2026大语言模型：无人机指挥与控制接口

AUniversalLargeLanguageModel-Drone

CommandandControlInterface

JavierRamos-Silva1,2*andPeterBurke2,3

1*DepartmentofElectricalEngineeringandComputerScience,

UniversityofCalifornia,Irvine,MS2625,Irvine,92697,CA,USA.

2*BME,MSE,CBEMS,Physics,UniversityofCalifornia,Irvine,MS

2625,Irvine,92697,CA,USA.

Abstract

Theuseofartificialintelligence(AI)fordronecontrolcanhaveatransforma-tiveimpactondronecapabilities,especiallywhenintegratedwithdronesensing,command,andcontrol,partofagrowingfieldofphysicalAI.Largelanguagemodels(LLMs)foragenticdronecontrolprovideanewopportunityfordroneautonomy.However,challengesremainintheinterface,witheachusecaserequir-ingatedious,laborintensiveefforttoconnecttheLLMtothedrone.Here,usingthenewmodelcontextprotocol(MCP),wesolvethatproblem,bydevelopinganddemonstratinginflightandsimulationanLLMagnosticanddroneagnos-ticapproach,providingthefirstuniversal,versatile,comprehensiveandeasytousedronecontrolinterface.ThisprovidesauniversalwayforAIsystemstoaccessdronedata,tools,andservices.OurcloudbasedMCPserversupportstheMavlinkprotocol,anubiquitousdronecontrollanguageusedalmostuniversallybymillionsofdronesincludingArdupilotandPX4framework.WedemonstrateintegrationwithmultiplecommercialincludingallmajorfrontiermodelsandopensourceLLMs.Infurthertesting,wedemonstrateextensiveflightplanningandcontrolcapabilityinasimulateddrone,integratedwithaGoogleMapsMCPserverforuptodate,realtimenavigationinformation.ThisdemonstratesauniversalapproachtointegrationofLLMswithdronecommandandcontrol,aparadigmthatleveragesandexploitsvirtuallyallofmodernAIindustrywithdronetechnologyinaneasytouseinterfacethattranslatesnaturallanguagetodronecontrol.

Keywords:Largelanguagemodel(LLM),drone,unmannedaerialvehicles(UAVs),unmannedaircraftsystems(UAS)

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Fig.1Architecture.AnyLLMthatsupportstheMCPstandardcanconnecttotheMCPserver,whichinturnprovidesalowlevelinterfacewithadroneusingMavlinkprotocol.

1Introduction

Autonomyindronesisusuallybasedononboardimageandstateprocessingfromonboardsensors[1].Anoutstandingexampleisthe2023AIcontrolledracingdrone[2]thatbeatahumandronepilot,similartotheIBMsupercomputerbeatingtheworldchessmasterinthe1990s.However,challengesremainintheamountofcomputepoweronboardduetoweightandenergyrestrictionsforalldroneclassesandsizes,espe-ciallysmalldrones.Withthecomingofageofinternetconnecteddrones,usingAIinthecloudopensnewopportunitiestoharnessthevirtuallyunlimitedcomputepowerofdatacentersdeployedglobally.LLMsareonesuchtechnology,trainedatscale.Todate,theinterfacebetweentheAILLMandthedronehasbeenanunsolvedchallengeforgeneralusecases.Hereweshow,usingthemodelcontextprotocol(MCP)standard,acomprehensiveandcompletedronecontrolinterfaceusingtheMavlinkprotocolfordronecommunication.Wedemonstrateautonomouscontrolofarealworldinternetconnecteddrone(withreal-timedynamicdecisionmakingbytheLLM),anddemon-stratemorecomprehensivemissionswithavirtual(simulated)drone.ThisapproachiskeytounlockingthepowerofAIfromthevirtualworldtothephysicalworld.

2Architecture

Fig.1showsthearchitecture,andFig.2theconceptofoperations,designedtobridgethevirtualworldofAIwiththematerial(physical)worldofdrones.

2.1Conceptofoperations

AnyLLM(privateproprietaryoropensource,cloudorlocallyhosted)thatsupportstheMCPstandardcanconnecttotheMCPserverdevelopedinthiswork,whichwecall”DroneServer”.

