基于改进Q-Learning的智能船舶局部路径规划（英文）-豆柴文库

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基于改进Q-Learning的智能船舶局部路径规划（英文）
ImprovingQ-LearningforIntelligentShipLocalPathPlanning
Abstract:
Asthemaritimeindustryembracesautomationandunmannedtechnologies,theabilitytonavigateautonomouslyandsafelybecomescrucialforships.Localpathplanningplaysavitalroleinenablingautonomousshipstonavigatethroughcomplexanddynamicenvironments.ThispaperproposesanimprovedversionofQ-Learning,apopularreinforcementlearningalgorithm,forintelligentshiplocalpathplanning.Theproposedapproachaimstoenhancetheaccuracyandefficiencyofshippathplanning,takingintoaccountvariousenvironmentalfactorsandshipdynamics.ExperimentalresultsdemonstratetheeffectivenessandsuperiorityoftheproposedmethodcomparedtotraditionalQ-Learningalgorithms.
1.Introduction:
Autonomousshipnavigationhasgainedsignificantattentioninrecentyearsduetoitspotentialtorevolutionizethemaritimeindustry.Localpathplanning,whichinvolvesdeterminingthebestpathforashiptonavigatethroughobstaclesandreachitsdestination,isacriticalcomponentofautonomousshipnavigation.Q-Learning,apopularreinforcementlearningalgorithm,hasbeenwidelyusedforpathplanninginvariouscontexts.However,traditionalQ-Learningalgorithmsoftenoverlookimportantenvironmentalfactorsandshipdynamics,leadingtosuboptimalpathplanningsolutions.Therefore,amodifiedandimprovedversionofQ-Learningisproposedinthispapertoaddresstheselimitationsandimproveshiplocalpathplanningefficiency.
2.RelatedWork:
Thissectionreviewsexistingapproachesandstudiesrelatedtoshiplocalpathplanning.Traditionalmethods,suchaspotentialfields,sufferfromthelocalminimumproblemandmaynotprovideoptimalsolutions.Machinelearningandreinforcementlearningtechniques,includingQ-Learning,havebeenemployedtoaddressthisissue.However,thesealgorithmsoftenlacktheabilitytoconsiderdynamicenvironmentalfactorsandshipcharacteristicsadequately.
3.ProposedMethod:
TheproposedmethodimprovesupontraditionalQ-Learningalgorithmsbyincorporatingadditionalenvironmentalfactorsandshipdynamicsduringtheplanningprocess.Firstly,amorecomprehensivestaterepresentationisde