000135340 001__ 135340
000135340 005__ 20240522124406.0
000135340 0247_ $$2doi$$a10.1186/s40323-024-00266-2
000135340 0248_ $$2sideral$$a138612
000135340 037__ $$aART-2024-138612
000135340 041__ $$aeng
000135340 100__ $$aGhnatios, Chady
000135340 245__ $$aOptimal trajectory planning combining model-based and data-driven hybrid approaches
000135340 260__ $$c2024
000135340 5060_ $$aAccess copy available to the general public$$fUnrestricted
000135340 5203_ $$aTrajectory planning aims at computing an optimal trajectory through the minimization of a cost function. This paper considers four different scenarios: (i) the first concerns a given trajectory on which a cost function is minimized by a acting on the velocity along it; (ii) the second considers trajectories expressed parametrically, from which an optimal path and the velocity along it are computed; (iii), the case in which only the departure and arrival points of the trajectory are known, and the optimal path must be determined; and finally, (iv) the case involving uncertainty in the environment in which the trajectory operates. When the considered cost functions are expressed analytically, the application of Euler–Lagrange equations constitutes an appealing option. However, in many applications, complex cost functions are learned by using black-box machine learning techniques, for instance deep neural networks. In such cases, a neural approach of the trajectory planning becomes an appealing alternative. Different numerical experiments will serve to illustrate the potential of the proposed methodologies on some selected use cases.
000135340 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T24-20R$$9info:eu-repo/grantAgreement/EC/H2020/956401/EU/Cross-scale concurrent material-structure design using functionally-graded 3D-printed matematerials/XS-Meta$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 956401-XS-Meta$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033$$9info:eu-repo/grantAgreement/ES/MCIN/AEI/10.13039/501100011033
000135340 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000135340 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135340 700__ $$aDi Lorenzo, Daniele
000135340 700__ $$aChampaney, Victor
000135340 700__ $$aAmmar, Amine
000135340 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, Elias$$uUniversidad de Zaragoza
000135340 700__ $$aChinesta, Francisco
000135340 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000135340 773__ $$g11, 1 (2024), 19 pp.$$pAdv. model. simul. eng. sci.$$tAdvanced modeling and simulation in engineering sciences$$x2213-7467
000135340 8564_ $$s1680206$$uhttps://zaguan.unizar.es/record/135340/files/texto_completo.pdf$$yVersión publicada
000135340 8564_ $$s2263327$$uhttps://zaguan.unizar.es/record/135340/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135340 909CO $$ooai:zaguan.unizar.es:135340$$particulos$$pdriver
000135340 951__ $$a2024-05-22-10:17:30
000135340 980__ $$aARTICLE