As a flow-sensing organ, the lateral line system plays an important role in various behaviors of fish. An engineering equivalent of a biological lateral line is of great interest to the navigation and control of underwater robots and vehicles. A vibrating sphere, also known as a dipole source, can emulate the rhythmic movement of fins and body appendages, and has been widely used as a stimulus in the study of biological lateral lines. Dipole source localization has also become a benchmark problem in the development of artificial lateral lines. In this paper we present two novel iterative schemes, referred to as Gauss-Newton (GN) and Newton-Raphson (NR) algorithms, for simultaneously localizing a dipole source and estimating its vibration amplitude and orientation, based on the analytical model for a dipole-generated flow field. The performance of the GN and NR methods is first confirmed with simulation results and the Cramer-Rao bound (CRB) analysis. Experiments are further conducted on an artificial lateral line prototype, consisting of six millimeter-scale ionic polymer-metal composite sensors with intra-sensor spacing optimized with CRB analysis. Consistent with simulation results, the experimental results show that both GN and NR schemes are able to simultaneously estimate the source location, vibration amplitude and orientation with comparable precision. Specifically, the maximum localization error is less than 5% of the body length (BL) when the source is within the distance of one BL. Experimental results have also shown that the proposed schemes are superior to the beamforming method, one of the most competitive approaches reported in literature, in terms of accuracy and computational efficiency.
Most fish and aquatic amphibians use the lateral line system, consisting of arrays of hair-like neuromasts, as an important sensory organ for prey/predator detection, communication, and navigation. In this paper a novel bio-inspired artificial lateral line system is proposed for underwater robots and vehicles by exploiting the inherent sensing capability of ionic polymer-metal composites (IPMCs). Analogous to its biological counterpart, the IPMC-based lateral line processes the sensor signals through a neural network. The effectiveness of the proposed lateral line is validated experimentally in the localization of a dipole source (vibrating sphere) underwater. In particular, as a proof of concept, a prototype with body length (BL) of 10 cm, comprising six millimeter-scale IPMC sensors, is constructed and tested. Experimental results have shown that the IPMC-based lateral line can localize the source from 1-2 BLs away, with a maximum localization error of 0.3 cm, when the data for training the neural network are collected from a grid of 2 cm by 2 cm lattices. The effect of the number of sensors on the localization accuracy has also been examined.
Fish and aquatic amphibians use the lateral line system, consisting of arrays of hair-like neuromasts, as an important sensory organ for prey/predator detection, communication, and navigation. In this paper a novel bio-inspired artificial lateral line system is proposed for underwater robots and vehicles by exploiting the inherent sensing capability of ionic polymer-metal composites (IPMCs). Analogous to its biological counterpart, the IPMC-based lateral line processes the sensor signals through a neural network. The effectiveness of the proposed lateral line was validated in localization of underwater motion sources, including both a vibrating sphere (a dipole source) and a flapping foil. In particular, as a proof of concept, a prototype with Body Length (BL) of 8 cm, comprising five millimeter-scale IPMC sensors, was constructed and tested. Experimental results showed that the IPMC-based lateral line could localize the sources from 4-5 BLs away, with a localization error comparable to source placement resolution at the sourcesensor separation of 1 BL. In addition to the ease of fabrication, these results established the competitiveness of the proposed approach, in terms of both localization range and accuracy, against the state of the art in artificial lateral lines.
Motivated by the lateral line system of fish, arrays of flow sensors have been proposed as a new sensing modality for underwater robots. Existing studies on such artificial lateral lines (ALLs) have been mostly focused on the localization of a fixed underwater vibrating sphere (dipole source). In this paper we examine the problem of tracking a moving dipole source using an ALL system. Based on an analytical model for the moving dipole-generated flow field, we formulate a nonlinear estimation problem that aims to minimize the error between the measured and model-predicted magnitudes of flow velocities at the sensor sites, which is subsequently solved with the Gauss-Newton scheme. A sliding discrete Fourier transform (SDFT) algorithm is proposed to efficiently compute the evolving signal magnitudes based on the flow velocity measurements. Simulation indicates that it is adequate and more computationally efficient to use only the signal magnitudes corresponding to the dipole vibration frequency. Finally, experiments conducted with an artificial lateral line consisting of six ionic polymer-metal composite (IPMC) flow sensors demonstrate that the proposed scheme is able to simultaneously locate the moving dipole and estimate its vibration amplitude and traveling speed with small errors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.