Global Robot Localization Under Noise Stress Utilizing EA Methods and Semisemantic Classification of a Known Environment

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00222506" target="_blank" >RIV/68407700:21230/14:00222506 - isvavai.cz</a>

Result on the web

<a href="http://www.tandfonline.com/doi/full/10.1080/08839514.2014.875684" target="_blank" >http://www.tandfonline.com/doi/full/10.1080/08839514.2014.875684</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/08839514.2014.875684" target="_blank" >10.1080/08839514.2014.875684</a>

Result language

angličtina

Original language name

Global Robot Localization Under Noise Stress Utilizing EA Methods and Semisemantic Classification of a Known Environment

Original language description

Global localization algorithms belong to the key research areas in the field of autonomous mobile robotics. The ability to correctly estimate the initial position after activation or to recover the global position if orientation is lost is required fromall modern autonomous systems. This article presents an algorithm for unmanned global navigation in a known environment containing noise and moving objects. Evolutionary algorithms (EA) form an important part of the discussed method. We also present a novel method of semisemantic classification of the environment in which a robot moves. This semisemantic description of the environment allows for a significantly better setup of the working parameters of individual EAs. It also enables to better connect EAs with the basic navigation methodology based on algebraic criteria, in other words, on the minimization of L1-norm. An extensive set of experimental results confirms that the connection of the semantic environment description and the na

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JC - Computer hardware and software

OECD FORD branch

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Project

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Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2014

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Applied Artificial Intelligence

ISSN

0883-9514

e-ISSN

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Volume of the periodical

28

Issue of the periodical within the volume

4

Country of publishing house

GB - UNITED KINGDOM

Number of pages

58

Pages from-to

360-417

UT code for WoS article

000333952500003

EID of the result in the Scopus database

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