Towards a Bijective Co-simulation Model Between Physical and Virtual Environments, Adapted to a Platform for Autonomous Industrial Vehicles

Mo&iuml;se Djoko-Kouam; Alain-J&eacute;r&ocirc;me Foug&egrave;res

doi:doi:10.11648/j.acis.20231102.12

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Towards a Bijective Co-simulation Model Between Physical and Virtual Environments, Adapted to a Platform for Autonomous Industrial Vehicles

Moïse Djoko-Kouam, Alain-Jérôme Fougères

Published in Automation, Control and Intelligent Systems (Volume 11, Issue 2)

Received: 31 May 2023 Accepted: 29 June 2023 Published: 11 July 2023

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Abstract

One of the major challenges faced by Industry 4.0 is the use of Automated Guided Vehicles (AGVs) and, more broadly, autonomous mobile robots. While autonomy in road transportation vehicles can already be well characterized, it is a different story for autonomous vehicles used in industries, such as Autonomous Industrial Vehicles (AIVs). The implementation and deployment of AIV fleets in industrial sectors encounter various issues, including vehicle localization, employee acceptance, traffic flow, and the ability of vehicles to adapt to fluctuating and dynamic environments. The challenge that autonomous vehicles represent for the future of the digital industry is so significant that it makes sense from our vision to go through a step of joint physical and digital simulation of these vehicles and their environments. This step would assist industrials in their respective development and fine-tuning activities. The objective of this article is to demonstrate that all the elements available to us at present (research conducted in our laboratory, technological building blocks, operating scenarios already carried out, state of the art) logically allow us to project ourselves towards a Co-Simulation platform based on a bijective model between physical and virtual environments. Furthermore, this projection is enriched by the idea of a digital component of the platform, capable of taking into account in an agnostic way, the different possible forms of the physical part of this platform. Thus, simulation enables the consideration of constraints and requirements formulated by manufacturers and future users of autonomous vehicles. Our approach is progressive, as presented in this article, and it is based on experiments with Co-Simulation platforms that combine physical and virtual approaches. We provide a detailed description of our AIVs and their traffic environments. The static architecture of the platform is described using class diagrams. The dynamic behavior of the platform is described, thanks to sequence diagrams, state diagrams, and algorithmic flowcharts. We also propose an approach to estimate the position of AIVs based on the combination of matrix-based tagging with a section change management technique. As a result of the presented approach, all of these diagrams make it possible to document different operating scenarios of the Co-Simulation platform. Beyond these results, and to complement them before concluding, we describe several application cases related to both algorithmic position estimation metrology and electric battery characterization. This serves to illustrate the potential value of our Co-Simulation platform model.

Published in	Automation, Control and Intelligent Systems (Volume 11, Issue 2)
DOI	10.11648/j.acis.20231102.12
Page(s)	27-44
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Autonomous Industrial Vehicle (AIV), Matrix Beaconing, Co-simulation Platform, AIVs Position Estimation, Agent-Based Simulation, Fuzzy Agent

References

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Moïse Djoko-Kouam, Alain-Jérôme Fougères. (2023). Towards a Bijective Co-simulation Model Between Physical and Virtual Environments, Adapted to a Platform for Autonomous Industrial Vehicles. Automation, Control and Intelligent Systems, 11(2), 27-44. https://doi.org/10.11648/j.acis.20231102.12

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ACS Style

Moïse Djoko-Kouam; Alain-Jérôme Fougères. Towards a Bijective Co-simulation Model Between Physical and Virtual Environments, Adapted to a Platform for Autonomous Industrial Vehicles. Autom. Control Intell. Syst. 2023, 11(2), 27-44. doi: 10.11648/j.acis.20231102.12

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AMA Style

Moïse Djoko-Kouam, Alain-Jérôme Fougères. Towards a Bijective Co-simulation Model Between Physical and Virtual Environments, Adapted to a Platform for Autonomous Industrial Vehicles. Autom Control Intell Syst. 2023;11(2):27-44. doi: 10.11648/j.acis.20231102.12

