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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Detalles Bibliográficos
Autores principales: Parras, Juan, Apellániz, Patricia A., Zazo, Santiago
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347268/
https://www.ncbi.nlm.nih.gov/pubmed/34372249
http://dx.doi.org/10.3390/s21155011

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347268/
https://www.ncbi.nlm.nih.gov/pubmed/34372249
http://dx.doi.org/10.3390/s21155011

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