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

TL;DR

This paper introduces a data-driven, end-to-end optimization framework for constrained trajectories that does not require explicit knowledge of dynamics, demonstrated in aeronautics and sailing route applications.

Contribution

It presents a novel dynamics-free, data-driven trajectory optimization method using a basis decomposition and a regularized maximum a posteriori approach.

Findings

Effective in aeronautics trajectory optimization

Successfully applied to sailing routes

Implemented in the PyRotor Python library

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.