2024利用生成式AI增强自主系统的安全性和韧性白皮书（英文版）-TII

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EnhancingSecurity,Resilience,andSafetyofAutonomousSystemswithGenerativeAI

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TableofContents

Sidebar:GenAImodels

4

GenAIUseCases

9

IndividualApplications

9

Self-Awareness

10

Anomalydetection

11

Autonomy

12

Adaptability

12

Predictivemaintenance

13

Faultmanagement

14

Fleet

16

Swarmintelligence

17

Swarmcoordination

19

Communicationresilience

21

Consensus

22

Human

23

Missionplanningandexecution

24

Human-machineteaming

25

Meta-learning

26

Datalabelingandsynthesis

27

Cybersecurity

28

Malwaredetection

29

Intrusiondetection:

31

Integratingthreatintelligence33

Policymanagement33

ThreatSimulation:34

Softwaresupplychainvisibility34

Challenges34

Cost35

Compute36

Adaptation36

Ethical/Regulatory36

Alignment36

Privacy36

Accuracy37

Size37

Newsecurityissues38

Conclusion38

Glossary41

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Rapidadvancesinautonomoussystemsandedgeroboticshaveunlockedunprecedentedopportunitiesinindustriesfrommanufacturingandtransportationtohealthcareandexploration.Increasingcomplexityandconnectivityhavealsointroducednewchallengesinensuringsecurity,resilience,andsafety.Asedgerobotsintegrateintoourdailylivesandcriticalinfrastructures,itisimperativethatwedevelopinnovativeapproachesthatcanraisethesesystems’trustworthinessandreliabilitytonewlevels.

ThiswhitepaperexploresthetransformativepotentialofgenerativeAI(GenAI)toenhancethesecurity,resilience,andsafetyofautonomoussystemsandedgerobots.Wecanusethesecutting-edgetechnologiestomeettheuniquedistributedanddynamicchallengesofedgerobotics,tounlocknewlevelsofintelligence,adaptability,androbustness.

GenAImodelsproducenewcontentbyanalyzingpatternsinadataset.Theyderivecharacteristicprobabilitydistributionsandapplythesetocreatenewdatapatternsthatareconsistentwiththeoriginal“real”dataset.

EarliergenerationsofdiscriminativeAImodelsappliedconditionalprobabilitiestopredictoutcomesforpreviouslyunseendata.Theapproachisversatileandwell-suitedtoawiderangeofproblems,includingclassificationsandregressions.Theyexcelatdelineatingthedecisionboundariesthatdifferentiatebetweenvariousclassesorcategorieswithinthedataset.

Thegrowingranksofgenerativetechniquesincludethosebasedontransformersandtheresultinglargelanguagemodels(LLMs),GenerativeAdversarialNetworks(GANs),VariationalAutoencoders(VAEs),GenerativeFlowModels(GFM),andGenerativeDiffusionModels(GDM).ThesehaveallopenedexcitingnewavenuesofAIresearch—withapplicationstodroneswarmsandintrusiondetection,physicalcommunicationsecurity,semanticcommunication,andmobilenetworks

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TheTechnologyInnovationInstitute’sSecureSystemsResearchCenter(TII-SSRC,AbuDhabi,UAE,

https://www.tii.ae/secure-systems)

isworkingtoapplyGenAItoitsworkextendingZeroTrustarchitectures(developedforinformationsecurity)toeveryaspectofinformationsecurityincyber-physicalsystems.

Thus,SSRCconsidershowGenAIcanhelpguaranteesecurity,resilience,andsafetyfordrones,swarms,swarmsofswarms,autonomousterrestrialandmarinevehicles,commandsystems,andhuman/droneinteractions—andparticularlyinareaswhereGenAIoutperformstraditionalAI/MLapproaches.

Examplesinclude:

•Individualapplications:healthmonitoring,stateestimation,predictivemaintenance,anomalydetection,self-healing,navigation,andemergencylandings.

•Fleetapplications:Swarmcoordination,swarmintelligence,collectivedecision-making.

