An OPC UA Machine Learning Server for Automated Guided Vehicle

Rafal Cupek; Łukasz Gólczyński; Adam Ziebinski

doi:10.1007/978-3-030-28374-2_19

An OPC UA Machine Learning Server for Automated Guided Vehicle

Rafal Cupek
, Łukasz Gólczyński
, Adam Ziebinski

Department of Distributed Systems and Informatic Devices (RAU/RAU8)

AIUT

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

Preparing training data sets for supervised machine learning is particularly difficult when the input information has a serial nature and the data sources are non-deterministic. This paper discusses a problem related to preparing a data set for the machine learning algorithms that are used for Automated Guided Vehicles (AGV). An OPC UA server that is dedicated for machine learning support was designed in order to comply with the communication standards and information models that are used in the new generation of manufacturing systems. The proposed approach not only utilises communication features of OPC UA technology but also its rich possibilities for information modelling. The OPC UA server that was created converts raw input data into a format that can be easily applied for the machine learning process. The presented solution is dedicated for the AGV that are used for internal logistics in flexible production systems. The authors discuss the different information models that are available for the OPC UA standard and explain the design choices that were made when preparing the server. The presented solution was verified during the development process for a new family of AGV that is being designed and produced by the AIUT Company.

Original language	English
Title of host publication	Computational Collective Intelligence - 11th International Conference, ICCCI 2019, Proceedings
Editors	Ngoc Thanh Nguyen, Richard Chbeir, Ernesto Exposito, Philippe Aniorté, Bogdan Trawinski, Ngoc Thanh Nguyen
Publisher	Springer Verlag
Pages	218-228
Number of pages	11
ISBN (Print)	9783030283735
DOIs	https://doi.org/10.1007/978-3-030-28374-2_19
Publication status	Published - 2019
Event	11th International Conference on Computational Collective Intelligence, ICCCI 2019 - Hendaye, France Duration: 4 Sept 2019 → 6 Sept 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11684 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Computational Collective Intelligence, ICCCI 2019
Country/Territory	France
City	Hendaye
Period	4/09/19 → 6/09/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

AGV
Automated Guided Vehicle
Communication middleware
Machine learning
Machine-to-Machine (M2M) communication
OPC UA (IEC 62541)

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-28374-2_19

Cite this

Cupek, R., Gólczyński, Ł., & Ziebinski, A. (2019). An OPC UA Machine Learning Server for Automated Guided Vehicle. In N. T. Nguyen, R. Chbeir, E. Exposito, P. Aniorté, B. Trawinski, & N. T. Nguyen (Eds.), Computational Collective Intelligence - 11th International Conference, ICCCI 2019, Proceedings (pp. 218-228). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11684 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-28374-2_19

Cupek, Rafal ; Gólczyński, Łukasz ; Ziebinski, Adam. / An OPC UA Machine Learning Server for Automated Guided Vehicle. Computational Collective Intelligence - 11th International Conference, ICCCI 2019, Proceedings. editor / Ngoc Thanh Nguyen ; Richard Chbeir ; Ernesto Exposito ; Philippe Aniorté ; Bogdan Trawinski ; Ngoc Thanh Nguyen. Springer Verlag, 2019. pp. 218-228 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{64a366dcd6dc47eea656e74e9793566b,

title = "An OPC UA Machine Learning Server for Automated Guided Vehicle",

abstract = "Preparing training data sets for supervised machine learning is particularly difficult when the input information has a serial nature and the data sources are non-deterministic. This paper discusses a problem related to preparing a data set for the machine learning algorithms that are used for Automated Guided Vehicles (AGV). An OPC UA server that is dedicated for machine learning support was designed in order to comply with the communication standards and information models that are used in the new generation of manufacturing systems. The proposed approach not only utilises communication features of OPC UA technology but also its rich possibilities for information modelling. The OPC UA server that was created converts raw input data into a format that can be easily applied for the machine learning process. The presented solution is dedicated for the AGV that are used for internal logistics in flexible production systems. The authors discuss the different information models that are available for the OPC UA standard and explain the design choices that were made when preparing the server. The presented solution was verified during the development process for a new family of AGV that is being designed and produced by the AIUT Company.",

keywords = "AGV, Automated Guided Vehicle, Communication middleware, Machine learning, Machine-to-Machine (M2M) communication, OPC UA (IEC 62541)",

author = "Rafal Cupek and {\L}ukasz G{\'o}lczy{\'n}ski and Adam Ziebinski",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 11th International Conference on Computational Collective Intelligence, ICCCI 2019 ; Conference date: 04-09-2019 Through 06-09-2019",

year = "2019",

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pages = "218--228",

editor = "Nguyen, \{Ngoc Thanh\} and Richard Chbeir and Ernesto Exposito and Philippe Aniort{\'e} and Bogdan Trawinski and Nguyen, \{Ngoc Thanh\}",

booktitle = "Computational Collective Intelligence - 11th International Conference, ICCCI 2019, Proceedings",

