Publication
Title
Autonomous port navigation with ranging sensors using model-based reinforcement learning
Author
Abstract
Autonomous shipping has recently gained much interest in the research community. However, little research focuses on inland - and port navigation, even though this is identified by countries such as Belgium and the Netherlands as an essential step towards a sustainable future. These environments pose unique challenges, since they can contain dynamic obstacles that do not broadcast their location, such as small vessels, kayaks or buoys. Therefore, this research proposes a navigational algorithm which can navigate an inland vessel in a wide variety of complex port scenarios using ranging sensors to observe the environment. The proposed methodology is based on a machine learning approach that has recently set benchmark results in various domains: model-based reinforcement learning. By randomizing the port environments during training, the trained model can navigate in scenarios that it never encountered during training. Furthermore, results show that our approach outperforms the commonly used dynamic window approach and a benchmark model-free reinforcement learning algorithm. This work is therefore a significant step towards vessels that can navigate autonomously in complex port scenarios.
Language
English
Source (book)
42nd International Conference on Ocean, Offshore & Arctic Engineering, 11-16 June, 2023, Melbourne, Australia
Publication
New York, N.Y. : The American Society of Mechanical Engineer , 2023
ISBN
978-0-7918-8687-8
DOI
10.1115/OMAE2023-104455
Volume/pages
p. 1-10
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identifier
Creation 24.11.2023
Last edited 25.11.2023
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