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

| Peer-Reviewed

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	H. Lasi, P. Fettke, H. G. Kemper, T. Feld, and M. Hoffmann, (2014). Industry 4.0. Business & information systems engineering, 6 (4): 239-242.
[2]	A. C. Pereira and F. Romero, (2017). A review of the meanings and the implications of the Industry 4.0 concept. Procedia Manufacturing, 13: 1206-1214.
[3]	Y. Wiseman, (2021). Autonomous vehicles. In Encyclopedia of Information Science and Technology, Fifth Edition, IGI Global, pp. 1-11.
[4]	H. Andreasson, A. Bouguerra, M. Cirillo, D. N. Dimitrov, D. Driankov, L. Karlsson, A. J. Lilienthal, F. Pecora, J. P. Saarinen, A. Sherikov, and T. Stoyanov, (2015). Autonomous Transport Vehicles: Where We Are and What Is Missing. IEEE Robotics & Automation Magazine, 22 (1): 64-75.
[5]	H. Khayyam, B. Javadi, M. Jalili, & R. N. Jazar, (2020). Artificial Intelligence and Internet of Things for Autonomous Vehicles. In Nonlinear Approaches in Engineering Applications, Springer, Cham, pp. 39-68.
[6]	R. S. Peres, X. Jia, J. Lee, K. Sun, A. W. Colombo, and J. Barata, (2020). Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE Access, 8, 220121-220139.
[7]	C. Medrano-Berumen and M. I. Akbaş, (2020). Validation of decision-making in artificial intelligence-based autonomous vehicles. Journal of Information and Telecommunication, DOI: 10.1080/24751839.2020.1824154.
[8]	R. Bostelman and E. Messina, (2016). Towards development of an automated guided vehicle intelligence level performance standard. In Autonomous Industrial Vehicles: From the Laboratory to the Factory Floor, ed. R. Bostelman and E. Messina (West Conshohocken, PA: ASTM International 2016), pp. 1-22. https://doi.org/10.1520/STP159420150054
[9]	Z. Tao, P. Bonnifait, V. Fremont, J. Ibanez-Guzman, (2013). Lane marking aided vehicle localization. 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).
[10]	J. H. Oh, D. Kim, and B. H. Lee, (2014). An Indoor Localization System for Mobile Robots Using an Active Infrared Positioning Sensor. Journal of Industrial and Intelligent Information Vol. 2, No. 1, March 2014.
[11]	S. B. Cruz, T. E. Abrudan, Z. Xiao, N. Trigoni, and J. Barros, (2017). Neighbor-Aided Localization in Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems, Vol. 18, No. 10, October 2017.
[12]	A. Debski, W. Grajewski, W. Zaborowski, W. Turek, (2015). Open-source Localization Device for Indoor Mobile Robots. Procedia Computer Science, Volume 76, 2015, Pages 139-146.
[13]	I. J. Cox, (1990). Blanche: Position estimation for an autonomous root vehicle. In Autonomous robot vehicles, Springer, New York, NY, pp. 221-228.
[14]	F. Bounini, D. Gingras, V. Lapointe, and D. Gruyer, (2014). Real-time simulator of collaborative autonomous vehicles. In 2014 Int. Conf. on Advances in Computing, Communications and Informatics (ICACCI), pp. 723-729.
[15]	B. Y. Ekren and S. Heragu, (2011). Simulation based performance analysis of an autonomous vehicle storage and retrieval system. Simulation Modelling Practice and Theory. 19 (7): 1640-1650.
[16]	S. D. Pendleton, H. Andersen, X. Du, X. Shen, M. Meghjani, Y. H. Eng, and M. H. Ang, (2017). Perception, planning, control, and coordination for autonomous vehicles. Machines, 5 (1): 6, https://doi.org/10.3390/machines5010006.
[17]	M. B. Alatise and G. P. Hancke, (2020). A review on challenges of autonomous mobile robot sensor fusion methods. IEEE Access, 8: 39830-39846.
[18]	N. A. K. Zghair, and A. S. Al-Araji, (2021). A one-decade survey of autonomous mobile robot systems. International Journal of Electrical and Computer Engineering, 11 (6): 4891.
