Reinforcement Learning-Based Tracking Control of USVs in Varying Operational Conditions

We present a reinforcement learning-based (RL) control scheme for trajectory tracking of fully-actuated surface vessels. The proposed method learns online both a model-based feedforward controller, as well an optimizing feedback policy in order to follow a desired trajectory under the influence of e...

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Detalles Bibliográficos
Autores principales: Martinsen, Andreas B., Lekkas, Anastasios M., Gros, Sébastien, Glomsrud, Jon Arne, Pedersen, Tom Arne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Robotics and AI
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806118/
https://www.ncbi.nlm.nih.gov/pubmed/33501200
http://dx.doi.org/10.3389/frobt.2020.00032

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806118/
https://www.ncbi.nlm.nih.gov/pubmed/33501200
http://dx.doi.org/10.3389/frobt.2020.00032

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