Evolutionary Trajectory Planner for Multiple UAVs in Realistic Scenarios

Besada Portas, Eva; Torre Cubillo, Luis de la; de la Cruz, Jesús M.; de Andrés-Toro, Bonifacio

Publicación:
Evolutionary Trajectory Planner for Multiple UAVs in Realistic Scenarios

dc.contributor.author	Besada Portas, Eva
dc.contributor.author	Torre Cubillo, Luis de la
dc.contributor.author	de la Cruz, Jesús M.
dc.contributor.author	de Andrés-Toro, Bonifacio
dc.date.accessioned	2025-07-02T11:57:00Z
dc.date.available	2025-07-02T11:57:00Z
dc.date.issued	2010-05-24
dc.description	Este es el manuscrito aceptado del artículo. La versión registrada fue publicada por primera vez en in IEEE Transactions on Robotics, vol. 26, no. 4, pp. 619-634, Aug. 2010, está disponible en línea en el sitio web del editor: https://doi.org/10.1109/TRO.2010.2048610. This is the accepted manuscript of the article. The registered version was first published in IEEE Transactions on Robotics, vol. 26, no. 4, pp. 619–634, Aug. 2010, and is available online from the publisher's website: https://doi.org/10.1109/TRO.2010.2048610.
dc.description.abstract	This paper presents a path planner for multiple unmanned aerial vehicles (UAVs) based on evolutionary algorithms (EAs) for realistic scenarios. The paths returned by the algorithm fulfill and optimize multiple criteria that 1) are calculated based on the properties of real UAVs, terrains, radars, and missiles and 2) are structured in different levels of priority according to the selected mission. The paths of all the UAVs are obtained with the multiple coordinated agents coevolution EA (MCACEA), which is a general framework that uses an EA per agent (i.e., UAV) that share their optimal solutions to coordinate the evolutions of the EAs populations using cooperation objectives. This planner works offline and online by means of recalculating parts of the original path to avoid unexpected risks while the UAV is flying. Its search space and computation time have been reduced using some special operators in the EAs. The successful results of the paths obtained in multiple scenarios, which are statistically analyzed in the paper, and tested against a simulator that incorporates complex models of the UAVs, radars, and missiles, make us believe that this planner could be used for real-flight missions.	en
dc.description.version	versión final
dc.identifier.citation	E. Besada-Portas, L. de la Torre, J. M. de la Cruz and B. de Andrés-Toro, ""Evolutionary Trajectory Planner for Multiple UAVs in Realistic Scenarios,"" in IEEE Transactions on Robotics, vol. 26, no. 4, pp. 619-634, Aug. 2010, doi: https://doi.org/10.1109/TRO.2010.2048610
dc.identifier.doi	https://doi.org/10.1109/TRO.2010.2048610
dc.identifier.issn	1552-3098; e- ISSN: 1941-0468
dc.identifier.uri	https://hdl.handle.net/20.500.14468/26977
dc.journal.issue	4
dc.journal.title	IEEE Transactions on Robotics
dc.journal.volume	26
dc.language.iso	en
dc.page.final	634
dc.page.initial	619
dc.publisher	IEEE Xplore
dc.relation.center	E.T.S. de Ingeniería Informática
dc.relation.department	Informática y Automática
dc.rights	info:eu-repo/semantics/openAccess
dc.rights.uri	Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.subject	12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywords	Unmanned aerial vehicles	en
dc.subject.keywords	Evolutionary computation	en
dc.subject.keywords	Mobile robots	en
dc.subject.keywords	Radar	en
dc.subject.keywords	Missiles	en
dc.subject.keywords	Path planning	en
dc.subject.keywords	Remotely operated vehicles	en
dc.subject.keywords	Constraint optimization	en
dc.subject.keywords	Testing	en
dc.subject.keywords	Computational modeling	en
dc.title	Evolutionary Trajectory Planner for Multiple UAVs in Realistic Scenarios	en
dc.type	artículo	es
dc.type	journal article	en
dspace.entity.type	Publication
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