dc.creator | García Martín, Javier | es |
dc.creator | Muros Ponce, Francisco Javier | es |
dc.creator | Maestre Torreblanca, José María | es |
dc.creator | Camacho, Eduardo F. | es |
dc.date.accessioned | 2023-01-23T13:54:06Z | |
dc.date.available | 2023-01-23T13:54:06Z | |
dc.date.issued | 2023-03 | |
dc.identifier.citation | 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. | |
dc.identifier.issn | 0921-8890 | es |
dc.identifier.uri | https://hdl.handle.net/11441/141759 | |
dc.description | This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). | es |
dc.description.abstract | 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 | es |
dc.format | application/pdf | es |
dc.format.extent | 11 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Robotics and Autonomous Systems, 161, 104314. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Clustering | es |
dc.subject | Cooperative game theory | es |
dc.subject | Multi-robot systems (MRS) | es |
dc.subject | Multi-robot task allocation (MRTA) | es |
dc.subject | Shapley value | es |
dc.subject | Task planning | es |
dc.title | Multi-robot task allocation clustering based on game theory | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.projectID | 789051 | es |
dc.relation.projectID | 10.13039/501100011033 | es |
dc.relation.projectID | C3PO-R2D2 | es |
dc.relation.projectID | PID2020-119476RB-I00 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0921889022002032 | es |
dc.identifier.doi | 10.1016/j.robot.2022.104314 | es |
dc.contributor.group | Universidad de Sevilla. TEP116: Automática y Robótica Industrial | es |
dc.journaltitle | Robotics and Autonomous Systems | es |
dc.publication.volumen | 161 | es |
dc.publication.initialPage | 104314 | es |
dc.contributor.funder | Horizonte 2020 | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |