NDLI: Underwater source localization using an IPMC-based artificial lateral line

Content Provider	IEEE Xplore Digital Library
Author	Abdulsadda, A.T. Xiaobo Tan
Copyright Year	2011
Description	Author affiliation: Smart Microsystems Laboratory, Department of Electrical and Computer Engineering, Michigan State University, East Lansing, 48824, USA (Abdulsadda, A.T.; Xiaobo Tan)
Abstract	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 source-sensor 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.
Starting Page	2719
Ending Page	2724
File Size	255547
Page Count	6
File Format	PDF
ISBN	9781612843865
ISSN	10504729
e-ISBN	9781612843858
DOI	10.1109/ICRA.2011.5980545
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot sensing systems Training Biology Fabrication Sensor arrays
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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