外文文献翻译基于激光测距仪的行人跟踪文档格式.docx

外文文献翻译基于激光测距仪的行人跟踪文档格式.docx

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Theabilityofrobotstotrackandfollowmovingtargetsisessentialtomanyreallifeapplicationssuchasmuseumguidance,officeorlibraryassistance.Ontopofbeingabletotrackthepedestrians,oneaspectofhuman-robotinteractionisrobot’sabilitytofollowapedestriantargetinanindoorenvironment.Therearevariousscenarioswheretherobotcanbegiveninstructionssuchasholdingbooksinalibraryorcarryinggroceriesatastorewhilefollowingthepedestriantarget.

ThekeycomponentsofmovingtargetfollowingtechniqueincludeSimultaneousLocalizationandMapping（SLAM）,DetectingandTrackingMovingObjects（DATMO）,andmotionplanning.Moreoftenthannot,therobotsarerequiredtooperateindynamicenvironmentswheretherearemultiplepedestriansandobstaclesinthesurroundings.Consequently,trackingandfollowingaspecifictargetpedestrianbecomemuchmorechallenging.Inotherwords,thefollowingbehaviorsmustberobustenoughtodealwithconstantocclusionsandobstacleavoidances.

Whendesigningthefollowingalgorithm,oneintuitiveapproachistosetthetargetlocationasthedestinationfortherobot.However,thisapproachcaneasilyleadtolosingthetargetbecauseitdoesnotreacttothetarget’smotionnorconsiderthevisibilityproblem（sincethetargetmaybeoccludedbyobstaclesandbecomeinvisible）.Forachievingrobusttargetfollowingandtracking,therobotshouldhavetheintelligenttopredicttargetmotionandgatherobservationsactively.

Inthispaper,weproposeamovingtargetfollowingplannerwhichisabletomanageobstacleavoidanceandtargetvisibilityproblems.Experimentalresultsareshowntocomparetheintuitiveapproachwithourapproachandprovetheimportanceofactiveinformationgatheringinplanning.Thispaperisorganizedasfollow:

SectionIIdiscussesrelatedworksofDATMOandplanningalgorithms.SectionIIIdescribesourDATMOsystemandintroducesourtargetfollowingplanner.Lastly,SectionIVillustratestheexperimentalresults.

II.RELATEDWORKS

TherearevariousapproachestodetectandtrackmovingobjectssuchasbuildingstaticanddynamicoccupancygridmapsproposedbyWolf&

Sukhatme[1],findinglocalminimainthelaserscanasinHoriuchietal.’swork[2]orusingmachinelearningmethodsinSpinelloetal.’swork[3].MostofDATMOapproachesassumethattherobotisstationaryorhasperfectodometry.Whentrackingmovingobjectsusingmobilerobots,ithasbeenproveninWangetal.’swork[4],thatSLAMandDATMOcanbedonesimultaneouslyifthemeasurementscanbedividedintostatic

anddynamicparts.Inourwork,weimplementaDATMOalgorithmwhichissimilartotheoneinMontesanoetal.’swork[5].Ascanmatchingmethodisusedtocorrectrobotodometryandmovingpointsaredetectedbymaintainingalocaloccupancygridmap.Moreover,ExtendedKalmanFilter（EKF）isappliedtotrackthemovingobjects.

Thispaperaimstosolvemovingtargetfollowingproblemwiththeexistenceofobstacles.Fornavigatinginstaticenvironments,therearemanysuccessfulworkssuchasFoxetal.[6],Ulrich&

Borenstein[7]Minguez&

Montano[8],Seder&

Petrovi’c[9].However,thosemethodsaredesignedtoreachafixedgoalandassumethattheenvironmentandrobotstatesarefully-observable.Applyingtraditionalobstacleavoidancealgorithmsonthetargetfollowingtaskcanfaileasilybecauseamovingtargetcanchangeitsspeedandmovingdirectionatanytimeandthetargetcanbeoccludedbyobstacles.Forplanningunderimperfectperception,Partially-ObservableMarkovDecisionProcess（POMDP）providesageneralframework.However,usingPOMDPtocomputeoptimalpoliciesareusuallyverycomputationallyexpensivebecauseithastocomputeaplanoverbeliefspace（typicallyN-1dimensionalforanN-stateproblem.）.ForapplyingPOMDPsonpracticalproblems,mostworksaimedonreducingthedimensionofbeliefspace.SuchlikePBVIPineauetal.[10],AMDPRoyetal.[11]andMOMDPSylvieetal.[12].ThosemethodsaremuchfasterthanoriginalPOMDP,buttheircomputationalcomplexityarestilltoohightodoreal-timeplanning.Ourmethodinthispaperismorelikeasub-optimalbutfastapproach:

assumingthattherobotalwaysreceivesthemostpossibleobservationsandplanapathwhichcanreachthegoalandminimizetheuncertaintyonparticularstates.Forexample,inPrentice&

Roy[13],therobotaimsonreachingthegoalwithminimumuncertaintyonitsposition,soitmaychooseapathwhichislongbuthasenoughlocalizationlandmarks.

