Biomimetic Sonar Research Articles

Biomimetic Sonar Innovation Inspired from Dolphins: A Comprehensive Review

Abstract: The sonar of dolphins has evolved over millions of years and has attained outstanding performance levels. Using the exceptional performance of the dolphins’ sonar as an impetus, bio-inspired wideband acoustic sensing approaches for underwater target recognition and tracking are under development. In this study, we have sight seen what they expect to extract as a gain from such a wideband sonar. The systems wideband sensors are grounded on bottlenose dolphins’ sonar, encapsulating a frequency band from about 30 to 150 kHz and having a frequency reliant on beamwidth substantially larger than that of conventional imaging sonars. The system can be made fairly compact and apt for mounting on diverse platforms including small-scale autonomous underwater automobiles to permit the sonar to function in an analogous way to that used by dolphins. In this paper, we have highlighted the mechanism and applications of the sonar innovation in various domains along with the ongoing developments with regard to it.

A robotic platform for integrating biomimetic sonar and AI

To gain a first-hand view of the biosonar inputs that a bat receives when operating in a complex natural environment, a bioinspired sonar head to mimic the auditory periphery of the bats' biosonar system is currently under development. The sonar head seeks to replicate the static geometric complexity of the baffles that emit the biosonar pulses and receive the returning echoes in horseshoe bats. Furthermore, some of the dynamic deformations of these structures are mimicked using tension-driven soft-robotics actuated by a set of 12 motors (two for the noseleaf and five for the each pinna). The periphery has been interfaced with acoustic emission and reception systems as well as a modular system control system for the acoustic input and output as well as the motors. Besides low-level control, the sonar head has onboard computing resources that can support deep-learning inference in the loop with acoustic data acquisition and pulse generation. All functional components have been integrated into a custom-designed shell that allows operating the system in natural outdoor environments. A wireless user interface allows remote control over the system so that the tasks of carrying and orienting the sonar head and controlling its function can be shared between two individuals.

Generating Ultrasonic Foliage Echoes with Variational Autoencoders

Navigation through dense foliage presents a fundamental challenge to autonomous systems, and achieving a performance level similar to echolocating bats could have important applications in areas such as forestry and farming. However, the clutter echoes originating from such environments have been difficult to analyze. To study the problem of sonar‐based navigation in dense foliage in simulation, an artificial generation system for leaf impulse responses (IRs) based on variational auto‐encoders is proposed. The system is to aid the construction of artificial foliage echo environments. A dataset of leaf echoes was collected in an anechoic chamber and convolved with the original signal to estimate the IR of each leaf. A modified version of the conditional variational autoencoder ‐ generative adversarial network (cVAE‐GAN) architecture was trained successfully on this dataset to produce a generative model that was conditional on leaf viewing angles, size, and species. The IRs generated by the model capture quantitative and qualitative similarity to the measured IRs. It surpasses the previous state of the art foliage echo model based on reflecting disks. The model's computational efficiency and its success suggest its potential use for simulating large environments of foliage to study bat biosonar and aid in engineering biomimetic sonar devices.

Autonomous localization and mapping based on biomimetic sonar in natural environments

Bats navigating in dense vegetation based on biosonar have to obtain the necessary sensory information from “clutter echoes,” i.e., echoes that are superpositions of contributions of many reflecting facets (e.g., leaves). Since the locations and reflective properties of the individual facets are unknown, clutter echoes have to be treated as random signals that can neither be predicted nor—under most practical circumstance—be replicated. Nevertheless, prior research has shown that deep neural networks are capable of extracting fairly precise location information from clutter echoes. This raises the question whether clutter echoes could be used to provide navigational guidance in a conventional simultaneous localization and mapping (SLAM) framework, which is to a large extent dependent on precise and deterministic measurement to generate a localized map. Our hypothesis is that biomimetic sonar echoes indeed contain the information that is necessary support to local path planning, which can be utilized by a suitable deep learning architecture and training process. To investigate this issue, we have collected data in natural, heavily vegetated environments using a biomimetic sonar head that mimics the periphery of the biosonar system in horseshoe bats. The annotation of the data and the network training process is currently undergoing.

