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基于蚁群算法的无人机航迹规划研究.docx

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基于蚁群算法的无人机航迹规划研究 Abstract Unmannedaerialvehicles(UAVs)havebecomeincreasinglypopularinrecentyearsduetotheirvariousapplications,suchassurveillance,agriculture,andrescuemissions.However,thesafeandefficientnavigationofUAVsinvariouscomplexenvironmentsremainsagreatchallenge.Inthispaper,weproposeanovelapproachbasedonantcolonyoptimization(ACO)totackletheproblemofUAVtrajectoryplanning.Specifically,weuseACOtosearchfortheoptimalpathforUAVstonavigateinagivenenvironment,consideringthesafetyconstraints,missiongoals,andasetofotherfactors.ThesimulationresultsdemonstratethatourproposedapproachcaneffectivelyimprovetheperformanceofUAVnavigation,andhenceprovideavaluablereferenceforthedevelopmentofpracticalUAVsystems. Introduction WiththerapiddevelopmentofUAVtechnology,UAVsarebecomingmoreandmorepopularinvariousfields,suchasmilitary,civilian,andcommercialapplications.TheabilitytonavigateautonomouslyincomplexenvironmentsiscrucialforthesuccessofUAVmissions.Therefore,thereisagrowingdemandforeffectiveUAVtrajectoryplanningalgorithmsthatcanensuresafeandefficientUAVnavigation. TheconventionalmethodsforUAVtrajectoryplanningincludeheuristicapproaches,suchasDijkstra’salgorithmandA*algorithm,andoptimization-basedmethods,suchasgeneticalgorithmsandparticleswarmoptimization.However,thesemethodsoftensufferfromtheproblemsoflocaloptima,slowconvergence,andhighcomputationalcomplexity,whichlimittheirapplicabilitytoreal-timeapplications. Toaddressthesechallenges,antcolonyoptimization(ACO)hasbeenproposedasanalternativeapproachtoUAVtrajectoryplanning.ACOisametaheuristicalgorithminspiredbytheforagingbehaviorofants,whichcaneffectivelysearchfortheoptimalsolutioninalargesearchspace.ACOhasbeenwidelyappliedinvariousoptimizationproblems,suchastravelingsalesmanproblemandvehicleroutingproblem.Inrecentyears,ACO-basedapproacheshavealsobeendevelopedforUAVtrajectoryplanning,andshownpromisingresultsintermsofreducingtheglobalenergyconsumptionandimprovingthesafetyofUAVnavigation. Inthispaper,weproposeanovelACO-basedapproachfo