•Human/Droneinteractions:communicationresilience,missionplanning,human-computerinteraction.

•Cybersecurityandresilience:Intrusiondetection,malwareclassification,threatsimulation.

ThispaperwillfocusondronesbecauseSSRCisdoingsomuchworkinthisarea.Whatwelearnfromdronescanbeappliedmuchmoretoautonomousandcyber-physicalsystemsingeneral,includingcars,robots,embeddedsystems,andsmartcities.Bythesametoken,

lessonslearnedintheseareascanbefoldedintoSSRC’sresearch.

approachallowsorganizationstomoveawayfromphysicaldevicemanagementapproachesthatrequireemployeestocarrymultiplephones.

1M.Xuetal.,“UnleashingthePowerofEdge-CloudGenerativeAIinMobileNetworks:ASurveyofAIGCServices.”arXiv,Oct.31,2023.

doi:10.48550/arXiv.2303.16129

Sidebar:GenAImodels

AsurveyofspecificgenerativeAImodelingtechniques,withtheirstrengthsandlimitationsasappliedtodronesafety,security,andresilience.

PopularexcitementovergenerativeAImodelshasbeendrivenbyhighlypublicizednewserviceslikeChatGPT,whichcreateauthoritative-soundingresponsestotextpromptsusingspeciallytrainedLLMs.

LargeLanguageModelsareAIsystemstrainedonvasttextdatasets.TheyuseDeepLearningtechniques,particularlyastructureknownasaTransformer,to“understand”andgeneratehuman-liketextbasedonthepatternsthey'velearned.LLMsanalyzetherelationshipsandcontextsinthetrainingdata.Theyuseavarietyoftechniquestobuildsimplifiedrepresentationsofthedata,allowingthemtomakeassociationsandcorrelationsbetweentheoriginaldataelements.Thesemodelsletthemcomposeresponsesthatcanmimichumanwritingstylesandcoverdiversetopics.VisualAIs,likeDALL-EandStableDiffusion,compilenewimagesfromtextandimageprompts.

LLMsarewidelyused,andwidelyuseful,increatingcontent,code,translations,summaries,syntheticdata,andtostructureunstructureddatafromtexts,documents,images,audio,andotherpromptdata.

Transformermodelsandtheservicesbuiltonthem—likeOpenAI’sChatGPT,Google’sGemini,andAnthropic’sClaude—haveattractedwidespreadattention,thankstotheirimpressiveabilitytocreatearticulate-seemingresponsestohumanprompts.These,andotherdomain-specificLLMsandSmallLanguageModels(SMLs),alsoshowpromiseforsupportinganalysis,research,anddevelopmenttoimprovedronesafety,securityandresilience.

ParallelingtheseveryvisibleAIdevelopments,however,hasbeenalmostadecadeofprogressonnewclassesofGenerativeAImodelscouldautomateandacceleraterepresentation-building.Whilegenerativeapplicationsattractthemostattentions,thesenewmodelsalsoaredrivingadvancesinanalyzingdataandinteractingwiththeworldaroundus.OthergenerativeAImodels—GenerativeAdversarialNetworks,VariationalAutoencoders,GenerativeDiffusionModels,andNormalizingFlowModels—thoughrelativelyunknown,canmakesubstantialcontributionstodronesecurity,safety,andresilience.

TransformerModels:Introducedin2017totranslatebetweenEnglishandFrenchtexts,TransformerModelsexcelatcapturinglong-rangedependenciesandcorrelationswithinunstructureddata

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Transformersleverageanovel“attentionmechanism”tolearntheconnectionsbetweenwordstohelpcreateembeddingsautomatically.Priortechniquesrequiredtranslatingrawtextintoavectorrepresentationusingaseparatemodel.Transformerscanbuildcomplexrepresentationsandlearnintricateconnectionsthroughtheirlayeredarchitecture,manner.,allowingresearcherstoprocesslargebodiesofunlabeledtextanddeveloplargelanguagemodelswithbillionsofparameters.Subsequentinnovationshavesupporteddocumentsummarization,composingquestion/answerassociationsacrosslargedatasets,codegeneration,in-depthanalysis,intrusiondetection,malwaredetection,andtranslatingcontrolsysteminstructionsacrossroboticarms.Theapproach’skeyadvantageisdistillingcontextfromcomplexdatasets.Challengesincludehallucination,longertrainingtime,slowerinference-building,heaviercomputationrequirements,andlargermodelsizescomparedtoothertechniques.

