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未知环境中移动机器人动态路径规划.docx

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未知环境中移动机器人动态路径规划 I.Introduction Dynamicpathplanningisanessentialtaskformobilerobotsoperatinginunknownenvironments.Itinvolvescalculatinganoptimalpaththattherobotshouldtakebetweentwopointswhileavoidingobstaclesintheenvironment.Inadynamicenvironment,obstaclescanmoveandchangeposition,forcingtherobottoconstantlyupdateitspathplanning.Thismakesdynamicpathplanningachallengingtask,astherobotmustquicklyreacttochangesintheenvironmentwhileensuringthatitreachesitsdestinationsafely. II.LiteratureReview Variousalgorithmshavebeenproposedfordynamicpathplanning,eachwithitsadvantagesanddisadvantages.Themostwidelyusedmethodsarepotentialfieldmethods,probabilisticmethods,andartificialneuralnetworks. Potentialfieldmethodsuseavirtualpotentialfieldtoguidetherobotalongapaththatavoidsobstaclesintheenvironment.Therobotisattractedtothegoalandrepelledbytheobstacles,andthealgorithmcalculatestheoptimalpathbasedonthegradientofthepotentialfield.However,thismethodcanresultintherobotgettingstuckinlocalminimaorbeingunabletomoveiftheobstaclesaretooclosetogether. Probabilisticmethods,suchasMonteCarloLocalization,useprobabilitytomodeltheenvironmentandthelikelihoodoftherobotbeingatacertainposition.Thismethodiseffectiveindealingwithdynamicenvironmentsasitcanquicklyupdatetherobot'slocalizationbasedonsensorreadings.However,itcanbecomputationallyexpensiveandmaynotbesuitableforreal-timeapplications. Artificialneuralnetworksuseamulti-layerednetworkofnodestogenerateanoptimalpathfortherobot.Theinputtothenetworkisthesensordatafromtherobot,andtheoutputisthedesiredtrajectory.Thismethodcanlearnfrompastexperienceandadapttochangingenvironmentsquickly.However,thismethodcanbecomplexandrequiresignificanttrainingdata,whichmaynotbeavailableinallsituations. III.ProposedMethod Inthisresearch,weproposeahybridalgorithmthatcombinesthestrengthsofthepotentialfieldmethodandartificialneuralnetworks.Thealgorithminvolvesusingapotentialfieldtonavigatetherobotthroughtheenvironmenttowardsitsgoalwhileusinganartificialneuralnetworktogenerat