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Fig.2Conceptofoperations.AnLLMhasaccesstomultipleservices,tools,andMCPservers.Oneofthemisthisdronecontrolserver,buttheLLMcanaccessanyofthousandsofotherMCPservers.Anexamplemissionisshownintegratingbothamap(Googlemaps)anddronecontrolserver.

AlistofLLMsasofthewritingofthispaperthatsupporttheMCPserverarchitectureis:

•AnthropicClaude–Nativesupport,includingClaude3.5Sonnet,ClaudeOpus4,andClaudeSonnet4.5.ClaudeDesktopapphasbuilt-inMCPclientsupport.

•OpenAI–AdoptedMCPinMarch2025.WorkswithGPT-4,GPT-4Turbo,andtheGPT-5seriesmodels.TheOpenAIAgentsSDKincludesMCPsupport.

•GoogleGemini–AnnouncedsupportinApril2025.WorkswithGemini2.0Flash,Gemini2.5Pro,andGemini2.5Flash.Googlelaunchedproduction-readymanagedMCPserversinDecember2025.

•Local/OpenSourceModels–AnyLLMthatsupportsfunctioncallingcanworkwithMCP,including:

–Llama3.2andLlama3.370B

–Qwen2.572B

–ModelsrunningonOllama(suchasqwen2.5:7b)

Inturn,theMCPservercanconnecttoanydronethatsupportstheMavlinkprotocolandhasaninternetconnection.ThisincludesPX4andArdupilot,thetwo

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largestdronesoftwarepackages,alreadyusedonmillionsofdrones.TheMCPserverhandlesalldronecommunicationsandprovidesLLMswithadescriptionofcapabilitiessuitableforthecontext.Wedescribeeachcomponentofthearchitectureindetailnext.

2.2Mavlinkprotocol

Mavlink[3]istheprotocolusedbyopensourceautonomousdronesrunningArdupilotandPX4.Thisincludesmillionsofdrones(land,air,sea)andthevastmajorityoftheglobalfleetofopensourcedrones.

Mavlinksupportshundredsofcommandsfordronecontrolandtelemetry,andissupportedbylibrariesinmostcommonmodernhighlevelprogramminglanguagessuchasC,Python,etc.

2.3Modelcontextprotocol:FrommanualtoolstouniversalstandardLLMinterface

2.3.1Tools:TediousandmanualagenticAIprogramming

Alargelanguagemodel(LLM)supportsinferenceandtextpredictionbasedontokens,butdoesnototherwiseperformanyfunction.Recently,LLMshavebeenprogrammedwiththeabilitytocall”tools”inordertointeractoutsidethecontextofpuretext.ThisenablesLLMstoperformasagents,accessinginternetcontent,readingandmodifyingfilesanddatabases,andevenwritingcode.Typically,eachtoolishandcodedorformalizedasanAPI,aprocessthatrequiresmanualattentionforeachsetoftoolsandeachvariationofanLLM.

2.3.2ModelContextProtocol

InordertoenablemoreseamlessconnectionbetweenLLMsandtools,themodelcontextprotocolconceptwasintroducedbyAnthropicinNov.2024[4].InDecember2025,AnthropicdonatedtheMCPstandardtheLinuxFoundation,inordertosecureitasanopen,industrywidestandard[5].TheMCPservesasalayerbetweenLLMsandtherestofthevirtualworldbystandardizingtheLLMcommunicationprotocol.AnMCPservercanconnectanyLLMtoanytool.MCPisnowsupportedbyallthemajorLLMproviders(OpenAI,Anthropic,Gemini).TheprimeusecasesinitiallyenvisionedincludedinteractionbetweenLLMsand(broadly)thevirtualworld,suchasbanking,finance,files,databases,websites,andsocialmedia,eventowriteandmodifycode.Fig.3showsthedetailedspecificationoftheMCPstandard.

