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dc.contributor.authorSarhadi, Pouria
dc.contributor.authorNaeem, Wasif
dc.contributor.authorAthanasopoulos, Nikolaos
dc.date.accessioned2022-09-13T23:02:49Z
dc.date.available2022-09-13T23:02:49Z
dc.date.issued2022-09-14
dc.identifier.citationSarhadi , P , Naeem , W & Athanasopoulos , N 2022 , ' A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning ' , Paper presented at 14th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles , Copenhagen , Denmark , 14/09/22 - 16/09/22 .
dc.identifier.citationconference
dc.identifier.otherPURE: 27794491
dc.identifier.otherPURE UUID: f646ec7a-8948-4356-8059-4bb399565439
dc.identifier.otherORCID: /0000-0002-6004-676X/work/119159376
dc.identifier.urihttp://hdl.handle.net/2299/25766
dc.description.abstractMachine 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.en
dc.language.isoeng
dc.titleA Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planningen
dc.contributor.institutionDepartment of Engineering and Technology
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.description.statusPeer reviewed
dc.date.embargoedUntil2022-09-14
rioxxterms.versionAM
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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