Design and Implementation of Bio-inspired Underwater Electrosense

Underwater electrosense, manipulating underwater electric field for sensing purpose, is a growing technology bio-inspired by weakly electric fish that can navigate in dark or cluttered water. We studied its theoretical foundations and developed sophisticated sensing algorithms including some first-introduced techniques such as discrete dipole approximation (DDA) and convolutional neural networks (CNN), which were tested and validated by simulation and a planar sensor prototype. This work pave a solid way to applications on practical underwater robots

Electrocommunication for weakly electric fish

This paper addresses the problem of the electro-communication for weakly electric fish. In particular we aim at sheding light on how the fish circumvent the jamming issue for both electro-communication and active electro-sensing. A real-time tracking algorithm is presented

Object Localization in Fluids based on a Bioinspired Electroreceptor System

Wolf-Homeyer S. Object Localization in Fluids based on a Bioinspired Electroreceptor System. Bielefeld: Universität Bielefeld; 2019.Weakly electric fish use self-generated electric fields for communication, active electrolocation and navigation. Additionally to visual sense, this ability enables them to detect objects and food even in dark or turbid waters. Specialized muscle cells in the tail region actively generate an electric field in the surrounding fluid, shaped like a dipole between tail and head. This dipole field may be distorted depending on environmental parameters such as the presence of objects of different geometry or material properties in the animal's vicinity. Electroreceptors, distributed all over the fish' skin allow to perceive distortions of the field, caused by objects. Furthermore, fish execute stereotyped scanning behaviors to obtain additional sensory information of detected objects. The development of innovative sensor systems for short-range exploration in fluids is still in its infancy. Also, the use of electric fields in bio-inspired technologies is still at an early stage. Based on the biological model of weakly electric fish, the question has already been examined if an array of electrodes can be used for a contactless object detection and localization and finally for navigation in fluids (Solberg et al. 2008). This examination is performed by analyses of electric field modulations, based on so-called EEVs. An EEV (Ensemble of Electrosensory Viewpoints) is a scalar field representation of the influence of an object on the electric field in the form of potential differences measured between two electrodes for every possible object location. The first part of this thesis explores the characteristics of the electric dipole field and the resulting EEV by means of numerical simulations to determine the influence of an object placed in the emitted field. It will also be investigated how many receptors are required and which arrangement is to be preferred to uniquely identify the positions of spherical objects in the vicinity of the sensor system. For this, a receptor system composed of a simple biomimetic abstraction of an emitter dipole and an orthogonally arranged pair of sensor electrodes is used. Inspired by the scanning movements of the fish, a fixed, minimal scanning strategy, composed of active receptor system movements is developed. The active electrolocation strategy introduced here is based on the superposition of extracted EEV contour-rings in order to find intersections of these contours. The second part of this work focuses on the development of an *application* for active electrolocation which is based on a minimal set of scanning movements as a precursor for the partitioning of the later search area in which sensor-emitter movements take place. In this application, EEVs are also used as major components of two localization algorithms. In order to find points within the search space which are part of several contour-rings, intersection points have to found. Due to numerical inaccuracies intersection points may degrade to contour-segments which lie very close to each other but do not touch. For this case, a nearness metric is used to identify such points. However, in this part of the work the EEVs are based on a simplified analytical representation, which renders the corresponding algorithms suitable for embedded computer systems. In the third part of this thesis, a fitted histogram representation of EEVs is used to compare a large number of different movement sequences to select the optimal composition from this variety. For this, the general shape of an EEV has to be considered, which plays a major role in estimating the best sequence