2A.Vaswanietal.,“AttentionIsAllYouNeed.”arXiv,Aug.01,2023.

doi:10.48550/arXiv.1706.03762.

GenerativeAdversarialNetworks(GANs):Theseweredevelopedin2014tocreaterealisticsyntheticnumbers,faces,andanimalimages

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GANspittwoneuralnetworksagainsteachother:oneisrewardedforgeneratingmorerealisticcontent,andthesecondisrewardedfordetectingfakecontent.Inthiscompetition,thegeneratorimprovesitsabilitytocreaterealisticoutputsthatcanfoolthediscriminator.GANsarewidelyusedincontentgeneration.Sincethefirstversionsweredesignedtoworkwithimages,researchersarenowfindingcreativewaystotranslatedata,suchascodeornetworklogs,intoimagessuitableforGANprocessing.GANsaregoodforrealisticsyntheticdatasetsthatcanbeusedtoimproveautonomoussystemsandcybersecurityalgorithms.They,too,however,sufferfromfailureslikemodecollapseorcatastrophicforgetting.

3I.J.Goodfellowetal.,“GenerativeAdversarialNetworks.”arXiv,Jun.10,2014.doi:10.48550/arXiv.1406.2661.

VariationalAutoencoder(VAE):VAEswereintroducedin2014toimproveinferencesdrawnfromacontinuouslyvaryingdatastream

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Thetechniquehelpsfindefficientwaystorepresentdataandcanbeusedtocompressdataordetectanomaliesandthreats.TrainingVAEsprocessinvolvesteachingasetofencodersanddecoderstotranslaterawdataintoanintermediatelatentspacewithadifferentprobabilitydistribution.VAEscanbeusedindependentlyinapplicationslikeanomalydetection,designingbetterencodingschemes,dataaugmentation,andimagegeneration.Inaddition,theyareoftenusedtopre-structuredataforotheralgorithms,includingGANs,toimprovetheirresults.

GenerativeDiffusionModel(GDF):GDFsemergedin2015toimprovelearning,sampling,inferences,andevaluationsthatwereinformedbynon-equilibriumthermodynamicsmodeling

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Thetechniqueaddsnoisetoasample(suchasanimage)andthenautomatesthedenoisingprocesstorevealthedata’sunderlyingstructure.Slightvariationscanleadtovalidnewtrainingdatasets.GDFsarewidelyusedinimagegenerationandcanimprovesignalclassificationinvariousdroneusecases.However,thetechniquerequireshighersamplingtimesanddemandsamorecomplexarchitecturethanGANsandVAEs.

NormalizingFlowModels(NFMs):Thesewereintroducedbyresearcherstomakecomplexdatasimplertoworkwith

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Thesemodelstakeeasy-to-understanddistributions,likeanormalbellcurve,andtransformthemstepbystep.Eachstepisreversible,meaningwecanalwaysgobacktothestartifneeded.Thisprocess,calleda“flow,”movesfromasimplebeginningtoanendthat

4D.P.KingmaandM.Welling,“Auto-EncodingVariationalBayes.”arXiv,Dec.10,2022.doi:10.48550/arXiv.1312.6114.

5Yang,Ling,etal."Diffusionmodels:Acomprehensivesurveyofmethodsandapplications."ACMComputingSurveys56.4(2023):1-39.

/doi/10.1145/3626235

6Kobyzev,Ivan,SimonJDPrince,andMarcusA.Brubaker."Normalizingflows:Anintroductionandreviewofcurrentmethods."IEEEtransactionsonpatternanalysisandmachineintelligence43.11(2020):3964-3979.