Investigating of the impact of biommetic pinna dynamics on sonar tasks in natural environments

Bats are known to perform incredible feats using their echolocation abilities, which include natural tasks, such as navigation through foliage and hunting prey, as well as tasks they have been trained to perform, such as classification of man-made objects. Much work has been done to engineer biomimetic systems that can perform these and similar tasks in a comparable way to bats. Biomimetic sonar devices have performed tasks such as identifying a geographic location, object classification, and foliage gap finding. Particular species of bats (notably those of the families Hipposideridae and Rhinolophidae) use rapid ear motion while making sonar calls, and the full effect and purpose of this motion is not fully understood. Our research goal is to perform sonar tasks with a biomimetic sonar head capable of both static and dynamic ear motion and investigate the effect of dynamic ear motion on captured echoes and performance on tasks. To this end, we have collected echo data in natural habitats with dense tropical vegetation in Brunei on the island of Borneo. Tasks currently under investigation include location identification and detection of man-made objects in foliage, and are performed by deep learning classifiers trained on echoes taken by the biomimetic sonar device.

Understanding the Concept of Biomimetics for Geoinformatics Technology and its utilization for Nature-based Solutions

This study aims to introduce a wide range of readers to some of animals' exceptional remote sensing and navigation abilities. A comparison of remote sensors employed by animals and those built by humans is presented in this research. Thermal infrared sensors used by snakes, echolocation used by bats and dolphins, and navigation systems used by birds are all part of the research and comparison. Prey countermeasures to prevent capture are also taken into account. Remote sensing and navigation capabilities of certain animals are far superior to those provided by the human body or devised by humans. The use of the biometric method in creating novel sensors may be promoted. The paper contributes to a better understanding of animal behavior, particularly their unique abilities to perceive, echolocate, and travel over long distances with great accuracy. Studying animal sensors within a biomimetic framework can help remote sensor designers improve their designs for the purpose of border management, forest fire control (for example, the photo mechanic infrared receptor for detecting forest fires in the beetle), jamming the signal from the tiger moth which clogs bat sonar, integration of visual and infrared information in bimodal neurons in the rattlesnake optic tectum, and biomimetic sonar that locates and recognizes objects.

Artificial neural network classification of foliage targets from spectrograms of sequential echoes using a biomimetic audible sonar

Classifying foliage targets using echolocation is important for recognizing landmarks by bats using ultrasonic emissions and blind human echolocators (BEs) using palatal clicks. Previous attempts to classify foliage used ultrasonic frequencies and single sensor (monaural) detection. Motivated by the echolocation capabilities of BEs, a biomimetic sonar emitting audible clicks acquired 5600 binaural echoes from five sequential emissions that probed two foliage targets at aspect angles separated by 18°. Echo spectrograms formed feature vector inputs to artificial neural networks (ANNs) for classifying two targets, Ficus benjamina and Schefflera arboricola, with leaf areas that differ by a factor of four. Classification performances of ANNs without and with hidden layers were analyzed using tenfold cross-validation. Performance improved with input feature size, with binaural echo classification outperforming that using monaural echoes for the same number of emissions and for the same number of echoes. Linear classification accuracy was comparable to that using nonlinear classification with both achieving fewer than 1% errors with binaural spectrogram features from five sequential emissions. This result was better by a factor of 20 compared to previous classification of these targets using only the time envelopes of the same echoes.

Artificial neural network classification of surface reflectors and volume scatterers using sequential echoes acquired with a biomimetic audible sonar.

This paper investigates classifying two target groups, surface reflectors (SR) and volume scatterers (VS), using echo envelope features. SR targets have convex surface patches that exhibit echo persistence over aspect angle, while VS targets are composed of random range-distributed and oriented reflectors producing echoes that become uncorrelated with small changes in aspect angle. The SR target group contains single-post (P1) and multiple-post (PM) types and the VS group contains Ficus benjamina (F) and Schefflera arboricola (S) foliage types with leaf areas that differ by a factor of 4. A biomimetic sonar emitting audible clicks acquired sequences of up to three binaural echoes from target views separated by 18°. Two artificial neural networks performing linear and nonlinear classification first differentiated SR/VS target groups and then P1/PM and F/S types. Classification performance improved with echo number, from a single monaural echo to three pairs of binaural echoes, demonstrating the benefit of sequential echoes. Linear and nonlinear classification of SR/VS targets achieved a minimum generalization error probability PEG = 0.003. Nonlinear P1/PM classification achieved PEG = 0.009 that was four times smaller than linear classification. Nonlinear F/S classification achieved PEG = 0.220, indicating that envelope features by themselves are inadequate to accurately differentiate foliage targets.

