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Proceedings Paper

Localization of source with unknown amplitude using IPMC sensor arrays
Author(s): Ahmad T. Abdulsadda; Feitian Zhang; Xiaobo Tan
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Paper Abstract

The lateral line system, consisting of arrays of neuromasts functioning as flow sensors, is an important sensory organ for fish that enables them to detect predators, locate preys, perform rheotaxis, and coordinate schooling. Creating artificial lateral line systems is of significant interest since it will provide a new sensing mechanism for control and coordination of underwater robots and vehicles. In this paper we propose recursive algorithms for localizing a vibrating sphere, also known as a dipole source, based on measurements from an array of flow sensors. A dipole source is frequently used in the study of biological lateral lines, as a surrogate for underwater motion sources such as a flapping fish fin. We first formulate a nonlinear estimation problem based on an analytical model for the dipole-generated flow field. Two algorithms are presented to estimate both the source location and the vibration amplitude, one based on the least squares method and the other based on the Newton-Raphson method. Simulation results show that both methods deliver comparable performance in source localization. A prototype of artificial lateral line system comprising four ionic polymer-metal composite (IPMC) sensors is built, and experimental results are further presented to demonstrate the effectiveness of IPMC lateral line systems and the proposed estimation algorithms.

Paper Details

Date Published: 28 March 2011
PDF: 11 pages
Proc. SPIE 7976, Electroactive Polymer Actuators and Devices (EAPAD) 2011, 797627 (28 March 2011); doi: 10.1117/12.881877
Show Author Affiliations
Ahmad T. Abdulsadda, Michigan State Univ. (United States)
Feitian Zhang, Michigan State Univ. (United States)
Xiaobo Tan, Michigan State Univ. (United States)

Published in SPIE Proceedings Vol. 7976:
Electroactive Polymer Actuators and Devices (EAPAD) 2011
Yoseph Bar-Cohen; Federico Carpi, Editor(s)

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