/abstract/document/9089305/authors

resemblesthecomplicatedtargetdataset.Bydoingthis,itispossibletostudyandusethedatamoreeffectively.NFMshavebeenusedtogeneratehandwrittennumbers,images,etc.Newerusecasesincludeenhancedclassificationandencodingschemes.Thetrainingprocesscreatesamodelthattransformstheprobabilitydistributionofadatasetintoamorecomplex,fullyreversibledistribution.NFMscan,however,requirehighercomputationandtrainingtimesthantechniqueslikeGANsandVAEs.

ThefollowingfiguresummarizesthemainGenAITechniquesandtheirapplicationsinthefieldofZeroTrustforautonomoussystems.

GenAIUseCases

Theproliferationofdrone-technologyhasbroughtchallengesthatspancrossbetweendomains—individual,fleet,humancontrol,andcybersecurity.Theirgrowthandcomplexitydemandconstantinnovationtoredoubletheirtrustworthinessandreliability.

Thefollowingapplications—whetherderivedfromUnmannedAerialVehicle(UAV)anddroneresearchorimportedfromotherdomains—haveimportantimplicationsforthefutureofUAVsandotherautonomoussystems.Note,too,thatmanyoftheseareearly-stageprojects,includedtogiveaflavorwhatGenAItoolsmightaccomplishastechniquesevolve.

GenAIshowstremendouspotentialforimprovingZeroTrustframeworkstoenhancesecurity,resilience,andsafetyinindividualautonomoussystems,suchasdrones,self-drivingcars,

robots,andembeddedsystems.Usecasesunderinvestigationincludeboostingself-

awareness,anomalydetection,autonomousdriving,predictivemaintenance,faultmanagement,self-healing,andlandingsafely.

Self-Awareness

Opportunity:Efficientlytranslatenoisy,blurry,andinconsistentdatatounderstandthedrone'scurrentstate—e.g.,compensatingformotionblurwhiletryingtodetectobstacles.

Thefoundationofdronehealthisaccuratelycapturingandmakingsenseofitscurrentstate—includingtheconditionofitscurrenthardware,itsapplications,itsphysicallocation,anditssecurityposture.Intherealworld,thiscangetmessy,asvideofeedssuffermotionblur,GPSdatajitters,inertialguidancedatalosecalibration,andnoiseorgapsdegradeinternalmonitoringdata.

Stateestimationiscrucialtoautonomousnavigationanddecision-making,andrawdatastreamsmustbeaccuratelycorrelatedwithposition,velocity,andorientation

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GenerativeAIcanhelpfillinmissingdataandfusedatafrommultiplesourcestoimprovestateestimation

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InnovationsinGenAIalgorithmslikeGANs,VAEs,andtraditionalMLalgorithmslikeLSTMcanfillinthesegaps,preservingvehiclesafetyviafaultdetection,predictivemaintenance,faultmanagementandsafe-landingprotocols.Forexample,innovativeGANapproachescanfillinmissingdataandmakeiteasiertofusedatastreamstocreateamoreaccuratestateassessment

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helpcorrelateinternallogdatawithacousticanalysis

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andidentifypotentialmechanicalissues

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Researchershavealsodevelopedtechniquesforgeneratingestimated-statevariablesusingConditionalGANs(CGANs)forindividualdronesanddroneswarms

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7T.D.Barfoot,Stateestimationforrobotics.CambridgeUniversityPress,2017.

8Liu,Guangyuan,NguyenVanHuynh,HongyangDu,DinhThaiHoang,DusitNiyato,KunZhu,JiawenKang,ZehuiXiong,AbbasJamalipour,andDongInKim.“GenerativeAIforUnmannedVehicleSwarms:Challenges,ApplicationsandOpportunities.”arXiv,February28,2024.

/10.48550/arXiv.2402.18062.

