COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning
Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been trea...
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ftdoajarticles:oai:doaj.org/article:e581ae5d11b54f63bbbc34a295cb818a 2023-05-15T17:47:06+02:00 COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning Eivind Meyer Amalie Heiberg Adil Rasheed Omer San 2020-01-01T00:00:00Z https://doi.org/10.1109/ACCESS.2020.3022600 https://doaj.org/article/e581ae5d11b54f63bbbc34a295cb818a EN eng IEEE https://ieeexplore.ieee.org/document/9187823/ https://doaj.org/toc/2169-3536 2169-3536 doi:10.1109/ACCESS.2020.3022600 https://doaj.org/article/e581ae5d11b54f63bbbc34a295cb818a IEEE Access, Vol 8, Pp 165344-165364 (2020) Deep reinforcement learning autonomous surface vehicle collision avoidance path following machine learning controllers the international regulations for preventing collisions at sea (COLREGs) Electrical engineering. Electronics. Nuclear engineering TK1-9971 article 2020 ftdoajarticles https://doi.org/10.1109/ACCESS.2020.3022600 2022-12-31T10:22:40Z Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been treated as separate problems, and have typically relied on non-linear first-principles models with parameters that can only be determined experimentally. The rise of deep reinforcement learning in recent years suggests an alternative approach: end-to-end learning of the optimal guidance policy from scratch by means of a trial-and-error based approach. In this article, we explore the potential of Proximal Policy Optimization, a deep reinforcement learning algorithm with demonstrated state-of-the-art performance on Continuous Control tasks, when applied to the dual-objective problem of controlling an autonomous surface vehicle in a COLREGs compliant manner such that it follows an a priori known desired path while avoiding collisions with other vessels along the way. Based on high-fidelity elevation and AIS tracking data from the Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's performance in challenging, dynamic real-world scenarios where the ultimate success of the agent rests upon its ability to navigate non-uniform marine terrain while handling challenging, but realistic vessel encounters. Article in Journal/Newspaper Norwegian Sea Directory of Open Access Journals: DOAJ Articles Norwegian Sea IEEE Access 8 165344 165364 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Deep reinforcement learning autonomous surface vehicle collision avoidance path following machine learning controllers the international regulations for preventing collisions at sea (COLREGs) Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
spellingShingle |
Deep reinforcement learning autonomous surface vehicle collision avoidance path following machine learning controllers the international regulations for preventing collisions at sea (COLREGs) Electrical engineering. Electronics. Nuclear engineering TK1-9971 Eivind Meyer Amalie Heiberg Adil Rasheed Omer San COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning |
topic_facet |
Deep reinforcement learning autonomous surface vehicle collision avoidance path following machine learning controllers the international regulations for preventing collisions at sea (COLREGs) Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
description |
Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been treated as separate problems, and have typically relied on non-linear first-principles models with parameters that can only be determined experimentally. The rise of deep reinforcement learning in recent years suggests an alternative approach: end-to-end learning of the optimal guidance policy from scratch by means of a trial-and-error based approach. In this article, we explore the potential of Proximal Policy Optimization, a deep reinforcement learning algorithm with demonstrated state-of-the-art performance on Continuous Control tasks, when applied to the dual-objective problem of controlling an autonomous surface vehicle in a COLREGs compliant manner such that it follows an a priori known desired path while avoiding collisions with other vessels along the way. Based on high-fidelity elevation and AIS tracking data from the Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's performance in challenging, dynamic real-world scenarios where the ultimate success of the agent rests upon its ability to navigate non-uniform marine terrain while handling challenging, but realistic vessel encounters. |
format |
Article in Journal/Newspaper |
author |
Eivind Meyer Amalie Heiberg Adil Rasheed Omer San |
author_facet |
Eivind Meyer Amalie Heiberg Adil Rasheed Omer San |
author_sort |
Eivind Meyer |
title |
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning |
title_short |
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning |
title_full |
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning |
title_fullStr |
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning |
title_full_unstemmed |
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning |
title_sort |
colreg-compliant collision avoidance for unmanned surface vehicle using deep reinforcement learning |
publisher |
IEEE |
publishDate |
2020 |
url |
https://doi.org/10.1109/ACCESS.2020.3022600 https://doaj.org/article/e581ae5d11b54f63bbbc34a295cb818a |
geographic |
Norwegian Sea |
geographic_facet |
Norwegian Sea |
genre |
Norwegian Sea |
genre_facet |
Norwegian Sea |
op_source |
IEEE Access, Vol 8, Pp 165344-165364 (2020) |
op_relation |
https://ieeexplore.ieee.org/document/9187823/ https://doaj.org/toc/2169-3536 2169-3536 doi:10.1109/ACCESS.2020.3022600 https://doaj.org/article/e581ae5d11b54f63bbbc34a295cb818a |
op_doi |
https://doi.org/10.1109/ACCESS.2020.3022600 |
container_title |
IEEE Access |
container_volume |
8 |
container_start_page |
165344 |
op_container_end_page |
165364 |
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1766151418722385920 |