Mathematics of biomimetics for active echo- and electro-sensing

Active sensing animals may inspire the development of new technologies that mimic their sensing behavior. Electric fish, for instance, orient themselves at night in complete darkness by using their active electro-sensing system. They generate a stable, relatively high-frequency, weak electric field and perceive the transdermal potential modulations caused by nearby targets with different electromagnetic properties than the surrounding water. Since they have an electric sense that allows underwater navigation, target classification and intraspecific communication, they are privileged animals for bio-inspiring man-built autonomous systems. Bats, on the other hand, process the reflected echoes due to the presence of acoustic inclusions for echolocation. In general, they use acoustic waves for most of the perceptual tasks, that range from hunting to navigating. This thesis introduces premier algorithms in electro-sensing and echo-sensing. The weakly electric fish is able to retrieve much more information about the target by approaching it. To mimic this behavior, an innovative (real-time) multi-scale method for target classification in electro-sensing is presented. The method is based on a family of transform-invariant shape descriptors computed from generalized polarization tensors (GPTs) reconstructed at multiple scales. The evidence provided by the different descriptors at each scale is fused using Dempster-Shafer Theory. Numerical simulations show that the recognition algorithm we proposed performs undoubtedly well and yields a robust classification. For real-world applications, inhomogeneous targets have to be identified. The shape descriptor-based classification algorithm is extended in order to consider inhomogenous material parameters. The approach is based on new invariants for the contracted generalized polarization tensors associated with inhomogeneous objects. The numerical simulations show that by comparing these invariants with those in a dictionary of precomputed homogeneous and inhomogeneous targets, one can successfully classify the inhomogeneous target. Another problem concerns intraspecific electro-communication for weakly electric fish. In particular, a description on how the fish circumvent the jamming issue for both electro-communication and active electro-sensing is presented. The main result is a real-time tracking algorithm, which provides a new approach to the communication problem. It finds a natural application in robotics, where efficient communication strategies are needed to be implemented by bio-inspired underwater robots. The concept of time-dependent polarization tensors (TDPTs) for the wave equation associated to a diametrically small acoustic inclusion, with constitutive parameters different from those of the background and size smaller than the operating wavelength, is used to mimic the echo-sensing capabilities of a static bat. Firstly, the solution to the Helmholtz equation is considered, and a rigorous systematic derivation of a complete asymptotic expansion of the scattered field due to the presence of the inclusion is presented. Then, by applying the Fourier transform, the corresponding time-domain expansion is readily obtained after truncating the high frequencies. The new concept of TDPTs is shown to be promising for performing imaging. Numerical simulations are presented, showing that the TDPTs reconstructed from noisy measurements allow to image fine shape details of the inclusion

Models of Causal Inference in the Elasmobranch Electrosensory System: How Sharks Find Food

We develop a theory of how the functional design of the electrosensory system in sharks reflects the inevitability of noise in high-precision measurements, and how the Central Nervous System may have developed an efficient solution to the problem of inferring parameters of stimulus sources, such as their location, via Bayesian neural computation. We use Finite Element Method to examine how the electrical properties of shark tissues and the geometrical configuration of both the shark body and the electrosensory array, act to focus weak electric fields in the aquatic environment, so that the majority of the voltage drop is signalled across the electrosensory cells. We analyse snapshots of two ethologically relevant stimuli: localized prey-like dipole electric sources, and uniform electric fields resembling motion-induced and other fields encountered in the ocean. We demonstrated that self movement (or self state) not only affects the measured field, by perturbing the self field, but also affects the external field. Electrosensory cells provide input to central brain regions via primary afferent nerves. Inspection of elasmobranch electrosensory afferent spike trains and inter-spike interval distributions indicates that they typically have fairly regular spontaneous inter-spike intervals with skewed Gaussian-like variability. However, because electrosensory afferent neurons converge onto secondary neurons, we consider the convergent input a "super afferent" with the pulse train received by a target neuron approaching a Poisson process with shorter mean intervals as the number of independent convergent spike trains increases. We implement a spiking neural particle filter which takes simulated electrosensory "super afferent" spike trains and can successfully infer the fixed Poisson parameter, or the equivalent real world state, distance to a source. The circuit obtained by converting the mathematical model to a network structure bears a striking resemblance to the cerebellar-like hindbrain circuits of the dorsal octavolateral nucleus. The elasmobranchs’ ability to sense electric fields down to a limit imposed by thermodynamics seems extraordinary. However we predict that the theories presented here generalize to other sensory systems, particularly the other octavolateralis senses which share cerebellar-like circuitry, suggesting that the cerebellum itself also plays a role in dynamic state estimation

