Utility- and Plan-based Action Selection based on Probabilis(2)

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

Fig.1.(a)Theodo(goalkeeper),Hugibert(attacker)and(b)whattheyperceiveoftheworldaroundthem.

port.APALcolorCCDcameraismountedontopoftherobotconsoleandlinkedtotheS-VHSinputofthevideocapturecard.Gain,shuttertime,andwhitebalanceofthecameraareadjustedmanually.CurrentlyRGB-16imagesarecapturedwitharesolu-tionof384172(bottom60%ofhalfPALresolution).Forbetterballguidancewe’vemountedasimpleconcave-shapedbarinfrontofeachrobot.Acustommadekickingdeviceenablestherobottokicktheballindirectionoftherobot’scurrentorientation.3FundamentalSoftwareConcepts

Thesoftwarearchitecture[9]ofoursystemisbasedonseveralindependentconcur-rentmodules.Themodulesareorganizedhierarchicallyintomain,intermediate,andbasicmodules.Themainmodulesareimage(sensor)analysis,informationfusion,ac-tionselection,pathplanning,androbotcontrol.Besidethemainmodulesthesystememploysauxiliarymodulesformonitoringtherobots’states.ThekeysoftwaremethodsemployedbytheAGILORoboCupperssoftwareare:

1.vision-basedcooperativestateestimationfordynamicenvironments,

2.synergeticcouplingofprogrammingandexperience-basedlearningformovementcontrolandsituatedactionselection,and

3.plan-basedcontrolofrobotteamsusingstructuredreactivecontrollers(SRCs).

3.1SelfLocalization,ObjectTrackingandDataFusionThevisionmoduleisakeypartofoursystem.Givenarawvideostream,themod-ulehastoestimatetheposeoftherobotandthepositionsoftheballandopponentrobots.Low/medium-levelimageprocessingoperationsareperformedwiththehelpoftheimageprocessinglibraryHALCON5.2.3(formerlyknownasHORUS[7]).

Wehavedevelopedaprobabilistic,vision-basedstateestimationmethodforindi-vidual,autonomousrobots.Thismethodenablesateamofmobilerobotstoestimatetheirjointpositionsinaknownenvironmentandtrackthepositionsofautonomouslymovingotherobjects.Allposesandpositionsofthestateestimationmodulecontainadescriptionofuncertainty,i.e.acovariancematrix.Thestateestimatorsofdifferentrobotscooperatetoincreasetheaccuracyandreliabilityoftheestimationprocess.Thiscooperationbetweentherobotsenablesthemtotracktemporarilyoccludedobjectsand

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