Learning relationships between bat habitats and biosonar behavior

Echolocating bats are known to adapt their biosonar behaviors according to their surroundings. Vespertilionid bats, for example, adapt their pulse duration and bandwidth as a function of their proximity to foliage. However, these relationships have been based on mostly qualitative observations so far. More powerful methods that are capable of handling large quantitative datasets could produce further and more detailed insights. In the current work, aligned multimodal datasets have been gathered to address this challenge: habitat geometry (laser scans), acoustic scene (biomimetic active sonar system), bat flight trajectories (infrared camera array), and biosonar behavior (microphone array). Machine learning approaches can then be used to analyze relationships between the components of these multimodal datasets. To do this, representations of the bat’s surroundings that are suitable for biosonar problems have been developed, for example a probability density function of the angles and distances from the laser scanned points of a habitat model relative to the bat’s position and orientation. These representations will serve as inputs to machine learning algorithms that can predict the quantitative features of the bat’s biosonar behavior. This research could lead to a better understanding of bat pulse adaptation and also aid in developing biomimetic sonar navigation systems.Echolocating bats are known to adapt their biosonar behaviors according to their surroundings. Vespertilionid bats, for example, adapt their pulse duration and bandwidth as a function of their proximity to foliage. However, these relationships have been based on mostly qualitative observations so far. More powerful methods that are capable of handling large quantitative datasets could produce further and more detailed insights. In the current work, aligned multimodal datasets have been gathered to address this challenge: habitat geometry (laser scans), acoustic scene (biomimetic active sonar system), bat flight trajectories (infrared camera array), and biosonar behavior (microphone array). Machine learning approaches can then be used to analyze relationships between the components of these multimodal datasets. To do this, representations of the bat’s surroundings that are suitable for biosonar problems have been developed, for example a probability density function of the angles and distances from the las...

Bats Actively Use Leaves as Specular Reflectors to Detect Acoustically Camouflaged Prey

Filtering relevant signals from noisy sensory input is a crucial challenge for animals [1, 2]. Many bats are acoustic specialists relying on sound to find prey. They discern salient acoustic signals from irrelevant background masking noise. It has long been considered a sensory impossibility for bats to use solely echolocation for the detection of silent and motionless prey resting directly on foliage due to the masking effects of background echoes [3, 4]. Some bats, however, do successfully perform this seemingly impossible task [5], raising the question-what underlying acoustic and behavioral mechanisms do bats use to solve this conundrum? To address this question, we used biomimetic sonar to record high-resolution measurements of echoes from insects resting on leaves. Based on our echo recordings, we predicted optimal approach angles from which masking echoes can best be avoided. In behavioral experiments, we put these predictions to test. We recorded the prey approach behavior of wild bats in a flight cage equipped with an ultrasonic microphone synchronized with two high-speed cameras for 3D flightpath reconstructions. Bats approached prey from our predicted optimal oblique angles, using the leaf as a specular reflector to uncover previously acoustically hidden prey. Our findings disclose key behavioral and acoustic mechanisms enabling the detection of prey echoes that background clutter would otherwise mask. This work adds to the fundamental understanding of how bat echolocation strategies can override acoustic camouflage by silent, motionless prey, thus providing new insights into the evolutionary arms race between predators and their prey.

A miniature biomimetic sonar and movement tag to study the biotic environment and predator-prey interactions in aquatic animals

How predators find, select and capture prey is central to understanding trophic cascades and ecosystem structure. But despite advances in biologging technology, obtaining in situ observations of organisms and their interactions remains challenging in the marine environment. For some species of toothed whales, echoes from organisms insonified by echolocation clicks and recorded by sound logging tags have provided a fine-scale view of prey density, and predator and prey behaviour during capture attempts, but such information is not available for marine predators that do not echolocate. Here the development and performance of a miniature biomimetic sonar and movement tag capable of acquiring similar data from non-echolocating marine predators is reported. The tag, weighing 200 g in air, records wide bandwidth sonar data at up to 50 pings a second synchronously with fast-sampling sensors for depth, acceleration, magnetic field and GPS. This sensor suite enables biotic conditions and predator behaviour to be related to geographic location over long-duration foraging trips by apex marine predators. The sonar operates at 1.5 MHz with a 3.4° beamwidth and a source level of 190 dB re 1 μPa at 1 m. Sonar recordings from a trial deployment of the tag on a southern elephant seal contained frequent targets corresponding to small organisms up to 6 m ahead of the tagged animal. Synchronously sampled movement data allowed interpretation of whether the seal attempted to capture organisms that it approached closely while the high sonar ping rate revealed attempts by prey to escape. Results from this trial demonstrate the ability of the tag to quantify the biotic environment and to track individual prey captures, providing fine-scale information on predator-prey interactions which has been difficult to obtain from non-echolocating marine animals.

