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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Applied Artificial Intelligence

ISSN

0883-9514

e-ISSN

—

Svazek periodika

28

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

58

Strana od-do

360-417

Kód UT WoS článku

000333952500003

EID výsledku v databázi Scopus

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