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

doi:10.1145/2554850.2554867

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 proceeding › Conference 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 language	English
Title of host publication	Proceedings of the ACM Symposium on Applied Computing
Publisher	Association for Computing Machinery
Pages	661-666
Number of pages	6
ISBN (Print)	978-1-4503-2469-4
DOIs	https://doi.org/10.1145/2554850.2554867
Publication status	Published - 2014
Event	29th Annual ACM Symposium on Applied Computing - Gyeongju, Korea, Republic of Duration: 24 Mar 2014 → 28 Mar 2014

Conference

Conference	29th Annual ACM Symposium on Applied Computing
Abbreviated title	SAC 2014
Country/Territory	Korea, Republic of
City	Gyeongju
Period	24/03/14 → 28/03/14

ASJC Scopus subject areas

Software

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10.1145/2554850.2554867

Cite this

@inproceedings{e2412e88da3749359c1ea3195695b40f,

title = "Self-localisation in indoor environments combining learning and evolution with wireless networks",

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.",

author = "Gustavo Pessin and Os{\'o}rio, {Fernando S.} and J{\'o} Ueyama and Wolf, {Denis F.} and Moioli, {Renan C.} and Vargas, {Patr{\'i}cia A.}",

year = "2014",

doi = "10.1145/2554850.2554867",

language = "English",

isbn = "978-1-4503-2469-4",

pages = "661--666",

booktitle = "Proceedings of the ACM Symposium on Applied Computing",

publisher = "Association for Computing Machinery",

address = "United States",

note = "29th Annual ACM Symposium on Applied Computing, SAC 2014 ; Conference date: 24-03-2014 Through 28-03-2014",

}

Pessin, G, Osório, FS, Ueyama, J, Wolf, DF, Moioli, RC & Vargas, PA 2014, Self-localisation in indoor environments combining learning and evolution with wireless networks. in Proceedings of the ACM Symposium on Applied Computing. Association for Computing Machinery, pp. 661-666, 29th Annual ACM Symposium on Applied Computing, Gyeongju, Korea, Republic of, 24/03/14. https://doi.org/10.1145/2554850.2554867

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T1 - Self-localisation in indoor environments combining learning and evolution with wireless networks

AU - Pessin, Gustavo

AU - Osório, Fernando S.

AU - Ueyama, Jó

AU - Wolf, Denis F.

AU - Moioli, Renan C.

AU - Vargas, Patrícia A.

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

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U2 - 10.1145/2554850.2554867

DO - 10.1145/2554850.2554867

M3 - Conference contribution

AN - SCOPUS:84905641033

SN - 978-1-4503-2469-4

SP - 661

EP - 666

BT - Proceedings of the ACM Symposium on Applied Computing

PB - Association for Computing Machinery

T2 - 29th Annual ACM Symposium on Applied Computing

Y2 - 24 March 2014 through 28 March 2014

ER -

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

Abstract

Conference

ASJC Scopus subject areas

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