A Novel Nash-Based Matching Approach for Multirobot Task Allocation in Distributed Robotic Networks

Ali Hamidoğlu; Ömer Melih Gül; Gökhan Koray Gültekin; Seifedine Nimer Kadry

doi:10.1007/978-3-031-64495-5_1

A Novel Nash-Based Matching Approach for Multirobot Task Allocation in Distributed Robotic Networks

Ali Hamidoğlu^*, Ömer Melih Gül, Gökhan Koray Gültekin, Seifedine Nimer Kadry

^*Corresponding author for this work

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

Abstract

Efficiency is paramount in distributed robotic networks, where multiple autonomous robots collaborate to perform complex tasks. In this context, the identification of the most efficient path for robots, considering both distance and cost, plays a crucial role in the development of an effective matching algorithm for addressing multirobot task allocation (MRTA) challenges. This study presents a novel cooperative Nash game framework that serves as a distributed matching method for addressing the task allocation problem in a distributed robotic network consisting of robots and tasks. A particular MRTA problem is investigated where each robot moves at a constant speed determined by maximizing energy harvesting while minimizing energy consumption during the motion. In this framework, Nash equilibrium is established as a near-optimal approach for matching based on distance. In the numerical experiments, the performances are assessed for various scenarios involving 10 robots and 20 robots with the same number of tasks. Here, the Hungarian algorithm is used as an optimal benchmark algorithm to demonstrate the reliability of the theoretical findings and the robustness of the proposed model.

Original language	English
Title of host publication	7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023
Editors	Ömer Melih Gül, Paolo Fiorini, Seifedine Nimer Kadry
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	3-15
Number of pages	13
ISBN (Print)	9783031644948
DOIs	https://doi.org/10.1007/978-3-031-64495-5_1
Publication status	Published - 2024
Externally published	Yes
Event	7th EAI International Conference on Robotics and Networks, ROSENET 2023 - Istanbul, Turkey Duration: 15 Dec 2023 → 16 Dec 2023

Publication series

Name	EAI/Springer Innovations in Communication and Computing
ISSN (Print)	2522-8595
ISSN (Electronic)	2522-8609

Conference

Conference	7th EAI International Conference on Robotics and Networks, ROSENET 2023
Country/Territory	Turkey
City	Istanbul
Period	15/12/23 → 16/12/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Distributed systems
Energy harvesting
Game theory
Multirobot systems
Task allocation

Access to Document

10.1007/978-3-031-64495-5_1

Cite this

Hamidoğlu, A., Gül, Ö. M., Gültekin, G. K., & Kadry, S. N. (2024). A Novel Nash-Based Matching Approach for Multirobot Task Allocation in Distributed Robotic Networks. In Ö. M. Gül, P. Fiorini, & S. N. Kadry (Eds.), 7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023 (pp. 3-15). (EAI/Springer Innovations in Communication and Computing). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-64495-5_1

Hamidoğlu, Ali ; Gül, Ömer Melih ; Gültekin, Gökhan Koray et al. / A Novel Nash-Based Matching Approach for Multirobot Task Allocation in Distributed Robotic Networks. 7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023. editor / Ömer Melih Gül ; Paolo Fiorini ; Seifedine Nimer Kadry. Springer Science and Business Media Deutschland GmbH, 2024. pp. 3-15 (EAI/Springer Innovations in Communication and Computing).

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title = "A Novel Nash-Based Matching Approach for Multirobot Task Allocation in Distributed Robotic Networks",

abstract = "Efficiency is paramount in distributed robotic networks, where multiple autonomous robots collaborate to perform complex tasks. In this context, the identification of the most efficient path for robots, considering both distance and cost, plays a crucial role in the development of an effective matching algorithm for addressing multirobot task allocation (MRTA) challenges. This study presents a novel cooperative Nash game framework that serves as a distributed matching method for addressing the task allocation problem in a distributed robotic network consisting of robots and tasks. A particular MRTA problem is investigated where each robot moves at a constant speed determined by maximizing energy harvesting while minimizing energy consumption during the motion. In this framework, Nash equilibrium is established as a near-optimal approach for matching based on distance. In the numerical experiments, the performances are assessed for various scenarios involving 10 robots and 20 robots with the same number of tasks. Here, the Hungarian algorithm is used as an optimal benchmark algorithm to demonstrate the reliability of the theoretical findings and the robustness of the proposed model.",

keywords = "Distributed systems, Energy harvesting, Game theory, Multirobot systems, Task allocation",

author = "Ali Hamidoğlu and G{\"u}l, {{\"O}mer Melih} and G{\"u}ltekin, {G{\"o}khan Koray} and Kadry, {Seifedine Nimer}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 7th EAI International Conference on Robotics and Networks, ROSENET 2023 ; Conference date: 15-12-2023 Through 16-12-2023",

year = "2024",

doi = "10.1007/978-3-031-64495-5_1",

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series = "EAI/Springer Innovations in Communication and Computing",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "3--15",

editor = "G{\"u}l, {{\"O}mer Melih} and Paolo Fiorini and Kadry, {Seifedine Nimer}",

