Towards Learning Ocean Models for Long-term Navigation in Dynamic Environments

Padrao, Paulo; Domínguez, A.; Bobadilla, Leonardo; Smith, Ryan N.

doi:10.1109/oceanschennai45887.2022.9775460

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2023

Publication Types

Select...

Article2

Other2

Relationship

Self Cite1

Independent3

Authors

Journals

Cited by 4 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper is an expansion of our preliminary work in Padrao et al (2022) and extends it to include the estimation of ocean features using partial differential equations. The rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Estimating spatio-temporal fields through reinforcement learning

et al. 2022

Self Cite

View full text Add to dashboard Cite

Prediction and estimation of phenomena of interest in aquatic environments are challenging since they present complex spatio-temporal dynamics. Over the past few decades, advances in machine learning and data processing contributed to ocean exploration and sampling using autonomous robots. In this work, we formulate a reinforcement learning framework to estimate spatio-temporal fields modeled by partial differential equations. The proposed framework addresses problems of the classic methods regarding the sampling process to determine the path to be used by the agent to collect samples. Simulation results demonstrate the applicability of our approach and show that the error at the end of the learning process is close to the expected error given by the fitting process due to added noise.

show abstract

Section: Introductionmentioning

confidence: 99%