Predicting Conflict Zones on Terrestrial Routes of Automated Guided Vehicles with Fuzzy Querying on Apache Kafka

Bozena Malysiak-Mrozek; Mario Bas; Vaidy Sunderam; Stanislaw Kozielski; Dariusz Mrozek

doi:10.1109/DSAA60987.2023.10302592

Predicting Conflict Zones on Terrestrial Routes of Automated Guided Vehicles with Fuzzy Querying on Apache Kafka

Bozena Malysiak-Mrozek
, Mario Bas
, Vaidy Sunderam
, Stanislaw Kozielski
, Dariusz Mrozek

Department of Applied Informatics (RAU/RAU7)

Silesian University of Technology
Emory University

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

1 Citation (Scopus)

Abstract

In today's world, smart factories are a coexisting element of smarticizing cities. Smart manufacturing of today relies on the automation of many component tasks of the production process. Automated guided vehicles (AGVs) that transport materials on the production lines are important elements of this automation. Appropriate management of a fleet of AGVs requires avoiding collisions. However, prediction and early detection of approaching collision points on the transportation routes not only prevent collisions but also enables adjusting the AGV operation and improving its flow. In this paper, we demonstrate the use of fuzzy sets and linguistic variables in collision prevention by processing AGV data streams with Apache Kafka. We extend the capabilities of Apache Kafka and ksqlDB towards fuzzy stream processing and use fuzzy KSQL queries to predict collisions. Our experiments prove that fuzzy querying against AGV data streams does not consume much time and computational resources, and we can successfully avoid collisions by predicting future positions of the AGV for various densities of data streams and widths of time windows.

Original language	English
Title of host publication	2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings
Editors	Yannis Manolopoulos, Zhi-Hua Zhou
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350345032
DOIs	https://doi.org/10.1109/DSAA60987.2023.10302592
Publication status	Published - 2023
Event	10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023 - Thessaloniki, Greece Duration: 9 Oct 2023 → 12 Oct 2023

Publication series

Name	2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings

Conference

Conference	10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023
Country/Territory	Greece
City	Thessaloniki
Period	9/10/23 → 12/10/23

UN SDGs

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

SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure

Keywords

Apache Kafka
Industry 4.0
automated guided vehicles
collision prediction
data stream
fuzzy sets

ASJC Scopus subject areas

Information Systems and Management
Statistics, Probability and Uncertainty
Artificial Intelligence
Computer Science Applications
Information Systems

Access to Document

10.1109/DSAA60987.2023.10302592

Cite this

Malysiak-Mrozek, B., Bas, M., Sunderam, V., Kozielski, S., & Mrozek, D. (2023). Predicting Conflict Zones on Terrestrial Routes of Automated Guided Vehicles with Fuzzy Querying on Apache Kafka. In Y. Manolopoulos, & Z.-H. Zhou (Eds.), 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings (2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSAA60987.2023.10302592

Malysiak-Mrozek, Bozena ; Bas, Mario ; Sunderam, Vaidy et al. / Predicting Conflict Zones on Terrestrial Routes of Automated Guided Vehicles with Fuzzy Querying on Apache Kafka. 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings. editor / Yannis Manolopoulos ; Zhi-Hua Zhou. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings).

@inproceedings{7416946f7171498683938307dc7a3c24,

title = "Predicting Conflict Zones on Terrestrial Routes of Automated Guided Vehicles with Fuzzy Querying on Apache Kafka",

abstract = "In today's world, smart factories are a coexisting element of smarticizing cities. Smart manufacturing of today relies on the automation of many component tasks of the production process. Automated guided vehicles (AGVs) that transport materials on the production lines are important elements of this automation. Appropriate management of a fleet of AGVs requires avoiding collisions. However, prediction and early detection of approaching collision points on the transportation routes not only prevent collisions but also enables adjusting the AGV operation and improving its flow. In this paper, we demonstrate the use of fuzzy sets and linguistic variables in collision prevention by processing AGV data streams with Apache Kafka. We extend the capabilities of Apache Kafka and ksqlDB towards fuzzy stream processing and use fuzzy KSQL queries to predict collisions. Our experiments prove that fuzzy querying against AGV data streams does not consume much time and computational resources, and we can successfully avoid collisions by predicting future positions of the AGV for various densities of data streams and widths of time windows.",

keywords = "Apache Kafka, Industry 4.0, automated guided vehicles, collision prediction, data stream, fuzzy sets",

