TY - GEN
T1 - A Novel Nash-Based Matching Approach for Multirobot Task Allocation in Distributed Robotic Networks
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.
KW - Distributed systems
KW - Energy harvesting
KW - Game theory
KW - Multirobot systems
KW - Task allocation
UR - https://www.scopus.com/pages/publications/85202296347
U2 - 10.1007/978-3-031-64495-5_1
DO - 10.1007/978-3-031-64495-5_1
M3 - Conference contribution
AN - SCOPUS:85202296347
SN - 9783031644948
T3 - EAI/Springer Innovations in Communication and Computing
SP - 3
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 -