Result: Fuzzy Multi-Agent Simulation for Collective Energy Management of Autonomous Industrial Vehicle Fleets

Title:
Fuzzy Multi-Agent Simulation for Collective Energy Management of Autonomous Industrial Vehicle Fleets
Authors:
Grosset, Juliette, Oukacha, Ouzna, Fougères, Alain-Jérôme, Djoko-Kouam, Moïse, Bonnin, Jean-Marie
Contributors:
Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), ECAM Rennes - Louis de Broglie (ECAM), Institut d'Électronique et des Technologies du numéRique (IETR), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Nantes Université - Ecole Polytechnique de l'Université de Nantes (Nantes Univ - EPUN), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ), Département Systèmes Réseaux, Cybersécurité et Droit du numérique (IMT Atlantique - SRCD), IMT Atlantique (IMT Atlantique), This research was funded by the Brittany region for funding the VIASIC and ALPHA projects, respectively as part of the ARED-2021-2024 call for projects entitled “The economy at the service of industry for intelligent production” and the PME 2022 call for projects entitled “Accelerate time to market of digital technological innovations from SMEs in the Greater West”.
Source:
Algorithms. 17(11):484-484
Publisher Information:
CCSD; MDPI, 2024.
Publication Year:
2024
Collection:
collection:UNIV-RENNES1
collection:CNRS
collection:UNIV-UBS
collection:INSA-RENNES
collection:IRISA
collection:IETR
collection:IRISA_SET
collection:STATS-UR1
collection:CENTRALESUPELEC
collection:UR1-HAL
collection:UR1-MATH-STIC
collection:UR1-UFR-ISTIC
collection:IMTA_SRCD
collection:TEST-UR-CSS
collection:IRISA_IMTA
collection:UNIV-RENNES
collection:IMT-ATLANTIQUE
collection:INSA-GROUPE
collection:INSTITUTS-TELECOM
collection:UR1-MATH-NUM
collection:NANTES-UNIVERSITE
collection:NANTES-UNIV
collection:INSTITUT-MINES-TELECOM
collection:DDRS-TEST-CJ
Subject Terms:
cooperative mobile robots, intelligent system architectures, fuzzy energy management, fuzzy multi-agent simulation, optimization control problem, airport 4.0, [SPI]Engineering Sciences [physics]
Original Identifier:
HAL: hal-04767307
Document Type:
Journal article<br />Journal articles
Language:
English
ISSN:
1999-4893
Relation:
info:eu-repo/semantics/altIdentifier/doi/10.3390/a17110484
DOI:
10.3390/a17110484
Access URL:
https://hal.science/hal-04767307
https://hal.science/hal-04767307v1/document
https://hal.science/hal-04767307v1/file/algorithms-17-00484-v2.pdf
Rights:
info:eu-repo/semantics/OpenAccess
URL: http://creativecommons.org/licenses/by/
Accession Number:
edshal.hal.04767307v1
Database:
HAL

Further Information

This paper presents a multi-agent simulation implemented in Python, using fuzzy logic to explore collective battery recharge management for autonomous industrial vehicles (AIVs) in an airport environment. This approach offers adaptability and resilience through a distributed system, taking into account variations in AIV battery capacity. Simulation scenarios were based on a proposed charging/discharging model for an AIV battery. The results highlight the effectiveness of adaptive fuzzy multi-agent models in optimizing charging strategies, improving operational efficiency, and reducing energy consumption. Dynamic factors such as workload variations and AIV-infrastructure communication are taken into account in the form of heuristics, underlining the importance of flexible and collaborative approaches in autonomous systems. In particular, an infrastructure capable of optimizing charging according to energy tariffs can significantly reduce consumption during peak hours, highlighting the importance of such strategies in dynamic environments. An optimal control model is established to improve the energy consumption of each AIV during its mission. The energy consumption depends on the speed, as demonstrated via numerical simulations using realistic data. The speed profile of each AIV is adjusted according to the various constraints within an airport. Overall, the study highlights the potential of incorporating adaptive fuzzy multi-agent models for AIV energy management to boost efficiency and sustainability in industrial operations.