3Developmentmethod

ThedevelopmentmethodwasbasedprimarilyonCursorIDE,aforkofMicrosoft’sVisualStudioCode,whichallowsaccesstomoderncodingtoolsincludingfron-tiermodelssuchasAnthropic’sClaude,GoogleGemini,andOpenAIChatGPT.Allofthecodewasgeneratedthisway.Thisisanextensionoftheconceptthatwerecentlydevelopedandpublished[6].Thisworkisforkedasamajorextension,

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Fig.3Modelcontextprotocol(MCP)standard.TheMCPserverexposesresources,prompts,andtoolstotheLLM.TheLLMdoesnotneedtoknowthedetailsoftheimplementationofthese,andusesthembasedonthecontextoftheprompt.

testanddemonstrationoftheoriginalprojectbasicMavlinkMCP(/ion-g-ion/MAVLinkMCP),whichhad10tools.Thisprojecthas15,000linesofcode,45tools,detailedinstallinstructions,aswellassystemdservicefilestoenabletheMCPservertoruncontinuouslyinthebackgroundwithouttheneedformanualstart.Githubwasusedforcontinuousdevelopment(asrecommendedin[6]),andprovidesdetailedusageinstructions.Testingwasperformedonbothavirtualandarealdrone,describedinmoredetailbelow.

4Serverarchitecture

Fig.4showsthedetailedarchitecture(the”techstack”)usedinthispaper.TheentireserverishostedonacloudLinuxUbuntuinstance.AnMCPserver(themaincontributionofthiswork)iscodedusingprimarlythePythonlanguage,deployedonacloudlinuxinstance,andconnectedtoadrone.TheMCPservertellstheLLMwhatsupportitprovidesfortheLLMtocontrolandgetinformationaboutthedrone.Thisishighlevel,inthenextsectionwedivedeepintotheMCPserverdesignandimplementation.

TheMCPserverconsistsofasetofcoderunonacloudLinuxinstancewithinternetconnectivity.Thecodebaseiscustomforthisworkandisavailableongithubat/PeterJBurke/droneserver.ThecodebaseismosltywritteninPython,becauseofallofthepackagesavailablebothforthedronecommandandcontrolinterface,andtheLLMinterface.Fig.4showsindetailthetechstackusedbytheMCPserver.Wedescribeeachbelow.

4.1Internals

Theinternalcodeprovidescustom,handcodedinterfacebetweenthedroneandtheLLM.Thiswriteonce,usemultipletimesprovidesdronepilotsandLLMcontextengineerswithapre-configuredinterface,sothehandcodingisnotnecessary.Natu-rallanguagepromptsareautomaticallytranslatedintodronecommandandcontrol

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interactions.Thisrepresentsthefirstversatile,universalinterfacebetweenanydronesupportingMavlink(whichismostofthem),andanyLLMsupportingMCP.Thus,theimpactisexpectedtobevastforapplicationsofindividualsingledroneswiththesignificantresourcesofLLMs,aswellasatscalewithcoordinatedswarmsofdronesandswarmsofagents.

4.2LLL-MCPserverlink

TheMCPPythonSDK[7]istheofficialPythonimplementationoftheModelContextProtocol,servingasaframeworkforbuildingserversthatexposetools,resources,andpromptstoAIapplicationslikeClaude.ItenablesdeveloperstobuildMCPserversthatexposeresources,tools,andpromptstoLargeLanguageModelsinastandard-izedway.Oncedeployed,itprovidesastandardizedwaytocreateintegrationsthatallowAIassistantsandagentstointeractwithexternalsystems,databases,APIs,andservicesthroughawell-definedprotocol.Itusesstandardtransportslikestdio,SSE,andStreamable

HTTPforcommunication

.(Here,weuse

HTTP

).ItistheprimarymachinethatprovidescontextandLLMinteraction.

Thepackageincludesbaseclassesandutilitiesfordefiningservercapabilities,han-dlingclientconnections,andmanagingtherequest/responselifecycle.DeveloperscanuseittobuildcustomMCPserversthatextendanAI’scapabilities-forexample,con-nectingtodatabases,filesystems,orthird-partyAPIs-whilemaintainingaconsistentinterfacethatMCP-compatibleclientscanconsume.

