Utility- and Plan-based Action Selection based on Probabilis(3)

Abstract. This paper describes the AGILO RoboCuppers 1 the RoboCup team of the image understanding group (FG BV) at the Technische Universit?t München. With a team of four Pioneer I robots, all equipped with CCD camera and a single board computer, we’ve

tofasterrecovertheirpositionsaftertheyhavelosttrackofthem.Adetaileddescrip-tionoftheselflocalizationalgorithmcanbefoundin[8]andthealgorithmsusedforcooperativemulti-objecttrackingareexplainedin[13,12].

Ourvisionalgorithmscanprocessupto25framespersecond(fps)ona200MHzPentiumPC.Theaveragenumberofimagesprocessedduringamatchisbetween12and17fps.Thisisduetocomputationalresourcesbeingsharedwiththepathplanningandactionselectionmodules.

3.2ExperienceBasedLearningforSituatedActionSelection,PathPlanning

andMovementControl

Anothermajor eldofourresearchactivitiesisautomaticrobotlearningbasedonexperiencesgainedfromexploration.Experiencebasedlearningprovidesapowerfultoolfortheautomaticconstructionofhigh-performanceactionselectionandlow-levelrobotcontrol.Inthisrespectexperiencebasedlearningcaneffectivelycomplementothermethodsfordevelopingsuchcontrollers,inparticularthehandcodingofcon-trollers.Weuselearningfromexperienceinseveralpartsofoursystemsuchaslowlevelrobotcontrol,pathplanningandactionselection.

InlowlevelrobotcontrolwerepresentthestateofaPioneerIrobotasaquintuple

,whereandarecoordinatesinaglobalsystem,istheorienta-tionoftherobotandandarethetranslationalandrotationalvelocities,respectively.

Thelow-levelrobotcontrolleracceptscommandsoftheform.Aneuralnet-workmapsthedesiredstatechangestolowlevelrobotcommands:

Totrainthisnetworkwemeasureahugenumberofstatechangesaccordingtodifferentexecutedlowlevelcommands[6].Doingsoourneuralcontrollerisbasedonnothingbutexperiencenotmakinganyassumptions.

Inorderto ndtheoptimalpathplanningalgorithmforourRoboCuprobotswesta-tisticallyevaluateddifferentmethodsandfoundoutthatthereisnooptimalalgorithmbutanumberofnavigationproblemclasseseachperformedbestwithacertainalgo-rithm/parameterization[6].Theseclassesarede nedwiththehelpofafeaturelan-guage.Inordertoselectthebestmethodforthegivensituationwe’velearnedadecisiontree[11].Thetrainingdataisobtainedfromaccuraterobotsimulationswhereahugenumberofpathplanningproblemswereperformedwithdifferentalgorithmseach.

Theselectionofanappropriateactionisperformedonthebasisofafusedenvi-ronmentalmodel.Asetofpossibleactionssuchasgo2ball,shoot2goal,dribble,block...isde ned.Forallrobotsandeachofthoseactionssuccessrates

[5].Fromallpromisingactions,whichexceedapre-andgainsareestimatedde nedthresholdtheonewiththehighestgainischosentobecarriedout.

3.3Plan-basedActionControl

Whileoursituatedactionselectionaimsatchoosingactionsthathavethehighestex-pectedutilityintherespectivesituationitdoesnottakeintoaccountastrategicassess-mentofthealternativeactionsandtherespectiveintentionsoftheteammates.Thisisthetaskoftheplan-basedactioncontrol.

Inordertorealizeanactionassessmentbasedonstrategicconsiderationandonaconsiderationsoftheintentionsoftheteammates,wedeveloparobotsoccerplaybook,alibraryofplanschematathatspecifyhowtoperformindividualteamplays.Theplans,orbetterplays,aretriggeredbyopportunities,forexample,theopponentteamleaving.

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