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无人驾驶汽车局部路径规划算法研究.docx

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无人驾驶汽车局部路径规划算法研究 Title:ResearchonPartialPathPlanningAlgorithmforAutonomousVehicles Abstract: Theadventofautonomousvehicleshasrevolutionizedtheautomotiveindustry,offeringimmensepotentialforenhancedmobility,increasedsafety,andimprovedefficiency.Oneofthekeycomponentsofautonomousdrivingistheabilitytoplanoptimalandsafepathsinreal-time.Thispaperaimstoinvestigateandanalyzevariouspartialpathplanningalgorithmsforautonomousvehicles,providinganoverviewoftheiradvantages,limitations,andfutureresearchdirections. Keywords:autonomousvehicles,pathplanning,partialpath,algorithm Introduction: Autonomousvehiclesrequirerobustandefficientpathplanningalgorithmstonavigateandmaneuverincomplexanddynamicenvironments.Partialpathplanningalgorithmsfocusonlocalplanning,consideringaspecificsegmentoftheoveralltrajectorytoensuresmooth,safe,andreal-timedecision-making.Thispaperwilldelveintotheresearchandanalysisofseveralpartialpathplanningalgorithms,highlightingtheirstrengthsandweaknesses. 1.TraditionalPartialPathPlanningAlgorithms: 1.1.PotentialField-BasedMethods: Potentialfield-basedalgorithmsapplyavirtualforcefieldconcepttoplantrajectoriesaroundobstacles.Theyconsiderattractiveforcestowardgoaldestinationswhilerepulsiveforcesaregeneratedaroundobstacles.Whilesimpletoimplementandinterpret,theyoftensufferfromlocalminimaandmightnotgenerategloballyoptimalpaths. 1.2.VisibilityGraph-BasedMethods: Visibilitygraph-basedalgorithmscreateagraphconnectingallvisiblepointsintheenvironment.Theseapproachesrelyongraphsearchalgorithmstodeterminetheoptimalpath.Whileeffectiveinstaticenvironments,theyarenotsuitablefordynamicscenariosastheyrequirefrequentgraphupdates. 2.Sample-BasedPartialPathPlanningAlgorithms: 2.1.Rapidly-ExploringRandomTrees(RRT): RRTisapopularsample-basedalgorithmthatgeneratesatreebyrapidlyexploringtheconfigurationspace.Iteffectivelyhandlescomplex,high-dimensionalspacesandcanadapttodynamicenvironments.However,itmightgeneratesuboptimalpathsduetorandomness. 2.2.ProbabilisticRoadmaps(PRM): PRMconstructsa