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Usingworkspaceinformationasaguideto

non-uniformsamplinginprobabilistic

roadmapplanners

JurP.vandenBerg

MarkH.Overmars

instituteofinformationandcomputingsciences,utrechtuniversity

technicalreportUU-CS-2003-037

www.cs.uu.nlUsingworkspaceinformationasaguidetonon-uniform

samplinginprobabilisticroadmapplanners

JurP.vandenBergMarkH.Overmars

November2003

Abstract

Theprobabilisticroadmap(PRM)plannerisapopularmethodforrobotmotionplanningproblemswithmanydegreesoffreedom.However,ithasbeenshownthatthemethodperformslesswellinsituationswheretherobothastopassthroughanarrowpassageinthescene.Thisismainlyduetotheuniformityofthesamplingusedintheplanner;itplacesmanysamplesinlargeopenregionsandtoofewintightpassages.Inthispaper,atechniquebasedonarobotindependentcelldecompositionofthefreeworkspaceisproposedtoguidetheprobabilisticsampling,suchthatthedistributionofsamplestendsmoretowardtheinterestingregionsinthescene.Itisshownthatthisleadstoimprovedperformanceondifficultplanningproblemsin2Dand3Dworkspaces.

1Introduction

Motionplanningisofgreatimportanceinvariousfields,suchasrobotics,virtualenviron-

ments,maintenanceplanning,andcomputer-aideddesign.Althoughanumberofexactand

completemethodshavebeenproposedfortherobotmotionplanningproblem(see[16]for

anoverview),theseareseldomlyusedbecausetheyareonlyapplicabletothesimplestin-

stancesoftheplanningproblem.Formorecomplicatedproblems,wheretherobothasmany

degreesoffreedom,thesemethodsarecomputationallyinfeasible.Therefore,thefocushas

shiftedtowardprobabilisticandheuristicmethods,sacrificingcompletenessforspeedand

applicability.

AtechniqueoftenusednowadaysistheProbabilisticRoadmap(PRM)planner[2,3,12,

14,17,18].Theideabehinditisthataroadmapiscreatedthatrepresentstheconnectivity

ofthefreepartoftheconfigurationspace.Thenodesofthegrapharerandomlysampled

collision-freeconfigurationsthatareconnectedbyasimpleandfastlocalplanner(typicallya

straight-linemotioninconfigurationspaceisused).Themethodiscapableofsolvingmotion

planningqueriesincomplexenvironments,andhasbeenusedinmanypracticalsituations.

However,themethodhastroubleinfindingpathsthroughnarrowpassagesinthescene.

Thisismainlyduetotheuniformityofthesampling;itplacesmanysamplesinopenregions

andtoofewintightpassages(athoroughanalysisisgivenin[11]).Thisproblemhasreceived

muchattentionofresearchersinthefield.Theearlieststrategiestryingtotacklethisprob-

lemuseinformationfromtheroadmapadaptivelyduringconstruction.Theyaddadditional

nodesintheneighborhoodofnodesthatwereonlyconnectedtoafewneighbors[13,15].

Latermethodsinvolvesamplingmoredenselynearobstacleboundaries[1,6,10],orfarfrom

obstacles,onthemedialaxis[20].Despitetheamountofworkdoneonthistopic(seethepro-

ceedingsoftheyearlyIEEEInternationalConferenceonRoboticsandAutomation(ICRA)

11INTRODUCTION2

Figure1:Acelldecompositionoftheworkspaceofa2Dexamplescene.Thegreycellsform

thewatersheds.

andtheWorkshoponAlgorithmicFoundationsofRobotics(WAFR)formanycontributions),

agenericsolutionhasnotyetemerged.Thegeneralobservationremains,however,thatthe

wayofsamplingiscrucialfortheresult[7,8,9].

Whereaspreviousmethodsaremainlyobstacle-basedstrategies,weproposeamethodthat

usestheshapeoffreeworkspacetoguidethesamplinginconfigurationspace.Information

fromtheworkspacecanonlybeusedeffectivelywhentheconfigurationspacemoreorless

resemblestheworkspace.Thismeansthatnarrowpassagesintheconfigurationspaceshould

correspondtonarrowpassagesintheworkspace,andthatconfigurationsindifficultregions

canbemappedstraightforwardlytothecorrespondingpointsintheworkspace.

Thisholdsforalargeclassofproblemsinrobotmotionplanning:free-flyingrobotsintwo

orthreedimensionalscenes.Ifthesizeoftherobotisnottoolargecomparedtothesizeof

thescene,eachdifficultregionintheconfigurationspacecanberelatedtoarelativelynarrow

passageintheworkspace.

Ourmethodidentifiesthenarrowpassagesintheworkspaceusingacelldecomposition

approach.Becausetheworkspaceisonly3-dimensional,thiscaneasilybedone.Subsequently,

cellsofthedecompositionaregroupedintoregionsofinterestbylabelingthem.

Thelabeling-partisthemostcrucialinthiscontext.Forthis,weproposeamethodcalled

watershedlabeling,inspiredbyatechniquefromthefieldofimageprocessing,calledwatershed

segmentation[19].Itisapowerfulmethodthatseparateslargeopenregionsfromeachother

byso-calledwatersheds.Thesewatershedsarepositionedinsidethecorridorsconnectingthese

regions.Asaresult,thenarrowpassagesarelabeleddifferentlythantheopenregions.See

Fig.1foranexample.

Thisinformationisusedtoeffectivelysteerthesamplingtowardthemostinteresting

regionsofthescene.Tothisend,eachofthelabeledregionsisassignedaweightindicating

thechancethatasampleinpickedinsidethisregion.Tobeprecise,forasamplewepickthe

positionintheregionandtheotherdegreesoffreedomrandomly.Narrowpassageregionswill