dc.creator | Pérez-Cutiño, Miguel Ángel | es |
dc.creator | Rodríguez, Fabio | es |
dc.creator | Pascual Callejo, Luis David | es |
dc.creator | Díaz Báñez, José Miguel | es |
dc.date.accessioned | 2022-09-08T15:03:05Z | |
dc.date.available | 2022-09-08T15:03:05Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Pérez-Cutiño, M.Á., Rodríguez, F., Pascual, L.D. y Díaz-Báñez, J.M. (2022). Ornithopter Trajectory Optimization with Neural Networks and Random Forest. Journal of Intelligent and Robotic Systems: Theory and Applications, 105 (1), Art. number 17. | |
dc.identifier.issn | 1573-0409 | es |
dc.identifier.issn | 0921-0296 | es |
dc.identifier.uri | https://hdl.handle.net/11441/136903 | |
dc.description.abstract | Trajectory optimization has recently been addressed to compute energy-efficient routes for ornithopter navigation, but its online application remains a challenge. To overcome the high computation time of traditional approaches, this paper proposes algorithms that recursively generate trajectories based on the output of neural networks and random forest. To this end, we create a large data set composed by energy-efficient trajectories obtained by running a competitive planner. To the best of our knowledge our proposed data set is the first one with a high number of pseudo-optimal paths for ornithopter trajectory optimization. We compare the performance of three methods to compute low-cost trajectories: two classification approaches to learn maneuvers and an alternative regression method that predicts new states. The algorithms are tested in several scenarios, including the landing case. The effectiveness and efficiency of the proposed algorithms are demonstrated through simulation, which show that the machine learning techniques can be used to compute the flight path of the ornithopter in real time, even under uncertainties such as wrong sensor readings or re-positioning of the target. Random Forest obtains the higher performance with more than 99% and 97% of accuracy in a landing and a mid-range scenario, respectively. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad MTM2016-76272- R AEI/FEDER,UE | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación PID2020-114154RB-I00 | es |
dc.description.sponsorship | Unión Europea, Horizon 2020, Marie Sklodowska-Curie grant agreement #734922 | es |
dc.format | application/pdf | es |
dc.format.extent | 16 p. | es |
dc.language.iso | eng | es |
dc.publisher | Springer Science and Business Media B.V. | es |
dc.relation.ispartof | Journal of Intelligent and Robotic Systems: Theory and Applications, 105 (1), Art. number 17. | |
dc.relation.isreferencedby | Dataset and evaluation code: https://github.com/mpcutino/OTO_dataset | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Trajectory optimization | es |
dc.subject | Neural networks | es |
dc.subject | Random forest | es |
dc.subject | Ornithopter | es |
dc.subject | Dataset | es |
dc.title | Ornithopter Trajectory Optimization with Neural Networks and Random Forest | 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 Matemática Aplicada II (ETSI) | es |
dc.relation.projectID | MTM2016-76272- R | es |
dc.relation.projectID | PID2020-114154RB-I00 | es |
dc.relation.projectID | #734922 | es |
dc.relation.publisherversion | https://link.springer.com/article/10.1007/s10846-022-01612-5? | es |
dc.identifier.doi | 10.1007/s10846-022-01612-5 | es |
dc.contributor.group | Universidad de Sevilla. FQM241: Grupo de Investigación en Localización | es |
dc.journaltitle | Journal of Intelligent and Robotic Systems: Theory and Applications | es |
dc.publication.volumen | 105 | es |
dc.publication.issue | 1 | es |
dc.publication.initialPage | Art. number 17 | es |