4.3DronetoMCPserverlink

4.3.1LowlevelMavlinkandTCP/IPareabstractedfromtheLLM

Startingfromthebottom,thelowlevelbitmovementfromtheMCPservertothedroneisthroughTCP/IP.Atthenextlevelup,theMavlinkprotocolisused.Mavlinkmessagesareverylowlevelmessages,andtherearehundredsofmessagesdefined.Althoughitwouldbepossibletocodeallofthemupintotheserver,theLLMmaynotneedorbeabletohandlesuchafinegrainedcontrolofthedronedetailedstateandconfiguration.Inaddition,itmaybetootaxingonthecontextwindow(seebelow)todefinehundredsoftools,oneforeachMavlinkmessage.Thisisdiscussedinmoredetailbelowinthesectioncalled“Numberoftoolsvscontextsize”.TheotherreasonisthathavingatoolforeachMavlinkmessagetypewouldbemuchlowerlevelthanatypicalusecaseforanLLM,whichweenvisionasintegratingatahigherlevelothertrainingdataandevenotherMCPserversandtoolsforintegrativesystemsmeta-engineeringratherthanlowlevelcontrolsuchasthrottlesetting,bankangle,etc.Therefore,inthiswork,wedidnotuseMavlinkasthebasesetofcommandstoexposetotheLLM.

4.3.2MavSDKisahigherlevelsetofcommandsusedforthiswork

WhileMavlinkistheprotocol,therearetwoPythonpackagesthathandlecommu-nications,links,andprovidehigherlevelcommandsandmethodswithinthePythonapplication.ThesearePymavlinkandMavSDK.PymavlinkprovidesadirectpythonimplementationoftheMAVLinkprotocol.Forexample,PyMavlinkcanbeusedto

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Fig.4Techstack.ThetechstackoftheMCPserverdevelopedinthiswork.Thedronecommu-nicatesoverTCP/IPusingMavlinkprotocol,whiletheLLMcommunicationsover

HTTPusingthe

MCPprotocol.Theservercontainscustomcodetocoordinatealltheinteractionstoprovideseemless

integrationbetweentheLLMandthedrone.

setthethrottleorreadtheattitudeandIMU.MavSDKishigherlevelabstraction,whichenablesmoremissionorientedcommands,suchas“gotoxyzlocation”“takeoff”“land”.MavSDKalsohandlesestablishingandmaintainingthecommunicationslinksunderthehood.Therefore,weusedMavSDKinthiswork.OurmodelexposesthekeyMavSDKmethodsastoolstotheLLMviatheMCPserver,discussednext.

MavSDKprovidesseveralhighlevelcommandssuchas“takeofftoxxxmeters”,or“flytoxyzposition”,commandswhicharenotavailableassimpleMavlinkcommands.Ofthe155MavSDKmethods,wechoseasubsettoimplementinthisinitialwork.Implementingallofthemethodswasnotdeemednecessaryatthistime,andthiswouldlikelyconsumetoomanytokensintheLLMcontext(seebelow).TableB1intheappendixlistsall155ofthemethodsinMavSDK(groupedbymajorfunction),whethertheyareexposedastoolstotheLLM,andabriefdescriptionoftheirtasks.Ofthe155,40areexposed.Table1belowshowsasasummaryhowmanyofeachclassareexposed.Wediscusstheselectionprocessinmoredetailbelow.

4.4BeyondonetoolperMavSDKmethod

IndevelopingtheMCPserverandtestingitonrealworldandsimulatedrealworldscenarios,wefoundthattheLLMwasnotwellsuitedtohandlesimplyMavSDKmethodspresentedastools.Forexample,theLLMwouldimmediatelyassumethatthedronewasatitsnewlocationaftersendingagotocommandviathegototool.Inresponsetoaprompt“Takeoff,flytoxyzlocationandland”,theLLMwouldsendallcommandssimultaneously,resultinginthedronelandingbeforeitreachedthetargetlocation.Thiscouldalsoleadtodangeroussituationsresultinginacrash.Forexample,ifpromptwastakeoffto100mandflytoxyz,theLLMmaysendthetakeoffandflytocommandinrapidsuccession,andthedronewouldnothavetimetoascendtotheinitialtakeoffheight,resultinginalowaltitudeflighttowardsthefinaldestination,whichmaybebelowthelevelofobstacles,resultinginacrash.Thus,asimple“onetoolperMavSDK”methodisnotadvisableforanMCPdronecommandandcontrolserver.

