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

Pouria Sarhadi, Wasif Naeem, Nikolaos Athanasopoulos

Research output: Contribution to conferencePaperpeer-review

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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 languageEnglish
Pages257-268
Number of pages12
DOIs
Publication statusE-pub ahead of print - 29 Nov 2022
Event14th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles - Technical University of Denmark, Copenhagen, Denmark
Duration: 14 Sept 202216 Sept 2022

Conference

Conference14th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles
Country/TerritoryDenmark
CityCopenhagen
Period14/09/2216/09/22

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