135340 20260219090930.0 doi 10.1186/s40323-024-00266-2 sideral 138612 ART-2024-138612 eng Ghnatios, Chady Optimal trajectory planning combining model-based and data-driven hybrid approaches 2024 Access copy available to the general public Unrestricted Trajectory 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. info:eu-repo/grantAgreement/ES/DGA-FSE/T24-20R info:eu-repo/grantAgreement/EC/H2020/956401/EU/Cross-scale concurrent material-structure design using functionally-graded 3D-printed matematerials/XS-Meta This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 956401-XS-Meta info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033 info:eu-repo/grantAgreement/ES/MCIN/AEI/10.13039/501100011033 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 3.2 2024 0.771 2024 Engineering (miscellaneous) 2024 Q1 Modeling and Simulation 2024 Q1 Applied Mathematics 2024 Q2 Computer Science Applications 2024 Q2 4.6 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Di Lorenzo, Daniele Champaney, Victor Ammar, Amine Cueto, Elias Universidad de Zaragoza (orcid)0000-0003-1017-4381 Chinesta, Francisco 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 11, 1 (2024), 19 pp. Adv. model. simul. eng. sci. Advanced modeling and simulation in engineering sciences 2213-7467 1680206 http://zaguan.unizar.es/record/135340/files/texto_completo.pdf Versión publicada 2263327 http://zaguan.unizar.es/record/135340/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:135340 articulos driver 2026-02-19-09:00:10 ARTICLE