9Y.He,S.Chai,andZ.Xu,"Anovelapproachforstateestimationusinggenerativeadversarialnetwork,"in2019IEEEInternationalConferenceonSystems,ManandCybernetics(SMC),2019,pp.2248-2253.

/document/8914585

10Y.Wang,A.Vinogradov,“ImprovingtheperformanceofconvolutionalGANusinghistory-stateensembleforunsupervisedearlyfaultdetectionwithacousticemissionsignalsAppl.Sci.,13(5)(2023),p.3136,doi:10.3390/APP13053136

11S.Zheng,A.Farahat,andC.Gupta,“GenerativeAdversarialNetworksforFailurePrediction.”arXiv,Oct.04,2019.Accessed:Mar.15,2024.[Online].Available:

/abs/

1910.02034

12.A.He,C.Luo,X.Tian,andW.Zeng,"AtwofoldSiamesenetworkforreal-timeobjecttracking,"inProceedingsoftheIEEEconferenceoncomputervisionandpatternrecognition,2018,pp.4834-4843.

/document/8578606

Anomalydetection

Opportunity:Improveanalysisofdronesensordatatoidentifyabnormalconditions.

Moreaccurate,multi-dimensionalsystem-staterecordscanalsohelpidentifyanomaliesrelevanttodronehealth.Forexample,VAEscanimprovefaultdetectionandisolation.Theycanalsoidentifythewarningsignsofstressinvarioussystems,toprioritizepredictivemaintenanceschedules.Typically,machine-learningclassificationalgorithmsaretrainedonmultipleclasses(suchasdatatagged“faulty”and“notfaulty”).Dataonrarerclasses,suchas“faulty”data,isoftenscarceinpublicdatasetsandtherealworld.Inthesecases,GenerativeAdversarialNetworks(GANs)canbevaluableinsynthesizingtheserareclasses—makingup“faulty”datathatlooks

conditions.Inaddition,researchersareexploringhowLLMscouldhelpbettercontextualizetooperateinnewenvironments

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Forexample,DriveLLMcombinesLLMwithtraditionalautonomousnavigationalgorithmstosupportbetterreasoninganddecision-makingwhenrespondingtoedgecases

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Researchersfoundthemethodcouldimproveproactivedecision-makinginunexpectedcircumstances.Anotherapplication,TypeFly,enhancescommunicationbetweenhumansanddronesthroughanaturallanguageinterfac

e15.

Suchlargelanguagemodelsmay,however,manifestsurfacebias,inaccuracies,and

hallucinationissuesthatrequireadditionalsafeguards.Similarly,MicrosoftResearchdiscussestheiradvancementsinintegratingChatGPTwithroboticstomakerobotcontrolmoreintuitive

throughnaturallanguage.They'veenabledChatGPTtounderstandandexecutetasksin

physicalenvironments,whichfacilitateseasierhuman-robotinteractionwithouttheneedfor

complexprogrammingknowledge.TheChatGPTteamhasdevelopeddesignprinciplesfor

languagemodelstosolveroboticstasks(involvingspecialpromptingstructuresandhigh-levelAPIs),andtheyhavedemonstratedhowChatGPTcanhandletaskslikeoperatingdronesandrobotarmsthroughuser-friendlycommandsandfeedback.Thedevelopersemphasizethe

importanceofsafetyandsimulationtestingbeforereal-worldapplication

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Adaptability

Opportunity:Improvetranslationofautonomoussystemssoftwaretorunacrossdifferenthardwaremakes,models,andconfigurations.

Autonomoussystemcontrollersmustbetrainedforaspecificmodelandconfiguration.Thiscancreatechallengeswhenupgradingindividualcomponentsoradoptingnewmodels.RTXisarobotcontrolLLMthatcantranslatecontrolpoliciestomanagedifferentroboticarmswithoutrefactoringthecontrolalgorithmsforthelatesthardware.Insometests,leveragingtheexperienceofothercontrollers,producedcontrolpoliciestheoutperformedthebestcontrolscustom-builtforanindividualarm

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13Wang,Lei,etal."Asurveyonlargelanguagemodelbasedautonomousagents."FrontiersofComputerScience18.6(2024):1-26.