Inthispaper,weproposeamotionplannerformovingtargetfollowing.TheplannerusesanextensionofdynamicwindowapproachpropsedbyChou&

Lian[14]tofindcollision-freevelocitiesandchooseapropervelocityusingheuristicsearch.Costfunctionsaredesignedforminimizingthedistancebetweenrobotandtargetandmaximizethepossibilitythattherobotcankeepobservingthetargetinafixedtimehorizon.Additionally,weapplytheconceptinnearnessdiagramalgorithmsuchastheoneinMinguez&

Montanos’work[8]forcomputingabetterestimationofthedistancebetweenrobotandtargetandthereforeachieveasmooth,non-hesitatingperformance.

III.MOVINGTARGETFOLLOWINGANDOBSTACLEAVOIDANCE

ItisessentialthatourDATMOsystemisabletotrackthemovingtarget,pedestrianinthiscase,withgreataccuracy.Themoreprecisepedestrianlocationacquired,thebettertherobotperformswhenfollowingthetarget.

A．DetectingandTrackingMovingObjects

Fordetectingmovingpointsinthelaserdata,weadopttheconceptofoccupancygridmapproposedbyWolf&

Sukhatme[1].Alocaloccupancygridmapisaintainedandusedtodifferentiatethemovingpoints.Forrobotlocalization,ascanmatchingtechniquecalledIteratedClosestPoint（ICP）isusedtoacquirerobotpose.Themovingpointsarefilteredoutofthedatapriortoscanmatchinginordertomaintaintheposeaccuracy.Thedetectedmovingpointsaresegmentedintonumerousclustersanddeterminediftheybelongtoapedestrianusingfeaturessuchasmotionvelocity,localminima,andsize.Finally,eachofthepedestrianistrackedusingExtendedKalmanFilter（EKF）whichsolvestheocclusionproblemandprovidesusthecomputationaladvantageinrealtimeperformance.

B.FollowingMethod

Ourgoalistoaccomplishmovingtargetfollowingwiththeexistenceofobstacles.Areasonablesolutionistoseeitasapathplanningproblemanduseobstacleavoidancealgorithmstofindcollision-freeactions.Inthispaper,weapplyourpreviouswork,DWA*,Chouetal.[10]astheobstacleavoidancealgorithm.

TheprocedureofDWA*isshowninFig.1（a）,itisatrajectory-rolloutalgorithm.TherightsideofFig.1（a）showstheprocedureforcomputingpropermotioncommands.First,theenvironmentinformationisrealizedasintervalconfigurationforfasterprocessing.Eachintervalvaluerepresentsthemaximumdistancecanberaveledbytherobotonacertaincirculartrajectory.Second,theintervalsareclusteredasnavigableareas.Third,foreacharea,acandidatevelocityisdeterminedaccordingtoanobjectivefunction.Foreachcandidatevelocity,anewrobotpositioniscomputedasanewnodeandsavedinatrajectorytree.Thenanodewiththeminimumestimatedcostvaluewillbeextractedasthebasenodeforgeneratingnewnodes.Theprocedureisrepeateduntilgoallocationisexpandedorthetreedepthreachesacertainvalue.Afterthetreeexpansionstops,thedeepestnodeisdeterminedasatemporalgoal,andthepresentcandidatevelocitywhichcanleadtherobottothetemporalgoalisselected.

Inourwork,weusedtwodifferentmethodsforintegratingDATMOandDWA*:

pseudogoalmethodandtrajectoryoptimizationmethod.Pseudo-goalmethodisamoreintuitiveapproachwhichdoesnotconsiderthetargetvelocitywhenfollowing.Anotherapproachistrajectory-optimizationwhereatrajectory-rolloutcontrollerisusedtoapproximateapredictedtargettrajectoryandmaximizetargetvisibility.Theirperformanceswillbeshownandcomparedinthenextsection.

1）Pseudo-goalmethod

Whenimplementingpeoplefollowingalgorithm,anintuitiveapproachissettingthepresentlocationofthemovingtargetasthegoalofnavigationalgorithm.Considerthetrackedtargetatananglewithrespecttotherobot,DWA*algorithmwillgenerateanangularandtranslationalvelocitywhichproducesanarc-liketrajectory.Ifthegoaliswithinacloseproximityoftherobot,theangularvelocitywillbesmallandtherc-likerouteoftenresultsinanindirectordetourpathfortherobottoreachthegoal（asshowninFig.2）.Onthecontrary,ifthegoalissetfurtherawayfromtherobotatthesameangle,anymovementofthegoalwillresultinamuchlargerdisplacementchangecomparetowhenthegoalisclosetotherobot.Therefore,theangularvelocitywouldincreaseinresponsetothesubstantialchangeofthegoallocation.Consequently,thetrajectorybecomesmoredirecttowardthegoal.

Inthepseudo-goalmethod,thespaceinfrontoftherobotisdividedinto7trajectoriesat35_,55_,70_,90_,110_,130_,145_asshowninFig.3.Apseudogoalisset3timestheoriginaldistancebetweenthegoalandrobotalongeachtrajectory.Afteracquiringthepedestrianlocation（redcircleinFig.3）,thepseudogoalwithtrajectoryclosesttothepedestrianlocationisthenselected.Bysettingthegoalfurtherawayfromtherobot,itremediestheissueofsmallangularvelocityandprovidesamuchmoredirectpathtoreachthegoal.

Anotherproblemisthelimitedsensingability.Inthispaper,thealgorithmisimplementedonamobilerobotwitha180-degreePOVLRF.