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Cupek, R, Gólczyński, Ł & Ziebinski, A 2019, An OPC UA Machine Learning Server for Automated Guided Vehicle. in NT Nguyen, R Chbeir, E Exposito, P Aniorté, B Trawinski & NT Nguyen (eds), Computational Collective Intelligence - 11th International Conference, ICCCI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11684 LNAI, Springer Verlag, pp. 218-228, 11th International Conference on Computational Collective Intelligence, ICCCI 2019, Hendaye, France, 4/09/19. https://doi.org/10.1007/978-3-030-28374-2_19

An OPC UA Machine Learning Server for Automated Guided Vehicle. / Cupek, Rafal; Gólczyński, Łukasz; Ziebinski, Adam.
Computational Collective Intelligence - 11th International Conference, ICCCI 2019, Proceedings. ed. / Ngoc Thanh Nguyen; Richard Chbeir; Ernesto Exposito; Philippe Aniorté; Bogdan Trawinski; Ngoc Thanh Nguyen. Springer Verlag, 2019. p. 218-228 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11684 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An OPC UA Machine Learning Server for Automated Guided Vehicle

AU - Cupek, Rafal

AU - Gólczyński, Łukasz

AU - Ziebinski, Adam

PY - 2019

Y1 - 2019

N2 - Preparing training data sets for supervised machine learning is particularly difficult when the input information has a serial nature and the data sources are non-deterministic. This paper discusses a problem related to preparing a data set for the machine learning algorithms that are used for Automated Guided Vehicles (AGV). An OPC UA server that is dedicated for machine learning support was designed in order to comply with the communication standards and information models that are used in the new generation of manufacturing systems. The proposed approach not only utilises communication features of OPC UA technology but also its rich possibilities for information modelling. The OPC UA server that was created converts raw input data into a format that can be easily applied for the machine learning process. The presented solution is dedicated for the AGV that are used for internal logistics in flexible production systems. The authors discuss the different information models that are available for the OPC UA standard and explain the design choices that were made when preparing the server. The presented solution was verified during the development process for a new family of AGV that is being designed and produced by the AIUT Company.

AB - Preparing training data sets for supervised machine learning is particularly difficult when the input information has a serial nature and the data sources are non-deterministic. This paper discusses a problem related to preparing a data set for the machine learning algorithms that are used for Automated Guided Vehicles (AGV). An OPC UA server that is dedicated for machine learning support was designed in order to comply with the communication standards and information models that are used in the new generation of manufacturing systems. The proposed approach not only utilises communication features of OPC UA technology but also its rich possibilities for information modelling. The OPC UA server that was created converts raw input data into a format that can be easily applied for the machine learning process. The presented solution is dedicated for the AGV that are used for internal logistics in flexible production systems. The authors discuss the different information models that are available for the OPC UA standard and explain the design choices that were made when preparing the server. The presented solution was verified during the development process for a new family of AGV that is being designed and produced by the AIUT Company.

KW - AGV

KW - Automated Guided Vehicle

KW - Communication middleware

KW - Machine learning

KW - Machine-to-Machine (M2M) communication

KW - OPC UA (IEC 62541)

UR - https://www.scopus.com/pages/publications/85072867338

U2 - 10.1007/978-3-030-28374-2_19

DO - 10.1007/978-3-030-28374-2_19

M3 - Conference contribution

AN - SCOPUS:85072867338

SN - 9783030283735

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 218

EP - 228

BT - Computational Collective Intelligence - 11th International Conference, ICCCI 2019, Proceedings

A2 - Nguyen, Ngoc Thanh

A2 - Chbeir, Richard

A2 - Exposito, Ernesto

A2 - Aniorté, Philippe

A2 - Trawinski, Bogdan

A2 - Nguyen, Ngoc Thanh

PB - Springer Verlag

T2 - 11th International Conference on Computational Collective Intelligence, ICCCI 2019

Y2 - 4 September 2019 through 6 September 2019

ER -

Cupek R, Gólczyński Ł, Ziebinski A. An OPC UA Machine Learning Server for Automated Guided Vehicle. In Nguyen NT, Chbeir R, Exposito E, Aniorté P, Trawinski B, Nguyen NT, editors, Computational Collective Intelligence - 11th International Conference, ICCCI 2019, Proceedings. Springer Verlag. 2019. p. 218-228. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-28374-2_19

An OPC UA Machine Learning Server for Automated Guided Vehicle

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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