author = "Bozena Malysiak-Mrozek and Mario Bas and Vaidy Sunderam and Stanislaw Kozielski and Dariusz Mrozek",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023 ; Conference date: 09-10-2023 Through 12-10-2023",

year = "2023",

doi = "10.1109/DSAA60987.2023.10302592",

language = "English",

series = "2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Yannis Manolopoulos and Zhi-Hua Zhou",

booktitle = "2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings",

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}

Malysiak-Mrozek, B, Bas, M, Sunderam, V, Kozielski, S & Mrozek, D 2023, Predicting Conflict Zones on Terrestrial Routes of Automated Guided Vehicles with Fuzzy Querying on Apache Kafka. in Y Manolopoulos & Z-H Zhou (eds), 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings. 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023, Thessaloniki, Greece, 9/10/23. https://doi.org/10.1109/DSAA60987.2023.10302592

Predicting Conflict Zones on Terrestrial Routes of Automated Guided Vehicles with Fuzzy Querying on Apache Kafka. / Malysiak-Mrozek, Bozena; Bas, Mario; Sunderam, Vaidy et al.
2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings. ed. / Yannis Manolopoulos; Zhi-Hua Zhou. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings).

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

TY - GEN

T1 - Predicting Conflict Zones on Terrestrial Routes of Automated Guided Vehicles with Fuzzy Querying on Apache Kafka

AU - Malysiak-Mrozek, Bozena

AU - Bas, Mario

AU - Sunderam, Vaidy

AU - Kozielski, Stanislaw

AU - Mrozek, Dariusz

PY - 2023

Y1 - 2023

N2 - In today's world, smart factories are a coexisting element of smarticizing cities. Smart manufacturing of today relies on the automation of many component tasks of the production process. Automated guided vehicles (AGVs) that transport materials on the production lines are important elements of this automation. Appropriate management of a fleet of AGVs requires avoiding collisions. However, prediction and early detection of approaching collision points on the transportation routes not only prevent collisions but also enables adjusting the AGV operation and improving its flow. In this paper, we demonstrate the use of fuzzy sets and linguistic variables in collision prevention by processing AGV data streams with Apache Kafka. We extend the capabilities of Apache Kafka and ksqlDB towards fuzzy stream processing and use fuzzy KSQL queries to predict collisions. Our experiments prove that fuzzy querying against AGV data streams does not consume much time and computational resources, and we can successfully avoid collisions by predicting future positions of the AGV for various densities of data streams and widths of time windows.

AB - In today's world, smart factories are a coexisting element of smarticizing cities. Smart manufacturing of today relies on the automation of many component tasks of the production process. Automated guided vehicles (AGVs) that transport materials on the production lines are important elements of this automation. Appropriate management of a fleet of AGVs requires avoiding collisions. However, prediction and early detection of approaching collision points on the transportation routes not only prevent collisions but also enables adjusting the AGV operation and improving its flow. In this paper, we demonstrate the use of fuzzy sets and linguistic variables in collision prevention by processing AGV data streams with Apache Kafka. We extend the capabilities of Apache Kafka and ksqlDB towards fuzzy stream processing and use fuzzy KSQL queries to predict collisions. Our experiments prove that fuzzy querying against AGV data streams does not consume much time and computational resources, and we can successfully avoid collisions by predicting future positions of the AGV for various densities of data streams and widths of time windows.

KW - Apache Kafka

KW - Industry 4.0

KW - automated guided vehicles

KW - collision prediction

KW - data stream

KW - fuzzy sets

UR - https://www.scopus.com/pages/publications/85179000482

U2 - 10.1109/DSAA60987.2023.10302592

DO - 10.1109/DSAA60987.2023.10302592

M3 - Conference contribution

AN - SCOPUS:85179000482

T3 - 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings

BT - 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings

A2 - Manolopoulos, Yannis

A2 - Zhou, Zhi-Hua

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023

Y2 - 9 October 2023 through 12 October 2023

ER -

Malysiak-Mrozek B, Bas M, Sunderam V, Kozielski S, Mrozek D. Predicting Conflict Zones on Terrestrial Routes of Automated Guided Vehicles with Fuzzy Querying on Apache Kafka. In Manolopoulos Y, Zhou ZH, editors, 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings). doi: 10.1109/DSAA60987.2023.10302592

Predicting Conflict Zones on Terrestrial Routes of Automated Guided Vehicles with Fuzzy Querying on Apache Kafka

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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