Biomimetic sonar and the problem of finding deterministic targets in foliage

The ability of (bio)sonars to find targets-of-interest is often hampered by a cluttered environment. For example, naval sonars encounter difficulties finding mines partially or fully buried within sand. Such situations pose target identification challenges are much harder than target detection and resolution problems. There are many bat species which navigate and hunt in dense vegetation and thus must be able to identify targets-of-interest within clutter. Evolutionary adaptation of the bat biosonar system is likely to have resulted in the “discovery” of features that support making distinctions between clutter and echoes of interest. The most well-established case is given by cf-fm bats that use Doppler shifts caused by the wingbeat of a flying insect prey to identify the prey in foliage. Other bat species have been shown to use a passive sonar approach that is based on unique prey-generated acoustic signals. Some of the most interesting cases can be found in bat species that are successful in finding preys that apparently does not emit any distinguishing sounds themselves and would hence limit the bats to an active-sonar approach. Such bat species could provide model species for new ways in which the target-identification problem in clutter can be solved with active sonar.

Coordination of sonar emitter and receiver dynamics inspired by bats

Recent biomimetic sonar systems have started to mimic the mobility seen in the emission and reception baffles of bats (i.e., “noseleaves” and pinnae) in greater detail. Some of these efforts have included rigid rotations as well as non-rigid shape changes. Since some bat species are known to actuate their noseleaves during pulse emission and their two pinnae during pulse reception, it may be hypothesized that coordination between the dynamic behaviors of these baffle structures is important to system-level biosonar performance. However, it remains to be determined how the complicated rigid and non-rigid changes to the noseleaves and pinnae of bats are coordinated in the animals' biosonar behaviors. To shed some light on this dynamic system integration in bat biosonar, we have conducted experiments with a species of hipposiderid bat (Pratt's roundleaf bat, Hipposideros pratti) where at least 24 landmark points have been placed on the noseleaf and both pinnae in order to track the non-rigid shape changes in all these three structures simultaneously. A canonical correlation analysis has confirmed that the motions of the noseleaf and both pinnae are highly correlated with each other. Work to identify the nature of these relationships using techniques from nonlinear dynamics is currently underway.

Fast moving biomimetic ears

Like many other mammals, bats are known to rely on spectral signatures to determine the directions of incoming sounds, especially in elevation. Because of its simple hardware and computational realizations, this principle could also be suitable for small, parsimonious biomimetic sonar systems. However, the biosonar systems of bat species in families such as the rhinolophids and hipposiderids often—but not always—concentrate most of the emitted energy within a narrow frequency band in order to pick up prey-induced Doppler signatures. It remains unclear how the use of such narrow-band biosonar pulses could be reconciled with direction-finding based on spectral signatures. A possible solution to this paradox could be provided by fast pinna motions in these animals that have been shown to produce readily perceivable Doppler-shift signatures in biomimetic reproductions. In the current work, such a biomimetic pinna with fast deformations has been used to map Doppler-shift signatures as a function of direction. For this purpose, the Doppler-shift signatures were clustered based on the similarity of their spectrogram-representations. Mapping the signatures' different cluster associations into direction space resulted in contiguous patches. Hence, it should be possible to obtain stable estimates of target direction based on the received Doppler signatures. <audio controls = “controls” style = “display: none;” > </audio> <audio controls = “controls” style = “display: none; “ > </audio>

The problem of finding passageways in foliage with biomimetic sonar

Bats living in densely vegetated habitats must have the ability to find passageways in foliage. To explore the sensing problem posed by this navigation task, a sonar head that mimicked a bat's biosonar periphery and emitted a bat-like chirp was placed on a linear track to scan artificial foliages (plastic leaves resembling broad-leaf foliage) interrupted by gaps of controllable width. The first approach to detecting the gaps tested has been based on comparing echo energy levels received when facing a gap as opposed to a closed foliage. The ability to detect a gap in this way depends on the employed beamwidth and the distance to the foliage, i.e., the narrower the beam and closer the sonar head, the narrower a gap can be detected. However, narrower beams were also found to increase the variability in the energy of the returned echoes which could impact the ability to reliably distinguish the different levels of echo energy associated with closed foliage and gaps. Since bats tend to have wide beams due to their limited size relative to the employed wavelengths, it may be hypothesized that the animals have evolved alternative approaches to improve the ability of detecting narrow passageways at a distance.

