株式会社極東書店トップ商品一覧Directed Sonar Sensing for Mobile Robot Navigation. Softcover reprint of the original 1st ed. 1992

商品詳細

Directed Sonar Sensing for Mobile Robot Navigation. Softcover reprint of the original 1st ed. 1992

Directed Sonar Sensing for Mobile Robot Navigation. Softcover reprint of the original 1st ed. 1992

・ISBN 978-1-4613-6625-6 paper EUR 149.99

¥40,091.- (税込) (※)価格はご注文時の参考価格となります。
納品価格につきましては書籍の入荷時点で確定となります。
版元の原価改定、外国為替の変動等により異なる場合がございますので、予めご了承下さい。

お気に入り
著者・編者Leonard, John J. / Durrant-Whyte, Hugh F.,
シリーズ (The Springer International Series in Engineering and Computer Science)
出版社 (Springer-Verlag New York Inc., US)
出版年月2012
ページ数183 pp.
言語ENG
ニュース番号<A04-86270>

解説

This monograph is a revised version of the D.Phil. thesis of the first author, submitted in October 1990 to the University of Oxford. This work investigates the problem of mobile robot navigation using sonar. We view model-based navigation as a process of tracking naturally occurring environment features, which we refer to as "targets". Targets that have been predicted from the environment map are tracked to provide that are observed, but not predicted, vehicle position estimates. Targets represent unknown environment features or obstacles, and cause new tracks to be initiated, classified, and ultimately integrated into the map. Chapter 1 presents a brief definition of the problem and a discussion of the basic research issues involved. No attempt is made to survey ex- haustively the mobile robot navigation literature-the reader is strongly encouraged to consult other sources. The recent collection edited by Cox and Wilfong [34] is an excellent starting point, as it contains many of the standard works of the field. Also, we assume familiarity with the Kalman filter. There are many well-known texts on the subject; our notation derives from Bar-Shalom and Fortmann [7]. Chapter 2 provides a detailed sonar sensor model. A good sensor model of our approach to navigation, and is used both for is a crucial component predicting expected observations and classifying unexpected observations.