research-article

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

Authors:

Gustavo Pessin,

Fernando S. Osório,

Renan C. Moioli,

Patrícia A. VargasAuthors Info & Claims

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

Pages 661 - 666

https://doi.org/10.1145/2554850.2554867

Published: 24 March 2014 Publication History

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.

References

[1]

Aldebaran. Aldebaran robotics. Jan, 18, 2013.

[2]

K. S. Chong and L. Kleeman. Accurate odometry and error modelling for a mobile robot. In IEEE Int. Conf. on Robotics and Automation, pages 2783--2788, 1997.

[3]

W. Dargie and C. Poellabauer. Fundamentals of Wireless Sensor Networks: Theory and Practice. Wiley, 2010.

Digital Library

[4]

E. Elnahrawy, X. Li, and R. Martin. The limits of localization using signal strength: a comparative study. In IEEE SECON, 2004.

[5]

F. Franceschini, M. Galetto, D. Maisano, and L. Mastrogiacomo. Mobile spatial coordinate measuring system (mscms) -- introduction to the system. The Int. Journal of Advanced Manufacturing Technology, 47, 2009.

[6]

F. Gustafsson and F. Gunnarsson. Positioning using time-difference of arrival measurements. In IEEE Int. Conf. on Acoustics, Speech, and Signal Proc., 2003.

[7]

S. Hara and D. Anzai. Experimental performance comparison of rssi- and tdoa-based location estimation methods. In Vehicular Technology Conference, 2008. VTC Spring 2008. IEEE, pages 2651--2655, 2008.

[8]

G.-B. Huang. Learning capability and storage capacity of two-hidden-layer feedforward networks. Neural Networks, IEEE Trans. on, 14(2): 274--281, 2003.

Digital Library

[9]

H. Lang, Y. Wang, and C. de Silva. Mobile robot localization and object pose estimation using optical encoder, vision and laser sensors. In IEEE Int. Conf. on Automation and Logistics, pages 617--622, 2008.

[10]

H. Liu, H. Darabi, P. Banerjee, and J. Liu. Survey of wireless indoor positioning techniques and systems. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Trans. on, 37, 2007.

Digital Library

[11]

D. A. Maisano, J. Jamshidi, F. Franceschini, P. Maropoulos, L. Mastrogiacomo, A. Mileham, and G. Owen. Indoor gps: System functionality and initial performance evaluation. International Journal of Manufacturing Research, 3(3): 335--349, 2008.

[12]

S. H. M. Marinello, J. Ueyama, G. Pessin, F. S. Osório, and P. A. Vargas. Determinando posições relativas de nós móveis por meio de bluetooth e aprendizado de máquina. Revista de Exatas e Tecnológicas (RETEC) ISSN 2236-739X, 3: 1--10, 2012.

[13]

A. Martinelli. The odometry error of a mobile robot with a synchronous drive system. Robotics and Automation, IEEE Trans. on, 18, 2002.

[14]

A. Napier, G. Sibley, and P. Newman. Real-time bounded-error pose estimation for road vehicles using vision. In IEEE Conf. on Intel. Transp. Systems, 2010.

[15]

C. Perkins, L. Lei, M. Kuhlman, T. Lee, G. Gateau, S. Bergbreiter, and P. Abshire. Distance sensing for mini-robots: Rssi vs. tdoa. In IEEE ISCAS, 2011.

[16]

G. Pessin, F. S. Osório, J. Ueyama, J. R. Souza, D. F. Wolf, T. Braun, and P. A. Vargas. Evaluating the impact of the number of access points in mobile robots localization using artificial neural networks. In 5th COMSWARE, 2011.

Digital Library

[17]

G. Pessin, F. Osório, J. Souza, F. Costa, J. Ueyama, D. Wolf, T. Braun, and P. Vargas. Evolving an indoor robotic localization system based on wireless networks. In Engineering Applications of Neural Networks, pages 61--70, 2012.

