Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances

We use the recent advances in Deep Learning to solve an underwater motion planning problem by making use of optimal control tools—namely, we propose using the Deep Galerkin Method (DGM) to approximate the Hamilton–Jacobi–Bellman PDE that can be used to solve continuous time and state optimal control...

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Autors principals:	Juan Parras, Patricia A. Apellániz, Santiago Zazo
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2021-07-01
Col·lecció:	Sensors
Matèries:	optimal control Hamilton–Jacobi–Bellman equation autonomous underwater vehicle Deep Galerkin Method
Accés en línia:	https://www.mdpi.com/1424-8220/21/15/5011
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