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@article{10.11648/j.acis.20231102.12,
  author = {Moïse Djoko-Kouam and Alain-Jérôme Fougères},
  title = {Towards a Bijective Co-simulation Model Between Physical and Virtual Environments, Adapted to a Platform for Autonomous Industrial Vehicles},
  journal = {Automation, Control and Intelligent Systems},
  volume = {11},
  number = {2},
  pages = {27-44},
  doi = {10.11648/j.acis.20231102.12},
  url = {https://doi.org/10.11648/j.acis.20231102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20231102.12},
  abstract = {One of the major challenges faced by Industry 4.0 is the use of Automated Guided Vehicles (AGVs) and, more broadly, autonomous mobile robots. While autonomy in road transportation vehicles can already be well characterized, it is a different story for autonomous vehicles used in industries, such as Autonomous Industrial Vehicles (AIVs). The implementation and deployment of AIV fleets in industrial sectors encounter various issues, including vehicle localization, employee acceptance, traffic flow, and the ability of vehicles to adapt to fluctuating and dynamic environments. The challenge that autonomous vehicles represent for the future of the digital industry is so significant that it makes sense from our vision to go through a step of joint physical and digital simulation of these vehicles and their environments. This step would assist industrials in their respective development and fine-tuning activities. The objective of this article is to demonstrate that all the elements available to us at present (research conducted in our laboratory, technological building blocks, operating scenarios already carried out, state of the art) logically allow us to project ourselves towards a Co-Simulation platform based on a bijective model between physical and virtual environments. Furthermore, this projection is enriched by the idea of a digital component of the platform, capable of taking into account in an agnostic way, the different possible forms of the physical part of this platform. Thus, simulation enables the consideration of constraints and requirements formulated by manufacturers and future users of autonomous vehicles. Our approach is progressive, as presented in this article, and it is based on experiments with Co-Simulation platforms that combine physical and virtual approaches. We provide a detailed description of our AIVs and their traffic environments. The static architecture of the platform is described using class diagrams. The dynamic behavior of the platform is described, thanks to sequence diagrams, state diagrams, and algorithmic flowcharts. We also propose an approach to estimate the position of AIVs based on the combination of matrix-based tagging with a section change management technique. As a result of the presented approach, all of these diagrams make it possible to document different operating scenarios of the Co-Simulation platform. Beyond these results, and to complement them before concluding, we describe several application cases related to both algorithmic position estimation metrology and electric battery characterization. This serves to illustrate the potential value of our Co-Simulation platform model.},
 year = {2023}
}

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TY  - JOUR
T1  - Towards a Bijective Co-simulation Model Between Physical and Virtual Environments, Adapted to a Platform for Autonomous Industrial Vehicles
AU  - Moïse Djoko-Kouam
AU  - Alain-Jérôme Fougères
Y1  - 2023/07/11
PY  - 2023
N1  - https://doi.org/10.11648/j.acis.20231102.12
DO  - 10.11648/j.acis.20231102.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 27
EP  - 44
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20231102.12
AB  - One of the major challenges faced by Industry 4.0 is the use of Automated Guided Vehicles (AGVs) and, more broadly, autonomous mobile robots. While autonomy in road transportation vehicles can already be well characterized, it is a different story for autonomous vehicles used in industries, such as Autonomous Industrial Vehicles (AIVs). The implementation and deployment of AIV fleets in industrial sectors encounter various issues, including vehicle localization, employee acceptance, traffic flow, and the ability of vehicles to adapt to fluctuating and dynamic environments. The challenge that autonomous vehicles represent for the future of the digital industry is so significant that it makes sense from our vision to go through a step of joint physical and digital simulation of these vehicles and their environments. This step would assist industrials in their respective development and fine-tuning activities. The objective of this article is to demonstrate that all the elements available to us at present (research conducted in our laboratory, technological building blocks, operating scenarios already carried out, state of the art) logically allow us to project ourselves towards a Co-Simulation platform based on a bijective model between physical and virtual environments. Furthermore, this projection is enriched by the idea of a digital component of the platform, capable of taking into account in an agnostic way, the different possible forms of the physical part of this platform. Thus, simulation enables the consideration of constraints and requirements formulated by manufacturers and future users of autonomous vehicles. Our approach is progressive, as presented in this article, and it is based on experiments with Co-Simulation platforms that combine physical and virtual approaches. We provide a detailed description of our AIVs and their traffic environments. The static architecture of the platform is described using class diagrams. The dynamic behavior of the platform is described, thanks to sequence diagrams, state diagrams, and algorithmic flowcharts. We also propose an approach to estimate the position of AIVs based on the combination of matrix-based tagging with a section change management technique. As a result of the presented approach, all of these diagrams make it possible to document different operating scenarios of the Co-Simulation platform. Beyond these results, and to complement them before concluding, we describe several application cases related to both algorithmic position estimation metrology and electric battery characterization. This serves to illustrate the potential value of our Co-Simulation platform model.
VL  - 11
IS  - 2
ER  -

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Author Information

Moïse Djoko-Kouam

IT and Telecommunications Department, ECAM Rennes Louis de Broglie, Bruz, France
Alain-Jérôme Fougères

IT and Telecommunications Department, ECAM Rennes Louis de Broglie, Bruz, France

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APA Style

Moïse Djoko-Kouam, Alain-Jérôme Fougères. (2023). Towards a Bijective Co-simulation Model Between Physical and Virtual Environments, Adapted to a Platform for Autonomous Industrial Vehicles. Automation, Control and Intelligent Systems, 11(2), 27-44. https://doi.org/10.11648/j.acis.20231102.12

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ACS Style

Moïse Djoko-Kouam; Alain-Jérôme Fougères. Towards a Bijective Co-simulation Model Between Physical and Virtual Environments, Adapted to a Platform for Autonomous Industrial Vehicles. Autom. Control Intell. Syst. 2023, 11(2), 27-44. doi: 10.11648/j.acis.20231102.12

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AMA Style

Moïse Djoko-Kouam, Alain-Jérôme Fougères. Towards a Bijective Co-simulation Model Between Physical and Virtual Environments, Adapted to a Platform for Autonomous Industrial Vehicles. Autom Control Intell Syst. 2023;11(2):27-44. doi: 10.11648/j.acis.20231102.12