[19]	F. Rubio, F. Valero, and C. Lopis-Albert, (2019). A review of Mobile Robots: Concepts, Methods, Theoretical Framework, and Applications. International Journal of Advanced Robotic Systems, 16 (2): 1-22.
[20]	H. Zhu, K. V. Yuen, L. Mihaylova, and H. Leung, (2017). Overview of environment perception for intelligent vehicles. IEEE Transactions on Intelligent Transportation Systems, 18 (10): 2584-2601.
[21]	F. Rosique, P. J. Navarro, C. Fernández, and A. Padilla, (2019). A systematic review of perception system and simulators for autonomous vehicles research. Sensors, 19 (3): 648, https://doi.org/10.3390/s19030648.
[22]	F. Arena and G. Pau, (2019). An overview of vehicular communications. Future Internet, 11 (2): 27, https://doi.org/10.3390/fi11020027.
[23]	M. Y. Abualhoul, O. Shagdar, and F. Nashashibi, (2016). Visible Light inter-vehicle Communication for platooning of autonomous vehicles. In 2016 IEEE Intelligent Vehicles Symposium (IV), pp. 508-513.
[24]	O. Grembek, A. Kurzhanskiy, A. Medury, P. Varaiya, and M. Yu, (2019). Making intersections safer with I2V communication. Transportation Research Part C: Emerging Technologies, 102: 396-410.
[25]	W. Huang, K. Wang, Y. Lv, and F. Zhu, (2016). Autonomous vehicles testing methods review, in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 163-168.
[26]	M. O’Kelly, A. Sinha, H. Namkoong, R. Tedrake, J. C. Duchi, (2018). Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. In Advances in Neural Information Processing Systems 31 (NeurIPS 2018), arXiv preprint arXiv: 1811.00145.
[27]	D. Kade, M. Wallmyr, T. Holstein, R. Lindell, H. Ürey, and O. Özcan, (2016). Low-Cost Mixed Reality Simulator for Industrial Vehicle Environment. In International Conference on Virtual, Augmented and Mixed Reality, Springer, Cham, pp. 597-608.
[28]	S. Liu, L. Li, J. Tang, S. Wu, and J. L. Gaudiot, (2020). Creating autonomous vehicle systems. Morgan & Claypool Publishers.
[29]	P. Jing, H. Hu, F. Zhan, Y. Chen, and Y. Shi, (2020). Agent-based simulation of autonomous vehicles: A systematic literature review. IEEE Access, 8: 79089-79103.
[30]	K. Dresner and P. Stone, (2008). A multiagent approach to autonomous intersection management. Journal of artificial intelligence research, 31: 591-656.
[31]	A.-J. Fougeres, (2013). A Modelling Approach Based on Fuzzy Agent. International Journal of Computer Science Issues, 9 (6): 19-28.
[32]	A.-J. Fougeres and E. Ostrosi, (2013). Fuzzy agent-based approach for consensual design synthesis in product configuration. Integrated Computer-Aided Engineering, 20 (3): 259-274.
[33]	C. Aynau, C. Bernay-Angeletti, R. Aufrere, L. Lequievre, C. Debain, and R. Chapuis, (2017). Real-time multisensor vehicle localization: A geographical information system based approach. IEEE Robotics & Automation Magazine, 24 (3): 65-74.
[34]	M. de Ryck, M. Versteyhe, F. Debrouwere, (2020). Automated guided vehicle systems, state-of-theart control algorithms and techniques. Journal of Manufacturing Systems, 54: 152-173.
[35]	H. S. Hasan, M. Hussein, S. M. Saad, and M. A. M. Dzahir, (2018). An overview of local positioning system: Technologies, techniques and applications. International Journal of Engineering & Technology, 7 (3.25): 1-5.
[36]	E. Gamma, R. Helm, R. Johnson, and J. Vlissides (1994) Design Patterns – Catalogue des modèles réutilisables, Book, p 125, 357.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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  -

Copy | Download

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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  -

Copy | Download