Open Access

Flow Vision for Autonomous Underwater Vehicles via an Artificial Lateral Line

EURASIP Journal on Advances in Signal Processing20102011:806406

https://doi.org/10.1155/2011/806406

Received: 15 June 2010

Accepted: 23 November 2010

Published: 8 December 2010

Abstract

Most fish have the capability of sensing flows and nearby movements even in dark or murky conditions by using the lateral line organs. This enables them to perform a variety of underwater activities, such as localizing prey, avoiding predators, navigating in narrow spaces, and schooling. To emulate this capability for Autonomous Underwater Vehicles, we developed an artificial lateral line using an array of Micro-Electro-Mechanical-Systems (MEMS) flow sensors. The signals collected via the artificial lateral line are then processed by an adaptive beamforming algorithm developed from Capon's method. The system produces 3D images of source locations for different hydrodynamic activities, including the vibration of a dipole source and the movement of a tail-flicking crayfish. A self-calibration algorithm provides the capability of self-adaptation to different environments. Lastly, we give a Cramer-Rao bound on the theoretical performance limit which is consistent with experimental results.

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Authors’ Affiliations

(1)
Department of Electrical and Computer Engineering, University of Illinois at Urbana Champaign
(2)
Department of Engineering, University of Texas at Brownsville
(3)
Department of Mechanical Engineering, Northwestern University

Copyright

© Nam Nguyen et al. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.