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Hamidoğlu, A, Gül, ÖM, Gültekin, GK & Kadry, SN 2024, A Novel Nash-Based Matching Approach for Multirobot Task Allocation in Distributed Robotic Networks. in ÖM Gül, P Fiorini & SN Kadry (eds), 7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023. EAI/Springer Innovations in Communication and Computing, Springer Science and Business Media Deutschland GmbH, pp. 3-15, 7th EAI International Conference on Robotics and Networks, ROSENET 2023, Istanbul, Turkey, 15/12/23. https://doi.org/10.1007/978-3-031-64495-5_1

A Novel Nash-Based Matching Approach for Multirobot Task Allocation in Distributed Robotic Networks. / Hamidoğlu, Ali; Gül, Ömer Melih; Gültekin, Gökhan Koray et al.
7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023. ed. / Ömer Melih Gül; Paolo Fiorini; Seifedine Nimer Kadry. Springer Science and Business Media Deutschland GmbH, 2024. p. 3-15 (EAI/Springer Innovations in Communication and Computing).

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

TY - GEN

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AU - Hamidoğlu, Ali

AU - Gül, Ömer Melih

AU - Gültekin, Gökhan Koray

AU - Kadry, Seifedine Nimer

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - Efficiency is paramount in distributed robotic networks, where multiple autonomous robots collaborate to perform complex tasks. In this context, the identification of the most efficient path for robots, considering both distance and cost, plays a crucial role in the development of an effective matching algorithm for addressing multirobot task allocation (MRTA) challenges. This study presents a novel cooperative Nash game framework that serves as a distributed matching method for addressing the task allocation problem in a distributed robotic network consisting of robots and tasks. A particular MRTA problem is investigated where each robot moves at a constant speed determined by maximizing energy harvesting while minimizing energy consumption during the motion. In this framework, Nash equilibrium is established as a near-optimal approach for matching based on distance. In the numerical experiments, the performances are assessed for various scenarios involving 10 robots and 20 robots with the same number of tasks. Here, the Hungarian algorithm is used as an optimal benchmark algorithm to demonstrate the reliability of the theoretical findings and the robustness of the proposed model.

AB - Efficiency is paramount in distributed robotic networks, where multiple autonomous robots collaborate to perform complex tasks. In this context, the identification of the most efficient path for robots, considering both distance and cost, plays a crucial role in the development of an effective matching algorithm for addressing multirobot task allocation (MRTA) challenges. This study presents a novel cooperative Nash game framework that serves as a distributed matching method for addressing the task allocation problem in a distributed robotic network consisting of robots and tasks. A particular MRTA problem is investigated where each robot moves at a constant speed determined by maximizing energy harvesting while minimizing energy consumption during the motion. In this framework, Nash equilibrium is established as a near-optimal approach for matching based on distance. In the numerical experiments, the performances are assessed for various scenarios involving 10 robots and 20 robots with the same number of tasks. Here, the Hungarian algorithm is used as an optimal benchmark algorithm to demonstrate the reliability of the theoretical findings and the robustness of the proposed model.

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M3 - Conference contribution

AN - SCOPUS:85202296347

SN - 9783031644948

T3 - EAI/Springer Innovations in Communication and Computing

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EP - 15

BT - 7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023

A2 - Gül, Ömer Melih

A2 - Fiorini, Paolo

A2 - Kadry, Seifedine Nimer

PB - Springer Science and Business Media Deutschland GmbH

T2 - 7th EAI International Conference on Robotics and Networks, ROSENET 2023

Y2 - 15 December 2023 through 16 December 2023

ER -

Hamidoğlu A, Gül ÖM, Gültekin GK, Kadry SN. A Novel Nash-Based Matching Approach for Multirobot Task Allocation in Distributed Robotic Networks. In Gül ÖM, Fiorini P, Kadry SN, editors, 7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023. Springer Science and Business Media Deutschland GmbH. 2024. p. 3-15. (EAI/Springer Innovations in Communication and Computing). doi: 10.1007/978-3-031-64495-5_1

A Novel Nash-Based Matching Approach for Multirobot Task Allocation in Distributed Robotic Networks

Abstract

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Bibliographical note

Keywords

Access to Document

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