using a convolutional neural network, we first estimate a two-dimensional image of the probability of a present source. Then we determine the position of each source, via an automated iterative 3D-aware algorithm. We study various neural network architectural designs and different ways of presenting the input to the neural network; multi-level amplified inputs and merged convolutional streams are shown to improve the imaging performance. Results show that the combined system can exhibit adequate 3D localization of multiple sources.

Research on an Artificial Lateral Line System Based on a Bionic Hair Sensor with Resonant Readout

Inspired by the lateral line system of fish, an artificial lateral line system based on bionic hair sensor with resonant readout is presented in this paper. An artificial lateral line system, which possesses great application potential in the field of gas flow visualization, includes two different sensors: a superficial neuromast and a canal neuromast flow velocity sensor, which are used to measure the constant and oscillatory air flow velocity, respectively. The sensitive mechanism of two artificial lateral line sensors is analyzed, and a finite element simulation is implemented to verify the structural design. Then the control circuit of the artificial lateral line system is designed, employing a demodulation algorithm of oscillatory signal based on the least mean square error algorithm, which is used to calculate the oscillatory air flow velocity. Finally, the experiments are implemented to assess the performance of the two artificial lateral line systems. The experimental results show that the artificial lateral line system, which can be used to measure the constant and oscillatory air flow velocity, has a minimum threshold of 0.785 mm/s in the measurement of oscillatory air flow velocity. Moreover, the artificial canal neuromast lateral line system can filter out low-frequency disturbance and has good sensitivity for high-frequency flow velocity.

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.

Using fish lateral line sensing to improve seismic acquisition and processing

Bioengineering, which studies the principles and design of biological systems, is a field that has inspired the development of several technologies that are currently in use. In this work, we use concepts from the fish lateral line sensing mechanism and apply them to seismic imaging processing. The lateral line is a sensory system composed of an integrated array of mechanical sensors spanning along the fish body. We compare the array of sensors along body fish with the seismic acquisition, which employs an array of equally spaced identical mechanical sensors to image the Earth’s subsurface. In both situations, the mechanical sensors capture and process mechanical vibrations from the environment to produce useful information. We explore the strategy of using the low-pass and high-pass sensors schema of fish lateral line to improve the seismic technique. We use the full-wave inversion method to compare the conventional acquisition procedure of identical sensors with alternative sets of different sensors, which mimics the fish lateral line. Our results show that the alternate sensors arrangement surpasses the performance of the conventional acquisition method, using just half of the input information. The results point at an image processing technique that is computationally more efficient and economical than the usual seismic processing method.

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.

Resolution improvement of dipole source localization for artificial lateral lines based on multiple signal classification

The lateral line is a critical mechanosensory organ that enables fish to perceive the surroundings accurately and rapidly. Massive efforts have been made to build an artificial lateral line system rivaling that of fish for underwater vehicles. Dipole source localization has become a standard problem for evaluating the sensing capabilities of the developed systems. In this paper we propose, for the first time, the multiple signal classification (MUSIC) method in order to achieve high-resolution dipole source localization based on spatial spectrum estimation. We also present the minimum variance distortionless response (MVDR) by making an improvement to the previous Capon's method. Experiments are conducted on a linear prototype of lateral line canal and the localization performance of these two methods are compared. The results show that the MUSIC method provides an overall localization resolution improvement of 10.4% and maintains a similar level of localization accuracy compared with the MVDR method. Further studies show that the MUSIC method has the potential of localizing two closer incoherent dipole sources with a minimum lateral separation of 20 mm, versus 70 mm for the MVDR method, at a dipole-array distance of half the array length. Both localization methods have strong robustness to the vibrational state of the dipole source. Our work provides a promising and robust way to meet the high-resolution and multi-source sensing requirements of underwater vehicles.

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.

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.

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.

Artificial fish skin of self-powered micro-electromechanical systems hair cells for sensing hydrodynamic flow phenomena

Using biological sensors, aquatic animals like fishes are capable of performing impressive behaviours such as super-manoeuvrability, hydrodynamic flow 'vision' and object localization with a success unmatched by human-engineered technologies. Inspired by the multiple functionalities of the ubiquitous lateral-line sensors of fishes, we developed flexible and surface-mountable arrays of micro-electromechanical systems (MEMS) artificial hair cell flow sensors. This paper reports the development of the MEMS artificial versions of superficial and canal neuromasts and experimental characterization of their unique flow-sensing roles. Our MEMS flow sensors feature a stereolithographically fabricated polymer hair cell mounted on Pb(Zr(0.52)Ti(0.48))O3 micro-diaphragm with floating bottom electrode. Canal-inspired versions are developed by mounting a polymer canal with pores that guide external flows to the hair cells embedded in the canal. Experimental results conducted employing our MEMS artificial superficial neuromasts (SNs) demonstrated a high sensitivity and very low threshold detection limit of 22 mV/(mm s(-1)) and 8.2 µm s(-1), respectively, for an oscillating dipole stimulus vibrating at 35 Hz. Flexible arrays of such superficial sensors were demonstrated to localize an underwater dipole stimulus. Comparative experimental studies revealed a high-pass filtering nature of the canal encapsulated sensors with a cut-off frequency of 10 Hz and a flat frequency response of artificial SNs. Flexible arrays of self-powered, miniaturized, light-weight, low-cost and robust artificial lateral-line systems could enhance the capabilities of underwater vehicles.

Nanofibril scaffold assisted MEMS artificial hydrogel neuromasts for enhanced sensitivity flow sensing

We present the development and testing of superficial neuromast-inspired flow sensors that also attain high sensitivity and resolution through a biomimetic hyaulronic acid-based hydrogel cupula dressing. The inspiration comes from the spatially distributed neuromasts of the blind cavefish that live in completely dark undersea caves; the sensors enable the fish to form three-dimensional flow and object maps, enabling them to maneuver efficiently in cluttered environments. A canopy shaped electrospun nanofibril scaffold, inspired by the cupular fibrils, assists the drop-casting process allowing the formation of a prolate spheroid-shaped artificial cupula. Rheological and nanoindentation characterizations showed that the Young's modulus of the artificial cupula closely matches the biological cupula (10-100 Pa). A comparative experimental study conducted to evaluate the sensitivities of the naked hair cell sensor and the cupula-dressed sensor in sensing steady-state flows demonstrated a sensitivity enhancement by 3.5-5 times due to the presence of hydrogel cupula. The novel strategies of sensor development presented in this report are applicable to the design and fabrication of other biomimetic sensors as well. The developed sensors can be used in the navigation and maneuvering of underwater robots, but can also find applications in biomedical and microfluidic devices.

Touch at a distance sensing: lateral-line inspired MEMS flow sensors

Evolution bestowed the blind cavefish with a resourcefully designed lateral-line of sensors that play an essential role in many important tasks including object detection and avoidance, energy-efficient maneuvering, rheotaxis etc. Biologists identified the two types of vital sensors on the fish bodies called the superficial neuromasts and the canal neuromasts that are responsible for flow sensing and pressure-gradient sensing, respectively. In this work, we present the design, fabrication and experimental characterization of biomimetic polymer artificial superficial neuromast micro-sensor arrays. These biomimetic micro-sensors demonstrated a high sensitivity of 0.9 mV/(m s(-1)) and 0.022 V/(m s(-1)) and threshold velocity detection limits of 0.1 m s(-1) and 0.015 m s(-1) in determining air and water flows respectively. Experimental results demonstrate that the biological canal inspired polymer encapsulation on the array of artificial superficial neuromast sensors is capable of filtering steady-state flows that could otherwise significantly mask the relevant oscillatory flow signals of high importance.