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Artículo
Multi-robot task allocation clustering based on game theory
Autor/es | García Martín, Javier
Muros Ponce, Francisco Javier Maestre Torreblanca, José María Camacho, Eduardo F. |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2023-03 |
Fecha de depósito | 2023-01-23 |
Publicado en |
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Resumen | A cooperative game theory framework is proposed to solve multi-robot task allocation (MRTA)
problems. In particular, a cooperative game is built to assess the performance of sets of robots
and tasks so that the Shapley ... A cooperative game theory framework is proposed to solve multi-robot task allocation (MRTA) problems. In particular, a cooperative game is built to assess the performance of sets of robots and tasks so that the Shapley value of the game can be used to compute their average marginal contribution. This fact allows us to partition the initial MRTA problem into a set of smaller and simpler MRTA subproblems, which are formed by ranking and clustering robots and tasks according to their Shapley value. A large-scale simulation case study illustrates the benefits of the proposed scheme, which is assessed using a genetic algorithm (GA) as a baseline method. The results show that the game theoretical approach outperforms GA both in performance and computation time for a range of problem instances |
Agencias financiadoras | Horizonte 2020 Ministerio de Ciencia e Innovación (MICIN). España |
Identificador del proyecto | 789051
10.13039/501100011033 C3PO-R2D2 PID2020-119476RB-I00 |
Cita | García Martín, J., Muros Ponce, F.J., Maestre Torreblanca, J.M. y Camacho, E.F. (2023). Multi-robot task allocation clustering based on game theory. Robotics and Autonomous Systems, 161, 104314. https://doi.org/10.1016/j.robot.2022.104314. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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RAS_2023_Martin_multi-robot_OA.pdf | 1.061Mb | [PDF] | Ver/ | |