/article/10.1007/s11704-024-40231-1

14Y.Cuietal.,“DriveLLM:ChartingthePathTowardFullAutonomousDrivingWithLargeLanguageModels,”IEEETransactionsonIntelligentVehicles,vol.9,no.1,pp.1450–1464,Jan.2024,doi:10.1109/TIV.2023.3327715.

15Chen,Guojun,XiaojingYu,andLinZhong."TypeFly:FlyingDroneswithLargeLanguageModel."arXivpreprintarXiv:2312.14950(2023).

/pdf/2312.14950

16Vemprala,Sai,etal."Chatgptforrobotics:Designprinciplesandmodelabilities."arXivpreprintarXiv:2306.17582(2023).

/abs/2306.17582

17O.X.-E.Collaborationetal.,“OpenX-Embodiment:RoboticLearningDatasetsandRT-XModels.”arXiv,Dec.17,2023.doi:10.48550/arXiv.2310.08864.

EarlyLLMs,likeGPT3.5,weretrainedonlargebodiesoftextscrapedfromtheInternet.Thesemodelslackedreal-worldexperiencethatcouldreflecthowvariousconfigurationsofrobotsandotherautonomoussystemsmakeandexecutedecisions.Researchintoroboticaffordancesexploreshowtoconstraineachrobotmodeltoactionsthatarefeasibleandappropriatefortheircapabilities

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ThisprovidesaframeworkforguidingLLMdevelopmentbasedonmorecompleteknowledgeofanoperationorprocedure.Atthesametime,thegroundingfunctiontranslatesthishigh-levelknowledgeintoexecutionbyaparticularrobotmodelinaspecifictargetenvironment.

Predictivemaintenance

Opportunity:Predictthependingbreakdownofdronecomponentstooptimizemaintenance,repair,andpart-replacementschedules.

Properlyrecordedandanalyzed,thedrone’ssensorandoperationaldatarevealimpendingmechanicalproblemsbeforebreakdownsoccur.Predictivealgorithmsletmaintenanceandrepaircrewsestablishregularschedules,prioritizemaintenance,andstayaheadofpartsinventories.Withadvancenoticeandplanning,evenmajorrepairsandreplacementscanbeperformedduringroutineservice.Theprobabilitiesofcostlybreakdownsand,worse,catastrophicfailuresdropsharply.Partscanbereplacedjust-before-needed,withservicelifecalculatedasafunctionofinstalled-partquality,servicetime,andoperationalprofile—slashingthecostsofreplacingperfectlysoundpartsonafixedschedule.

TraditionalMLalgorithmsoftenlieattheheartofpredictivemaintenance.Forexample,metricslikeRemainingUsefulLife(RUL)andHealthIndicatorscanidentifymotoranomalies.ButsyntheticdatageneratedbyGANsandotherGenAIalgorithmscanimprovethealgorithms’performance.

MultipleMLtechniques,includingGenAIalgorithms,canbecombinedtoimprovefaultdiagnosisandpredictivemaintenanceworkflows

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Forexample,GANtechniqueshavebeenappliedtoacousticsignalsfrommachinerytoidentifyandpredictfaultsnotidentifiedbyothermethods

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GANshavealsobeenusedtogeneratesyntheticmonitoring-datasetstohelptrainotherMLalgorithmstoimprovefailure-predictionandoptimizemaintenanceschedules

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GAN-FP,geneticadversarialnetworksforfailureprediction,specializeingenerating,balancing,andlabelingtrainingdatatoimproveperformanceofotherMLalgorithm

s22.

Faultmanagement

Opportunity:Identifyfaults,makedynamicadjustments,andeffectasafelandingwhenrequired.

