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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