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1、ProbabilisticAlgorithmsinRoboticsSebastianThrunApril2000CMU-CS-00-126SchoolofComputerScienceCarnegieMellonUniversityPittsburgh,PA15213AbstractThisarticledescribesamethodologyforprogrammingrobotsknownasprobabilisticrobotics.Theproba-bilisticparadigmpaystributetotheinherentuncertaintyin
2、robotperception,relyingonexplicitrepresen-tationsofuncertaintywhendeterminingwhattodo.Thisarticlesurveyssomeoftheprogressinthefield,usingin-depthexamplestoillustratesomeofthenutsandboltsofthebasicapproach.Ourcentralcon-jectureisthattheprobabilisticapproachtoroboticsscalesbettertocomple
3、xreal-worldapplicationsthanapproachesthatignorearobot'suncertainty.ThisresearchissponsoredbytheNationalScienceFoundation(andCAREERgrantnumberIIS-9876136andregulargrantnumberIIS-9877033),andbyDARPA-ATOviaTACOM(contractnumberDAAE07-98-C-L032)andDARPA-ISOviaRomeLabs(contractnumberF30602-
4、98-2-0137),whichisgratefullyacknowledged.Theviewsandconclusionscontainedinthisdocumentarethoseoftheauthorandshouldnotbeinterpretedasnecessarilyrepresentingofficialpoliciesorendorsements,eitherexpressedorimplied,oftheUnitedStatesGovernmentoranyofthesponsoringinstitutions.Keywords:Artific
5、ialintelligence,bayesfilters,decisiontheory,robotics,localization,machinelearn-ing,mapping,navigation,particlefilters,planning,POMDPs,positionestimation1IntroductionBuildingautonomousrobotshasbeenacentralobjectiveofresearchinartificialintelligence.Overthepastdecades,researchersinAIhavede
6、velopedarangeofmethodologiesfordevelopingroboticsoftware,rangingfrommodel-basedtopurelyreactiveparadigms.Morethanonce,thediscussiononwhattherightwaymightbetoprogramrobotshasbeenaccompaniedwithspeculationsconcerningtheverynatureofintelligenceperse,inanimalsandpeople.Oneoftheseapproache
7、s,probabilisticrobotics,hasledtofieldedsystemswithunprecedentedlevelsofautonomyandrobustness.Whiletherootsofthisapproachcanbetracedbacktotheearly1960s,inrecentyearstheprobabilisticapproachhasbecomethedominantparadigminawidearrayofroboticproblems.Probabilisticalgorithmshavebeenatthecore
8、ofase