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Table1MAVSDKPythonMethodImplementationSummary

Category

Total

Implemented

Coverage

Action

22

10

45%

Telemetry

31

17

55%

Mission

10

6

60%

MissionRaw

7

2

29%

Param

7

5

71%

Camera

21

0

0%

Gimbal

8

0

0%

Offboard

10

0

0%

FollowMe

7

0

0%

Geofence

2

0

0%

ManualControl

3

0

0%

Info

5

0

0%

Calibration

6

0

0%

LogFiles

3

0

0%

FTP

9

0

0%

Tune

1

0

0%

Shell

2

0

0%

Transponder

1

0

0%

TOTAL

~155

40

26%

Therefore,wedecidedtomanuallyaddsomeadditionaltools,suchas“waitforxxx”tobeenabled.Severaladditionaltoolsweredefined,basedontheauthor’sextensiveexperiencewithprogrammingdrones[6,8].Inthefuture,itwouldbeagoodideatoautomatethisorevenuseAItoimprovethetoolsetbasedonadatabaseofmissionprofiles.Fornow,thetoolsweremanuallycuratedandtested.Table2showsasummaryofhowmanycustomandMavSDKcommandsweredeveloped.

4.5Oneoffvs.continuousdronecontrol

ModernLLMsaredesignedforprompt/responseoperation,whichdoesnotfitwellwithcontinuouscommandandcontrolofdronesforlongmissions.ThearchitectofmodernLLMsis“fireandforget”.However,fordroneflight,thereshouldinmanycasesbecontinuousmonitoring,whichLLMsarenotgoodit.Forexample,theLLMmaysayitcheckstheprogresseveryonceitawhilebutitdoesnotdoit.TheLLMalsotoldthedronetotakeoffandthenimmediatelytoldittoflytoxyzlocation,beforethetakeoffmaneviurwascomplete,causingthe(virtual)dronetocrashintoanobstacle.Also,themonitorlocationmethodweusedduringdevelopmentdidnotprovidetheuserwithrealtimefeedbackofthedronelocationorstatus.Infactoneoptionwastojustwaitforthesetlocationtoreturncomplete,whichcouldblocktheLLMforthelengthofthemission,whichmightbeaslongas30minutesorlonger.Therefore,wehadtoimplementsomelogicofrealtimemonitoringofthedroneintotheMCPserveritself.ThismadetheMCPserverakindofgroundcontrolstation

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withit’sowninternalmemoryandlogic,whichisnotthegoal.Ideally,allthememory,logic,realtime,longtermmonitoringwouldbedonebytheLLM,andtheMCPserverwouldjustbeaninterpreterbetweenthedroneandtheLLM.Wedonotknowwhatthecorrectlongtermsolutiontothisis.LLMtechnologywithrealtime,longtermsituationalawarenessandmemoryneedstobedevelopedforthisapproachtoscaleandreachitsfullpotential.

ThespecifictimingandcoordinationisspecifictotheLLMmodelused,andthechatbotand/oragenticwrapper.Forclosedsourceproviders,thesedetailsareusuallynotexposedtothepublic.Forexample,ChatGPT’sagenticmodeworkslikethis:RunALLtoolcallsinsequence.OnlyshowoutputtotheuserAFTERtheturniscomplete.Itdoesnotpausebetweentoolcallstoshowintermediateresults.WeattemptedtoworkaroundthiswiththeDISPLAYTOUSERtoolinordertogivetheuserincrementalprogressreportsonthestatusoftheflight,butChatGPTwaiteduntiltheendtoshowtheminsomecases

ItisanimportanttopicforfutureresearchtodevelopagenticLLMsystemsforcontinuousdronecommandandcontrol.Oncedeveloped,theinterfaceherecanserveasacontinuous,realtime,standardized,andeasytousebridgebetweenthevirtualLLMworldandtherealworld.

Table2SummaryofExposedMCPTools

Category

MAVSDKEq.