Dynamics of sensorimotor behavior in electrolocation and electrocommunication

Pedraja F. Dynamics of sensorimotor behavior in electrolocation and electrocommunication. Bielefeld: Universität Bielefeld; 2019.How are external sensory stimuli perceived, integrated and represented within the central nervous system? How does the nervous system generate appropriate behavioral responses based on this input and how does this behavior affect perception? The above questions have in common that they view sensory input and motor control as two sides of the sensorimotor loop. In this closed-loop system, actions inevitably generate sensory flow that can serve to organize behavior. To look, to smell, to touch, etc. are perceptual acts that depend on the interaction, coordination and interpretation of motor and sensory information through neural mechanisms. Active sensory systems are particularly amenable to the study of the reciprocal relations of motor and sensory components, as parts of closed loop control structures. A notable advantage of these sensory systems is the experimental accessibility of their sensory input, both in terms of its measurement and in terms of detailed modeling reconstructions of the input. In the case of weakly electric fish studied in this thesis, the animals sense and process environmental perturbations of a self-generated electric field. The fact that this field serves as the carrier of sensory information and at the same time is controlled by the animal, enables to precisely determine aspects of sensing that are often hard to obtain or quantify in sensory systems that do not actively generate the carrier: where, when and what an animal samples. Drawing on these benefits, my thesis focuses on the role of motor and electromotor behavior in sensorimotor integration. For this, a biophysical model for the active and passive electroreception was combined with physiological recordings and behavioral approaches. The central topics addressed are: (i) Object detection and sensorimotor learning. The sensory information obtained by the African species Gnathonemus petersii while learning a detection task was computationally reconstructed using boundary element methods (BEM). This revealed that the improved task performance was paralleled by an enhancement of the quality of the sensory information, which was mediated by changes of the electromotor patterns. The versatile manner in which the fish changed the spatial and temporal allocation of otherwise stable motor components not only improved the quality of the sensory input, but also resulted in shifts of the animals' attention towards the object. (ii) Dynamic choice of optimal behavior. Extending on the above results, I next explored how changing the distance of an object to be detected by the fish influenced the electromotor behavior. With increasing complexity (distance), the fish resorted to a new motor strategy. This consisted in first approaching a salient element in the arena, from where the fish then made a perceptually-guided decision. This interpretation is backed up by analyzing the trajectories in the context of attractors, revealing that the focus of attention was altered in a task-dependent manner. (iii) Distance estimation using a non-visual form of motion parallax. In the above experiments it is implicitly assumed that electric fish acquire spatial information like the position and distance of a target. How this is achieved dynamically has been addressed recently. Based on the properties of the electric field geometry, theoretical considerations indicated that relative movements might provide depth information. In a behavioral assay, I show that this novel form of electric parallax exists and is used across phylogenetically distant taxa of weakly electric fish (Apteronotus albifrons, Eigenmania virescens and Gnathonemus petersii). Notably, these species electrically sample the environment in temporally distinct ways (using discrete pulses or quasi-sinusoidal waves), suggesting an ubiquitous role for parallax in electric sensing. (iv) The role of multi-modal integration in socially relevant agonistic behaviour. Extending on the above results, I next addressed if passive as well as active electric sensory information can be used to evaluate more complex features of the environment. For this I turned to social interactions of the South American species Gymnotus omarorum to study if an electrical assessment of a competitor is possible. Based on modeling the sensory consequences of dyadic encounters, I showed that passive as well as active sensory information can drive agonistic interactions. This suggests that aggressive interactions may be triggered by information about contenders obtained through the active and passive electrosensory system. (v) Hierarchy as a social consequence of electric interactions. The above analysis indicated that active as well as passive electrolocation may contribute in a non-reciprocal manner to social interactions. Gymnotus omarorum then was tested in intra- and intersexual dyads in small plain arenas. A sex-independent dominant-subordinate status emerged after highly aggressive contests. Subordinates signaled their submission by retreating and emitting specific (submissive) electric signals. The emergence of a dominant-subordinate status was also observed in a larger arena after longer but milder contests with rare electric signaling of submission with a unique consequence: the persistence of dominance over time with no outcome reversion

A discrete dipole approximation approach to underwater active electrosense problems

Weakly electric fish use self-established electric field to sense the underwater environment that may be cluttered and turbid. Previous works on building artificial counterparts are limited to simplest cases, as no analytical solutions exist under complex boundary conditions. Universal numerical approaches like Finite Element Method (FEM) and Boundary Element Method (BEM) suffer from lengthy meshing process and heavily computational burden. In this paper, discrete dipole approximation (DDA), which is widely used in light scattering and absorption problems, was for the first time proposed to be applied for underwater electrosense. This approach is lightweight, flexible and computationally efficient compared with FEM. It was simulated in electric fields excited by parallelplate electrodes and spherical electrodes of a simplified robotic model. A constrained unscented Kalman filter (CUKF) was further utilized to localize the position and identify the size of an invading cube. Results comparison with FEM indicate the differences of a cuboidal object in two orthogonal positions were 7.10% and 10.46% respectively, and the difference in size was 11.82%. These results were achieved at a cost of less than 1% of the computational effort of the FEM. The proposed approach proved effective from the simulation results and laid a solid foundation for real-time underwater active electrosense in a more general environment