Copy | Download

@article{10.11648/j.acis.20231102.12,
  author = {Moïse Djoko-Kouam and Alain-Jérôme Fougères},
  title = {Towards a Bijective Co-simulation Model Between Physical and Virtual Environments, Adapted to a Platform for Autonomous Industrial Vehicles},
  journal = {Automation, Control and Intelligent Systems},
  volume = {11},
  number = {2},
  pages = {27-44},
  doi = {10.11648/j.acis.20231102.12},
  url = {https://doi.org/10.11648/j.acis.20231102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20231102.12},
  abstract = {One of the major challenges faced by Industry 4.0 is the use of Automated Guided Vehicles (AGVs) and, more broadly, autonomous mobile robots. While autonomy in road transportation vehicles can already be well characterized, it is a different story for autonomous vehicles used in industries, such as Autonomous Industrial Vehicles (AIVs). The implementation and deployment of AIV fleets in industrial sectors encounter various issues, including vehicle localization, employee acceptance, traffic flow, and the ability of vehicles to adapt to fluctuating and dynamic environments. The challenge that autonomous vehicles represent for the future of the digital industry is so significant that it makes sense from our vision to go through a step of joint physical and digital simulation of these vehicles and their environments. This step would assist industrials in their respective development and fine-tuning activities. The objective of this article is to demonstrate that all the elements available to us at present (research conducted in our laboratory, technological building blocks, operating scenarios already carried out, state of the art) logically allow us to project ourselves towards a Co-Simulation platform based on a bijective model between physical and virtual environments. Furthermore, this projection is enriched by the idea of a digital component of the platform, capable of taking into account in an agnostic way, the different possible forms of the physical part of this platform. Thus, simulation enables the consideration of constraints and requirements formulated by manufacturers and future users of autonomous vehicles. Our approach is progressive, as presented in this article, and it is based on experiments with Co-Simulation platforms that combine physical and virtual approaches. We provide a detailed description of our AIVs and their traffic environments. The static architecture of the platform is described using class diagrams. The dynamic behavior of the platform is described, thanks to sequence diagrams, state diagrams, and algorithmic flowcharts. We also propose an approach to estimate the position of AIVs based on the combination of matrix-based tagging with a section change management technique. As a result of the presented approach, all of these diagrams make it possible to document different operating scenarios of the Co-Simulation platform. Beyond these results, and to complement them before concluding, we describe several application cases related to both algorithmic position estimation metrology and electric battery characterization. This serves to illustrate the potential value of our Co-Simulation platform model.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Towards a Bijective Co-simulation Model Between Physical and Virtual Environments, Adapted to a Platform for Autonomous Industrial Vehicles
AU  - Moïse Djoko-Kouam
AU  - Alain-Jérôme Fougères
Y1  - 2023/07/11
PY  - 2023
N1  - https://doi.org/10.11648/j.acis.20231102.12
DO  - 10.11648/j.acis.20231102.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 27
EP  - 44
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20231102.12
AB  - One of the major challenges faced by Industry 4.0 is the use of Automated Guided Vehicles (AGVs) and, more broadly, autonomous mobile robots. While autonomy in road transportation vehicles can already be well characterized, it is a different story for autonomous vehicles used in industries, such as Autonomous Industrial Vehicles (AIVs). The implementation and deployment of AIV fleets in industrial sectors encounter various issues, including vehicle localization, employee acceptance, traffic flow, and the ability of vehicles to adapt to fluctuating and dynamic environments. The challenge that autonomous vehicles represent for the future of the digital industry is so significant that it makes sense from our vision to go through a step of joint physical and digital simulation of these vehicles and their environments. This step would assist industrials in their respective development and fine-tuning activities. The objective of this article is to demonstrate that all the elements available to us at present (research conducted in our laboratory, technological building blocks, operating scenarios already carried out, state of the art) logically allow us to project ourselves towards a Co-Simulation platform based on a bijective model between physical and virtual environments. Furthermore, this projection is enriched by the idea of a digital component of the platform, capable of taking into account in an agnostic way, the different possible forms of the physical part of this platform. Thus, simulation enables the consideration of constraints and requirements formulated by manufacturers and future users of autonomous vehicles. Our approach is progressive, as presented in this article, and it is based on experiments with Co-Simulation platforms that combine physical and virtual approaches. We provide a detailed description of our AIVs and their traffic environments. The static architecture of the platform is described using class diagrams. The dynamic behavior of the platform is described, thanks to sequence diagrams, state diagrams, and algorithmic flowcharts. We also propose an approach to estimate the position of AIVs based on the combination of matrix-based tagging with a section change management technique. As a result of the presented approach, all of these diagrams make it possible to document different operating scenarios of the Co-Simulation platform. Beyond these results, and to complement them before concluding, we describe several application cases related to both algorithmic position estimation metrology and electric battery characterization. This serves to illustrate the potential value of our Co-Simulation platform model.
VL  - 11
IS  - 2
ER  -

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