18M.Ahnetal.,“DoAsICan,NotAsISay:GroundingLanguageinRoboticAffordances.”arXiv,Aug.16,2022.Accessed:Mar.21,2024.[Online].Available:

/abs/2204.01691

19Z.Mianetal.,“Aliteraturereviewoffaultdiagnosisbasedonensemblelearning,”EngineeringApplicationsofArtificialIntelligence,vol.127,p.107357,Jan.2024,doi:10.1016/j.engappai.2023.107357.

20Y.Wang,A.VinogradovImprovingtheperformanceofconvolutionalGANusinghistory-stateensembleforunsupervisedearlyfaultdetectionwithacousticemissionsignalsAppl.Sci.,13(5)(2023),p.3136,10.3390/APP13053136

21Q.Fu,H.Wang,J.Zhao,andX.Yan,“AMaintenance-predictionMethodforAircraftEnginesusingGenerativeAdversarialNetworks,”in2019IEEE5thInternationalConferenceonComputerandCommunications(ICCC),Dec.2019,pp.225–229.doi:10.1109/ICCC47050.2019.9064184.

22S.Zheng,A.Farahat,andC.Gupta,“GenerativeAdversarialNetworksforFailurePrediction.”arXiv,Oct.04,2019.Accessed:Mar.15,2024.[Online].Available:

/abs/1910.02034

GenAImodelscantransformdataforotherMLsystemstoimprovefaultdetectioninautonomoussystems.Forexample,VAEscanhelpcompressoperationaldataintomoreefficientrepresentationsforlongshort-termmemorynetworks(LSTN),atypeofrecurrentneuralnetwork

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Spatio-temporaltransformernetworkscancapturetrendsanddimensionsacrossdifferenttimescalestoimprovebatteryfaultdiagnosisandfailureprognosis,enhancingpredictivemaintenanceforUAVs.Forexample,theBERTerysystemcanspotsubtlechanges(changesinvisibletoearlierMLtechniques)thatsignalimpendingbatteryfailureasmuchas24hoursbeforethebatteriesfail

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GANshavebeenusedtogeneratetrainingsamplesandbuildinferencenetworksforaircraft-enginemonitoringdatatoimprovefailurepredictionsofotherMLalgorithms

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ResearchershavecombinedVAEsandLSTMtosupportcontinuouslearningfromvehiclesensordata,generatingsyntheticdataforwiderrangesoffaultscenarios.BytrainingotherMLalgorithmsonthiskindofsyntheticdata,Sadhuetal.achieved90%accuracyindetectingfaultsand99%accuracyinclassifyingthem.

Demandsforcomputingpowerandrelativelyslowexecutionspeedaretopconcernswhenrunningthesekindsofalgorithmsonlow-costhardware.OnesolutionistoportthecomputationstoFPGAs,whicharemorepower-efficientthanGPUs.ThisishowSadhuetal.achieveda40xspeedup(athalfthepowerconsumption)fortheirVAE-LSTMfaultdetectionalgorithm

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VAEscanalsobeusedtotrainmodelsthatidentifynormaloperation.Usingthistechnique,Dhakletal.achieveda95.6%accuracyindetectingdeviationsindicativeoffaultsandanomaliesnotrepresentedinthetrainingdataset

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Whenaproblemarisesinadroneoritscommunicationsnetwork,thedronemustlandsafelytoavoidsecondarydamage.Tominimizethisrisk,MonteCarloalgorithmshavebeenusedtocalculate“targetlevelsofsafety”(levelsofacceptablerisk)forvariouslandingzones

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Techniqueslikethiscouldbecombinedwithtransformerstomakecontext-awaredecisionswhenafaultforcesaUAVsystemtoselectanappropriatelandingzone.

Inthefuture,itmayalsobepossibletouseGenAItechniquesliketransformerstoletsystemsself-healinresponsetohardwarefailures,softwarebugs,ornetworkdisruption.Forexample,Khlaisamniangetal.haveproposedaframeworkforusingGenAItodetectanomalies,generatecode,debugit,andcreatereportsoncomputersystems

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Althoughstillinitsearlystages,thiswo