Custom

Total

FlightControl

4

1

5

Safety

3

0

3

Navigation

4

5

9

MissionManagement

6

4

10

Telemetry

14

0

14

ParameterManagement

3

0

3

Other

0

1

1

TOTAL

34

11

45

5Demonstrationandtesting

Demonstrationandtestingwasperformedonarealdroneandavirtualdrone.Therealdroneprovidedrealworldtesting,whilethevirtualdroneprovidedmoreextensivetestinginsituationsnotsuitable,safe,orallowedwitharealdrone.Inthemaintext,weuseOpenAI.ClaudeDesktopwasalsodemonstrated(notshown).Intheappendix,weshowanddiscussdemonstratedwithopen-source,locallyrunLLMsusingLMStudio.

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Fig.5Pictureofdroneusedinthiswork.ALIDARandopticalflowsensorisusedforGPS-deniedflights,forexampleinthedronecage.

5.1Realdrone

5.1.1Dronedesign

Thedronedesignwasasub-250g4inchquadrunningArdupilot,asdescribedinref.[9].Somemodificationswereneededforstableflightinoursmall(10x10x10foot)dronecage.Alidarandopticalflowsensorwasusedforpositionstabilizationwith1cmaccuracy.Thisenabledindefinitestablehover,despitetheweakornoGPSsignal.AsGPSholdstabilityisnotenough,eveninthepresenceofagoodlockinoursmallcage,theGPSfunctionalitywascompletelydisabledinthesoftware.ApictureofthedroneandtheLIDARisshowninFig.5.

5.1.2Demoincage:Initialflighttest

ThedronewasequippedwithaRaspberryPiZeroW,connectedtothedroneviaUART,andprovidingWiFiconnectiontotheinternet.MavlinkRouter[10]passedallMavlinktrafficfromtheinternettotheflightcontroller.AconnectiontothedronewithalaptoprunningMissionPlannergroundcontrolstation(usingTCPconnectivity)wasinitiallyusedtoconfirmthedronecouldtakeoff,loiter,andlandautonomouslyinthecagesafelybeforetheLLMcontrolwastested.

5.1.3Demoincage:LLMcontrolledflighttest

Oncestablehoverandflightwasdemonstratedunderinternetcontrol,theLLMwasthenconnectedtothedroneviatheMCPserver.TheMCPserverhastheIPaddressofthedroneasanenvironmentalparameter,andrunsasaserviceonacloudLinuxinstance.Duringtheflight,thedronewasalsomonitoredbyasimultaneousconnectiontoMissionPlanner,stillconnectedfromthefirsttestabove.

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5.1.4DetailsofMCPserverdeployment

Inordertohandlefirewalls,weusedaTailscaleVPNtoallowtransparent,securecommunicationfromtheMCPserverinthecloudtothedrone.TheMCPserverrunscontinuouslywithouttheneedforusermonitoring.However,fordebuggingpurposes,MCPactivitiesareprogrammedtobeloggedtotheterminalforstatusmonitoring.SincetheMCPserverrunsasasystemdservice/daemon,itisalwaysonatboot.ThedemandsontheLinuxinstancearenominal,sothecheapestLinuxcloudinstancesprovideplentyofprocessingandI/Opower.Thesearereadilyavailablefrommultipleserviceprovidersforalowcost.

5.1.5Demoincage:Actualflighttests

Fig.6showstheactualflighttest.Thedronewasontheground,withoutthepropellersspinning.TheLLMwasaskedtoflipacoin,andtakeoffifitcameupheads.Then,theLLMwasaskedaquestionaboutmovies,andifitwastrue,tolandthedrone.BothmaneuverswereexecutedflawlesslybytheLLM,asshowninFig.6.Thisdemon-stratesforthefirsttimeLLMnaturallanguagecontrolofarealdroneinresponsetounpredictableorpre-trainedworldknowledgeusingauniversalMCPinterface.

5.2Virtualdrone

5.2.1SITLinstance

ASITLSoftwareintheloop[11,12]instanceofavirtualdronewasdeployedonacloudhostedUbuntu22.04instance.ThevirtualdronethushadanIPaddress.Again,weusedTailscaleVPNforeasysecureconnectionbetweenfirewalledsystems.Agroundcontrolstation(QGroundControl)wasusedtocontinuouslymonitorthestatusandpositionofthevirtualdroneonamap.

