Improved Gas Source Localization with a Mobile Robot by Learning Analytical Gas Dispersal Models from Statistical Gas Distribution Maps Using Evolutionary Algorithms [chapter]

Achim J. Lilienthal
Intelligent Systems for Machine Olfaction  
means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Intelligent systems for machine olfaction : tools and methodologies / Evor L. Hines and Mark S. Leeson, editors. p.
more » ... eson, editors. p. cm. Includes bibliographical references and index. ISBN 978-1-61520-915-6 (hardcover) --ISBN 978-1-61520-916-3 (ebook) 1. ABSTRACT The method presented in this chapter computes an estimate of the location of a single gas source from a set of localized gas sensor measurements. The estimation process consists of three steps. First, a statistical model of the time-averaged gas distribution is estimated in the form of a two-dimensional grid map. In order to compute the gas distribution grid map the Kernel DM algorithm is applied, which carries out spatial integration by convolving localized sensor readings and modeling the information content of the point measurements with a Gaussian kernel. The statistical gas distribution grid map averages out the transitory effects of turbulence and converges to a representation of the time-averaged spatial distribution of a target gas. The second step is to learn the parameters of an analytical model of average gas distribution. Learning is achieved by nonlinear least squares fitting of the 250 Improved Gas Source Localization with a Mobile Robot
doi:10.4018/978-1-61520-915-6.ch010 fatcat:6d635da2xnaive4hxbthb7zxn4