Self-localisation in indoor environments combining learning and evolution with wireless networks

Gustavo Pessin, Fernando S. Osório, Jó Ueyama, Denis F. Wolf, Renan C. Moioli, Patrícia A. Vargas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

This work combines wireless networks (WLAN) (Wireless LAN IEEE 802.11 b/g) with learning and evolution of artificial neural networks. Our main objective is to propose an architecture for a self-adaptive system, addressing alternative methods to the usage of GPS for self-localisation in autonomous mobile robots either in indoor or outdoor environments. We seek to describe alternatives and evaluation methods for localisation of mobile agents using the strength signal from Access Points (APs). The results show that the proposed method used with autonomous mobile robots does not require the use of special hardware, and hence is low cost, easy to operate, and fully autonomous.

Original languageEnglish
Title of host publicationProceedings of the ACM Symposium on Applied Computing
PublisherAssociation for Computing Machinery
Pages661-666
Number of pages6
ISBN (Print)978-1-4503-2469-4
DOIs
Publication statusPublished - 2014
Event29th Annual ACM Symposium on Applied Computing - Gyeongju, Korea, Republic of
Duration: 24 Mar 201428 Mar 2014

Conference

Conference29th Annual ACM Symposium on Applied Computing
Abbreviated titleSAC 2014
CountryKorea, Republic of
CityGyeongju
Period24/03/1428/03/14

ASJC Scopus subject areas

  • Software

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  • Cite this

    Pessin, G., Osório, F. S., Ueyama, J., Wolf, D. F., Moioli, R. C., & Vargas, P. A. (2014). Self-localisation in indoor environments combining learning and evolution with wireless networks. In Proceedings of the ACM Symposium on Applied Computing (pp. 661-666). Association for Computing Machinery. https://doi.org/10.1145/2554850.2554867