[18]

R. Siegwart and I. R. Nourbakhsh. Introduction to Autonomous Mobile Robots. The MIT Press, 2004.

Digital Library

[19]

E. Teoh, K. Tan, and C. Xiang. Estimating the number of hidden neurons in a feedforward network using the singular value decomposition. Neural Networks, IEEE Trans. on, 17(6), 2006.

Digital Library

[20]

A. Zell, G. Mamier, M. Mache, R. Hübner, S. Herrmann, T. Soyez, M. Schmalzl, T. Sommer, A. Hatzigeorgiou, D. Posselt, T. Schreiner, B. Kett, G. Clemente, and J. Wieland. Stuttgart neural network simulator - user manual, version 4.1 (report no. 6/95). Technical report, Univ. of Stuttgart, 1995.

Cited By

Carvalho EFerreira BFilho GGomes PFreitas GVargas PUeyama JPessin G(2019)Towards a Smart Fault Tolerant Indoor Localization System Through Recurrent Neural Networks2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852007(1-7)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852007
Ferreira BSerejo GFerreira MFerreira DCardoso LYoshidome EArruda HLira WFerreira JCarvalho EPessin GSouza C(2017)Wearable computing for railway environments: proposal and evaluation of a safety solutionIET Intelligent Transport Systems10.1049/iet-its.2016.018711:6(319-325)Online publication date: 9-May-2017
https://doi.org/10.1049/iet-its.2016.0187
Filho GUeyama JFaiçal BGuidoni DVillas L(2015)ResiDIProceedings of the 2015 XXXIII Brazilian Symposium on Computer Networks and Distributed Systems10.1109/SBRC.2015.36(237-245)Online publication date: 18-May-2015
https://dl.acm.org/doi/10.1109/SBRC.2015.36
Show More Cited By

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

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Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

March 2014

1890 pages

ISBN:9781450324694

DOI:10.1145/2554850

Conference Chairs:
Yookun Cho
Seoul National University, Korea
,
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Sangwook Kim
Kyungpook National University, Korea
,
Chih-Cheng Hung
Southern Polytechnic State University
,
Jiman Hong
Soongsil University, South Korea

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2014

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Ministry of Education of Brazil
Fundação de Amparo à Pesquisa do Estado de São Paulo
Conselho Nacional de Desenvolvimento Científico e Tecnológico

Conference

SAC 2014

Sponsor:

SIGAPP

SAC 2014: Symposium on Applied Computing

March 24 - 28, 2014

Gyeongju, Republic of Korea

Acceptance Rates

SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Carvalho EFerreira BFilho GGomes PFreitas GVargas PUeyama JPessin G(2019)Towards a Smart Fault Tolerant Indoor Localization System Through Recurrent Neural Networks2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852007(1-7)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852007
Ferreira BSerejo GFerreira MFerreira DCardoso LYoshidome EArruda HLira WFerreira JCarvalho EPessin GSouza C(2017)Wearable computing for railway environments: proposal and evaluation of a safety solutionIET Intelligent Transport Systems10.1049/iet-its.2016.018711:6(319-325)Online publication date: 9-May-2017
https://doi.org/10.1049/iet-its.2016.0187
Filho GUeyama JFaiçal BGuidoni DVillas L(2015)ResiDIProceedings of the 2015 XXXIII Brazilian Symposium on Computer Networks and Distributed Systems10.1109/SBRC.2015.36(237-245)Online publication date: 18-May-2015
https://dl.acm.org/doi/10.1109/SBRC.2015.36
Filho GUeyama JFaical BPessin GFarias CPazzi RGuidoni DVillas L(2015)An Energy-Aware System for Decision-Making in a Residential Infrastructure Using Wireless Sensors and ActuatorsProceedings of the 2015 IEEE 14th International Symposium on Network Computing and Applications (NCA)10.1109/NCA.2015.30(9-16)Online publication date: 28-Sep-2015
https://dl.acm.org/doi/10.1109/NCA.2015.30
de Carvalho Santos VToledo COsorio F(2015)An exploratory path planning method based on genetic algorithm for autonomous mobile robots2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7256875(62-69)Online publication date: May-2015
https://doi.org/10.1109/CEC.2015.7256875

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