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An end-to-end data-driven optimisation framework for constrained trajectories

Florent Dewez 1 Benjamin Guedj 2, 3, 4, 1, 5 Arthur Talpaert 1 Vincent Vandewalle 6, 1
1 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille, Université de Lille, Sciences et Technologies
Abstract : Many real-world problems require to optimise trajectories under constraints. Classical approaches are based on optimal control methods but require an exact knowledge of the underlying dynamics, which could be challenging or even out of reach. In this paper, we leverage data-driven approaches to design a new end-to-end framework which is dynamics-free for optimised and realistic trajectories. We first decompose the trajectories on function basis, trading the initial infinite dimension problem on a multivariate functional space for a parameter optimisation problem. A maximum \emph{a posteriori} approach which incorporates information from data is used to obtain a new optimisation problem which is regularised. The penalised term focuses the search on a region centered on data and includes estimated linear constraints in the problem. We apply our data-driven approach to two settings in aeronautics and sailing routes optimisation, yielding commanding results. The developed approach has been implemented in the Python library PyRotor.
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Contributor : Benjamin Guedj Connect in order to contact the contributor
Submitted on : Wednesday, November 25, 2020 - 11:52:46 PM
Last modification on : Thursday, January 20, 2022 - 4:15:59 PM


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  • HAL Id : hal-03024720, version 1
  • ARXIV : 2011.11820



Florent Dewez, Benjamin Guedj, Arthur Talpaert, Vincent Vandewalle. An end-to-end data-driven optimisation framework for constrained trajectories. 2020. ⟨hal-03024720⟩



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