A neural network approach for AGV localization using trilateration

Author

Azenha, Abílio ; Carvalho, Adriano

Author_Institution

Fac. of Eng., Univ. of Porto, Porto, Portugal

fYear

2009

fDate

3-5 Nov. 2009

Firstpage

2699

Lastpage

2702

Abstract

This paper describes radio frequency (RF) localization for indoors quasi-structured environments using an artificial neural network (ANN) approach. Making use of available communications sub-system based on wireless protocols, received signal strength indication (RSSI) can become a measurement that sources ANN schemes which emulate trilateration for indoors RF localization. Some ANN learning results are presented which give encouraging insights for going on applying ANN approach to automated guided vehicles (AGVs) indoors localization.

Keywords

automatic guided vehicles; learning (artificial intelligence); neural nets; protocols; signal sampling; AGV localization; ANN learning; automated guided vehicles; neural network approach; quasi-structured environments; radio frequency localization; received signal strength indication; trilateration; wireless protocols; Artificial neural networks; Automatic control; Communication system control; Equations; Hardware; Neural networks; RF signals; Radio frequency; Vehicles; Wireless application protocol;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics, 2009. IECON '09. 35th Annual Conference of IEEE

Conference_Location

Porto

ISSN

1553-572X

Print_ISBN

978-1-4244-4648-3

Electronic_ISBN

1553-572X

Type

conf

DOI

10.1109/IECON.2009.5415429

Filename

5415429