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

School of Electronics, Electrical Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

107 Downloads (Pure)

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
Title of host publication	Proceedings of the 14th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, CAMS 2022
Publisher	International Federation of Automatic Control
Pages	257-268
DOIs	https://doi.org/10.1016/j.ifacol.2022.10.440
Publication status	Published - 29 Nov 2022
Event	IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles - Duration: 14 Sept 2022 → 16 Sept 2022

Publication series

Name	IFAC-PapersOnLine
Number	31
Volume	55
ISSN (Print)	2405-8971
ISSN (Electronic)	2405-8963

Conference

Conference	IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles
Period	14/09/2022 → 16/09/2022

Access to Document

10.1016/j.ifacol.2022.10.440Licence: CC BY-NC-ND

A survey of recent machine learning solutions for ship collision avoidance and mission planning
Copyright 2022 the authors. This is an open access article published under a Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited.
Final published version, 1.56 MBLicence: CC BY-NC-ND

1 Active

R1476ECS: Decarbonisation of Maritime Transportation - a Return to Commercial Sailing - Linked to R1475MEE
Malyuskin, O. (PI) & Naeem, W. (CoI)
01/12/2020 → …
Project: Research

10 Citations
1 Conference contribution

An Integrated Risk Assessment and Collision Avoidance Methodology for an Autonomous Catamaran with Fuzzy Weighting Functions
Sarhadi, P., Naeem, W. & Athanasopoulos, N., 27 May 2022, 13th UKACC International Conference on Control. Institute of Electrical and Electronics Engineers Inc., p. 228-234 7 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Open Access
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6 Citations (Scopus)

77 Downloads (Pure)

Cite this

Sarhadi, P., Naeem, W., & Athanasopoulos, N. (2022). A survey of recent machine learning solutions for ship collision avoidance and mission planning. In Proceedings of the 14th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, CAMS 2022 (pp. 257-268). (IFAC-PapersOnLine; Vol. 55, No. 31). International Federation of Automatic Control. https://doi.org/10.1016/j.ifacol.2022.10.440

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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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Sarhadi, P, Naeem, W & Athanasopoulos, N 2022, A survey of recent machine learning solutions for ship collision avoidance and mission planning. in Proceedings of the 14th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, CAMS 2022. IFAC-PapersOnLine, no. 31, vol. 55, International Federation of Automatic Control, pp. 257-268, IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles, 14/09/2022. https://doi.org/10.1016/j.ifacol.2022.10.440

A survey of recent machine learning solutions for ship collision avoidance and mission planning. / Sarhadi, Pouria; Naeem, Wasif ; Athanasopoulos, Nikolaos.
Proceedings of the 14th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, CAMS 2022. International Federation of Automatic Control, 2022. p. 257-268 (IFAC-PapersOnLine; Vol. 55, No. 31).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Sarhadi P, Naeem W , Athanasopoulos N. A survey of recent machine learning solutions for ship collision avoidance and mission planning. In Proceedings of the 14th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, CAMS 2022. International Federation of Automatic Control. 2022. p. 257-268. (IFAC-PapersOnLine; 31). doi: 10.1016/j.ifacol.2022.10.440

A survey of recent machine learning solutions for ship collision avoidance and mission planning

Abstract

Publication series

Conference

Access to Document

Fingerprint

Projects

R1476ECS: Decarbonisation of Maritime Transportation - a Return to Commercial Sailing - Linked to R1475MEE

Research output

An Integrated Risk Assessment and Collision Avoidance Methodology for an Autonomous Catamaran with Fuzzy Weighting Functions

Cite this