A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

Pouria Sarhadi; Wasif Naeem; Nikolaos Athanasopoulos

doi:10.1016/j.ifacol.2022.10.440

A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

Pouria Sarhadi, Wasif Naeem, Nikolaos Athanasopoulos

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Abstract

Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.

Original language	English
Pages	257-268
Number of pages	12
DOIs	https://doi.org/10.1016/j.ifacol.2022.10.440
Publication status	E-pub ahead of print - 29 Nov 2022
Event	14th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles - Technical University of Denmark, Copenhagen, Denmark Duration: 14 Sept 2022 → 16 Sept 2022

Conference

Conference	14th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles
Country/Territory	Denmark
City	Copenhagen
Period	14/09/22 → 16/09/22

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10.1016/j.ifacol.2022.10.440

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© 2022 The Authors. This is an open access article distributed under the CC-BY-NC-ND License, to view a copy of the license, see: https://creativecommons.org/licenses/by-nc-nd/2.0/
Final published version, 1.56 MBLicence: CC BY-NC-ND

Cite this

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title = "A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning",

abstract = "Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.",

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AU - Naeem, Wasif

AU - Athanasopoulos, Nikolaos

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Y1 - 2022/11/29

N2 - Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.

AB - Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.

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DO - 10.1016/j.ifacol.2022.10.440

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T2 - 14th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles

Y2 - 14 September 2022 through 16 September 2022

ER -

A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

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Conference

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