5.2.2LLMvirtualdronecontrol

Extensive(virtual)testflightswereperformedtoconfirmthefullfunctionalityofthesoftwareMCPserverduringdevelopment.Theprojectwasabletocontrolthedronetakeoff,land,flyto,arm,disarm,andotherbasicfunctionalities.ThisdemonstratedtheabilityofanLLMtoflythedronevirtuallyanywhereintheworld.

5.2.3LLMvirtualdronecontrol:Hicups

OneofthedisadvantagestothisapproachistheLLMwasonlywillingtodoacertainnumberoftoolcallswhilemonitoringthedroneflight.Also,theLLMwouldnotalwaysfollowthepromptinstructiontoloopbetweentoolcallstocheckthedronestatus.Forthisreason,atthisjuncture,theMCPserverisnotabletofollowthedroneonlongmissions(longerthanabout5-10minutes).ThisisalimitationoftheLLMmodelused,andnotofthiswork.

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5.2.4IntegrationwithotherMCPsservers:Googlemaps

Duringtesting,wewouldaskthedronetoflytothenearestgrocerystore,buttheLLMmodeldidnothaveuptodateinformationabouttheworldmap.Byaseriesoffortunateevents,duringthewritingofthispaper,GoogledecidedtoopenupGoogleMapstoanMCPserveronDec.10,2025[13].Therefore,weusedthisasanopportunitytodemonstratemultipleMCPserversinasingleagentfordronecontrol.ShowninFig.7isademowheregooglemapswasusedtoprovideuptodaterealtimeinformationaboutlocalstore,andflythedronethere.Thisisamajormilestone.Thus,wehavedemonstratedMCPLLMdronecontrol,andotherMCPLLMintegrationforrealtime,globalnavigationinformationfordronecommandandcontrol.

6Discussion

6.1Numberoftoolsvscontextsize

AtypicalmaximumnumberoftokensforacommercialLLMisaround100k-1Mtokens.LocalLLMswithlesscomputepowerhavesmallerlimits.AnthropichasnotedthatMCPserverscanconsumealargenumberoftokens[14],andhassuggestedstrategiestoaddressthis.Inthiswork,weusedabout5ktokensforthetooldefinitions(45tools).Forthisapplication,thisisacceptable.However,thisneedstobetakenintoaccountforfutureagenticsystemswithmanydifferentMCPservers.

6.2Limitsandextensionsofthisapproach

ThisapproachisthefirstdemonstrationofLLMcontrolofadronethroughascalable,industrystandardinterfaceplatformMCP.ItabstractsawayfromtheLLMtheneedtoknowthedetailsofhowthedroneoperatesorcommunicates.Assuch,itrepresentsthefirststeptowardsphysicalAI,wheretheLLMhasknowledgeofthephysicalworldand,inthiscase,controlofit.

However,itonlyhasasmallamountofinformationinthisrealization.Theamountofinformationaboutthedroneinthisworkisonlythegpslocation,orientation,velocity.Inprincipleadditionaldatasuchastemperature,humidity,wind,etc,couldbebeameddownfromthedrone’ssensors.

However,theworkdoesnotprovideenoughinformationfortheLLMtoenabletheLLMtoprovideamoresophisticated3dmodelofthephysicalworld.Apossibleextensionofthisprojectwouldbetoenablethiscapability.OnesuchstrategycouldbetoequipswarmsofdroneswithLidar,Radar,sonar,orother3dmappingabilities,andtogivedynamicinformationintotheLLMdirectly.Thiswouldbeasteptowardscyber-physicalintegrationatscale:Similartohowgoogleearthisacomputermapoftheworld,onecouldenvisionanextensionofthisworktoanLLMmodeloftheentirephysicalworld,withdynamicaswellasstatic,highresolution3ddimensionalrepresentationsofobjectsandtheirinteractions.Suchatechnologywouldbetransfor-mativeandbringAIfromthevirtualtothephysicalworldforapplicationswecannotyetevenimagine.

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6.3Safety

Wedidourtestflightsinadronecage.Obviously,therearesafetyissues.Forone,wefirmlybelievethatthereshouldalwaysbeahumanintheloopforpossiblemanualoverride.

Inaddition,thereshouldbesomereliablewaytoensuretheLLMdoesnotbreakoutofanyfirewallrules.Forexample,theMCPshouldnotallowtheAItooverridetheoverride,e.g.lockthehuma