Biomimetic Sonar for Electrical Activation of the Auditory Pathway

Relying on the mechanism of bat’s echolocation system, a bioinspired electronic device has been developed to investigate the cortical activity of mammals in response to auditory sensorial stimuli. By means of implanted electrodes, acoustical information about the external environment generated by a biomimetic system and converted in electrical signals was delivered to anatomically selected structures of the auditory pathway. Electrocorticographic recordings showed that cerebral activity response is highly dependent on the information carried out by ultrasounds and is frequency-locked with the signal repetition rate. Frequency analysis reveals that delta and beta rhythm content increases, suggesting that sensorial information is successfully transferred and integrated. In addition, principal component analysis highlights how all the stimuli generate patterns of neural activity which can be clearly classified. The results show that brain response is modulated by echo signal features suggesting that spatial information sent by biomimetic sonar is efficiently interpreted and encoded by the auditory system. Consequently, these results give new perspective in artificial environmental perception, which could be used for developing new techniques useful in treating pathological conditions or influencing our perception of the surroundings.

A biomimetic perspective on the dynamics in horseshoe bat biosonar

Research with behaving horseshoe bats has suggested that the animals' biosonar system is characterized by pervasive dynamics at the interfaces for ultrasound emission and reception. Baffle shapes that diffract the outgoing and incoming ultrasonic pulses change their geometries during emission and reception. However, it remains unclear if and how this dynamics could affect the function of bats' biosonar system. To investigate this question, biomimetic reproduction of this peripheral dynamics have been used to obtain system characterizations as well as echo recordings. The data obtained with these biomimetic sonar systems have yielded the following key results: If the static and dynamic geometries of the diffracting baffles as well as their coupling to the transducers is suitable, time-variant device characteristics can be produced—even for small and geometrically simple changes to the shapes. These characteristics depend on direction and frequency (like a static beampattern) as well as on time. Furthermore...

Directional Receiver for Biomimetic Sonar System

An ultrasonic localization method for a sonar system equipped with an emitter and two directional receivers and inspired by bat echolocation uses knowledge of the beam pattern of the receivers to estimate target orientation. Rousettus leschenaultii's left ear constitutes the model for the design of the optimal receiver for this sonar system and 3D printing was used to fabricate receiver structures comprising of two truncated cones with an elliptical external perimeter and a parabolic flare rate in the upper part. Measurements show one receiver has a predominant lobe in the same region and with similar attenuation values as the bat ear model. The final sonar system is to be mounted on vehicular and aerial robots which require remote control for motion and sensors for estimation of each robot's location.

Seeing the world through a dynamic biomimetic sonar

The outer baffle surfaces surrounding the sonar pulse emission and reception apertures of the biosonar system of horseshoe bats (family Rhinolophidae) have been shown to dynamically deform while actively sensing the environment. It is hypothesized that this dynamic sensing strategy enables the animal, in part, to cope with dense unstructured sonar environments. In the present work, a biomimetic dynamic sonar system inspired by the biosonar system of horseshoe bats has been assembled and tested. The sonar head features dynamic deforming baffles for emission (mimicking the bats’ noseleaf) and reception (pinnae). The dynamic baffles were actuated to change their geometries concurrently with the diffraction of the emitted ultrasonic pulses and returning echoes. The time-variant signatures induced by the dynamic baffle motions were systematically characterized in a controlled anechoic setting and the interaction between emission and reception dynamic signatures was investigated. The sonar was further tested in the context of natural environments with a specific focus on the interaction of dynamic ultrasonic pulse packets with natural targets. For both experimental approaches, a sonar with static baffle shape configuration was used as a reference to establish the impact of dynamic features.

BatSLAM: Simultaneous Localization and Mapping Using Biomimetic Sonar

We propose to combine a biomimetic navigation model which solves a simultaneous localization and mapping task with a biomimetic sonar mounted on a mobile robot to address two related questions. First, can robotic sonar sensing lead to intelligent interactions with complex environments? Second, can we model sonar based spatial orientation and the construction of spatial maps by bats? To address these questions we adapt the mapping module of RatSLAM, a previously published navigation system based on computational models of the rodent hippocampus. We analyze the performance of the proposed robotic implementation operating in the real world. We conclude that the biomimetic navigation model operating on the information from the biomimetic sonar allows an autonomous agent to map unmodified (office) environments efficiently and consistently. Furthermore, these results also show that successful navigation does not require the readings of the biomimetic sonar to be interpreted in terms of individual objects/landmarks in the environment. We argue that the system has applications in robotics as well as in the field of biology as a simple, first order, model for sonar based spatial orientation and map building.