dc.creator | García Martín, Javier | es |
dc.creator | Domínguez Frejo, José Ramón | es |
dc.creator | García Rodríguez, Ramón Andrés | es |
dc.creator | Camacho, Eduardo F. | es |
dc.date.accessioned | 2022-02-24T15:28:27Z | |
dc.date.available | 2022-02-24T15:28:27Z | |
dc.date.issued | 2021-11 | |
dc.identifier.citation | García Martín, J., Frejo, J.R., García Rodríguez, R.A. y Camacho, E.F. (2021). Multi-robot task allocation problem with multiple nonlinear criteria using branch and bound and genetic algorithms. Intelligent Service Robotics, 14, 707-727. | |
dc.identifier.issn | 1861-2776 | es |
dc.identifier.uri | https://hdl.handle.net/11441/130210 | |
dc.description.abstract | The paper proposes the formulation of a single-task robot (ST), single-robot task (SR), time-extended assignment (TA), multi-robot task allocation (MRTA) problem with multiple, nonlinear criteria using discrete variables that drastically reduce the computation burden. Obtaining an allocation is addressed by a Branch and Bound (B&B) algorithm in low scale problems and by a genetic algorithm (GA) specifically developed for the proposed formulation in larger scale problems. The GA crossover and mutation strategies design ensure that the descendant allocations of each generation will maintain a certain level of feasibility, reducing greatly the range of possible descendants, and accelerating their convergence to a sub-optimal allocation. The proposed MRTA algorithms are simulated and analyzed in the context of a thermosolar power plant, for which the spatially distributed Direct Normal Irradiance (DNI) is estimated using a heterogeneous fleet composed of both aerial and ground unmanned vehicles. Three optimization criteria are simultaneously considered: distance traveled, time required to complete the task and energetic feasibility. Even though this paper uses a thermosolar power plant as a case study, the proposed algorithms can be applied to any MRTA problem that uses a multi-criteria and nonlinear cost function in an equivalent way. The performance and response of the proposed algorithms are compared for four different scenarios. The results show that the B&B algorithm can find the global optimal solution in a reasonable time for a case with four robots and six tasks. For larger problems, the genetic algorithm approaches the global optimal solution in much less computation time. Moreover, the trade-off between computation time and accuracy can be easily carried out by tuning the parameters of the genetic algorithm according to the available computational power. | es |
dc.description.sponsorship | Unión Europea 789051 | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades IJC2018-035395-I | es |
dc.format | application/pdf | es |
dc.format.extent | 21 p. | es |
dc.language.iso | eng | es |
dc.publisher | Springer Science and Business Media Deutschland GmbH | es |
dc.relation.ispartof | Intelligent Service Robotics, 14, 707-727. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Multi-robot system | es |
dc.subject | Task planning | es |
dc.subject | Multi-robot task allocation (MRTA) | es |
dc.subject | Robotic sensor network | es |
dc.subject | UAV | es |
dc.subject | UGV | es |
dc.subject | Branch and bound | es |
dc.subject | Genetic algorithm | es |
dc.subject | Thermosolar plant | es |
dc.title | Multi-robot task allocation problem with multiple nonlinear criteria using branch and bound and genetic algorithms | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
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 | IJC2018-035395-I | es |
dc.relation.publisherversion | https://link.springer.com/article/10.1007/s11370-021-00393-4 | es |
dc.identifier.doi | 10.1007/s11370-021-00393-4 | es |
dc.contributor.group | Universidad de Sevilla. TEP116: Automática y Robótica Industrial | es |
dc.journaltitle | Intelligent Service Robotics | es |
dc.publication.volumen | 14 | es |
dc.publication.initialPage | 707 | es |
dc.publication.endPage | 727 | es |