Long-range and high-precision localization method for underwater bionic positioning system based on joint active-passive electrolocation

Active electrolocation organ of weakly electric fish act as a proximity detection system with high accuracy in recognizing object parameters such as size and shape. In contrast, some fish with passive electrolocation organ are able to detect objects at a greater range. This paper proposes a joint active-passive electrolocation algorithm for long-range and high-precision underwater localization, inspired by the active and passive electroreceptive organs of fish. The study begins by designing a large experimental platform for the underwater localization system to investigate the response of underwater objects to active and passive electric fields. Based on the response, the paper proposes separate underwater active and passive electrolocation algorithms, which are then combined to form a joint algorithm. Experimental results demonstrate that the proposed algorithm achieves high localization accuracy and long detection distance. The joint active-passive electrolocation algorithm has potential applications in submarine resource exploration, underwater robotics, and maritime military projects, while also providing new ideas for future research on long-range underwater object detection and identification based on electrolocation.

Minimal sensor arrays for localizing objects using an electric sense

Weakly electric fish encode perturbations in a self-generated electric field to sense their environment. Localizing objects using this electric sense requires that distance be decoded from a two-dimensional \emph{electric image} of the field perturbations on their skin. Many studies of object localization by weakly electric fish, and by electric sensing in a generic context, have focused on extracting location information from different features of the electric image. Some of these studies have also considered the additional information gained from sampling the electric image at different times, and from different viewpoints. Here, we take a different perspective and instead consider the information available at a single point in space (i.e. a single sensor or receptor) at a single point in time (i.e. constant field). By combining the information from multiple receptors, we show that an object's distance can be unambiguously encoded by as few as four receptors at specific locations on a sensing surface in a manner that is relatively robust to environmental noise. This provides a lower bound on the information (i.e. receptor array size) required to decode the three-dimensional location of an object using an electric sense.

Underwater pre-touch based on artificial electric sense

This article exploits a bio-inspired sensor technology named artificial electric-sense to emulate underwater pre-touch. The sensor is considered as an electric finger controlled remotely by an operator to follow the boundaries of objects. Using electric measurements only, the approach feeds back pre-touch forces and torques to the operator through an haptic interface. These forces and torques are generated by a set of virtual electric charges and dipoles arranged on the probe and reacting in the electric field reflected by the objects. This model of emulated forces is passive and guarantees the stability of a position–position haptic feedback loop. The whole approach is assessed through a set of experiments carried out on a Cartesian slave robot coupled to an haptic interface. The obtained results show the feasibility of the concept and its robustness to different configurations of objects. Such an electro-haptic feedback opens new perspectives in both electric field sensing and underwater robotics.

Representation of object's shape by multiple electric images in electrolocation

Weakly electric fish generate an electric field by discharging an electric organ located on the tail region. An object near the fish modulates the self-generated electric field. The modulated field enables the fish to perceive objects even in complete darkness. The ability to perceive objects is provided by the electrosensory system of the fish. Electroreceptors distributed on the fish's skin surface can sense the modulated field, on the basis of transdermal voltage across the skin surface, called electric images. The fish can extract object's features such as lateral distance, size, shape, and electric property from an electric image. Although previous studies have demonstrated the relationship between electric-image features and object's distance and size, it remains unclear what features of an electric image represent the object's shape. We make here a hypothesis that shape information is not represented by a single image but by multiple images caused by the object's rotation or fish movement around the object. To test the hypothesis, we develop a computational model that can predict electric images produced by the rotation of differently shaped objects. We used five different shapes of resistive objects: a circle, a square, an equilateral triangle, a rectangle, and an ellipsoid. We show that differently shaped objects of a fixed arrangement generate similar Gaussian electric images, irrespective of their shapes. We also show that the features of an electric image such as the peak amplitude, half-maximum width, and peak position exhibit the angle-dependent variations characteristic to object rotation, depending on object shapes and lateral distances. Furthermore, we demonstrate that an integration effect of the peak amplitude and half-maximum width could be an invariant measure of object shape. These results suggest that the fish could perceive an object shape by combining those image features produced during exploratory behaviors around the object.

Application of reduced sensor movement sequences as a precursor for search area partitioning and a selection of discrete EEV contour-ring fragments for active electrolocation

In addition to their visual sense, weakly electric fish use active electrolocation to detect and analyse objects in their nearby environment. Their ability to generate and sense electric fields combined with scanning-like swimming movements are intended to extract further parameters like the size, shape and material properties of objects. Inspired by this biological example, this work introduces an application for active electrolocation based on reduced sensor movement sequences as presented in Wolf-Homeyer et al (2016 Bioinspir. Biomim. 11 055002). Initially, the application is conducted with a simulated receptor-system consisting of an emitter-dipole and an orthogonally arranged pair of sensor-electrodes. Close inspection of a minimal set of scanning movements allows the exclusion of sectors of the general search area early in the proposed localization algorithm (search area partitioning). Furthermore, the proposed algorithm is based on an analytical representation of the electric field and of the so-called EEV (ensemble of electrosensory viewpoints) (Solberg et al 2008 Int. J. Robot. Res. 27 529-48) rather than using computationally expensive FEM simulations, rendering it suitable for embedded computer systems. Two-dimensional discrete EEV contour-ring points (CRPs) of desired accuracy are extracted. In the core of the localization algorithm, fragments of the EEV are selected from valid sectors of the search area, which generates sets of CRPs, one for each sensor-emitter position/orientation. These sets are investigated by means of a nearness metric to find points in different sets which correspond to each other in order to estimate the object position. Two resultant scanning strategies/localization algorithms are introduced.