Neurobotics Lab Research Learning, Vision and Sonar Recognition with Mobile Robots

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NeuroboticsLabResearch:Learning,VisionandSonarRecognitionwithMobileRobotsPaoloGaudiano,CarolinaChang,IhsanEcemis,SiegfriedMartens,ErolS¸ahin,WilliamStreileinandRobertWagner

ArtificialLife,Inc.,FourCopleyPlace,Boston,MA02116BostonUniversityNeuroboticsLab,677BeaconSt.,Boston,MA02215

AbstractThisarticleprovidesanoverviewofresearchprojectsundertakenintheNeuroboticsLaboratoryatBostonUniver-sity.Wefocusonapplicationsofneuralnetworksandotherbiomimetictechniquesinsensoryprocessing,navigation,andothertasksusingmobilerobots.Theseapplicationssharesomecentralthemes:theinclusionofminimalas-sumptionsabouttherobotsandtheenvironment;cross-validationofmodulesonavarietyofroboticsplatformsandenvironments;andreal-timeoperationusingrealrobots.

Keywords:Mobilerobots,looming,mobilerobots,robotlearning,Neuralnetworks,ARTMAP,sensorfusion

1IntroductionTheNeuroboticsLaboratorywasfoundedin1996withthegoalofapplyingneuralnetworksandotherbiomimetictechniquestothecontrolandguidanceofwheeledmobilerobot.Researchinthelabcoversvariousproblemsinthegeneralareaofautonomousmobilerobotics,withanemphasisonnavigationandcontrolusingbiomimeticalgorithmsthatoperateinreal-timewithonlyminimalassumptionsabouttherobotsortheenvironment,andthatcanlearn,ifneeded,withlittleornoexternalsupervision.Inthefollowingsectionswedescribeseveralresearchprojectsthatwehaveinvestigatedoverthelastthreeyears.Inthefirstproject,aneuralnetworkmodelofclassicalandoperantconditioninglearnstogenerateavoidanceandapproachmovementsinamobilerobotwithoutexternalsupervision.Thesecondprojectinvolvestheuseofvisualloomingasasimple,efficienttechniqueforrangingandlocalization.ThethirdprojectutilizesneuralnetworksbasedonAdaptiveResonanceTheory(ART)forrangingandlocalizationusingsensorfusionbetweensonarandvisualestimatesofdistance.ThefourthprojectalsousesanART-basedneuralnetwork,butthistimetoperformreal-timeobjectrecognitionusingspectralinformationfromultrasonicsensorreturns.

2LearningapproachandavoidancebehaviorswithoutsupervisionWhenananimalhastooperateinanunknownenvironmentitmustsomehowlearntorecognizeinformativecuesintheenvironment,andtopredicttheconsequencesofitsownactions.Thislearningispossiblefororganismsinspiteofwhatseemlikeinsurmountabledifficultiesfromastandardengineeringviewpoint:noisysensors,unknownkinematicsanddynamics,non-stationarystatistics,andsoon.Psychologistshaveidentifiedclassicalandoperantconditioningastwoprimaryformsoflearningthatenableanimalstoacquirethecausalstructureoftheirenvironment.Classicalconditioningreferstotheactoflearningtorecognizeinformativestimuliintheenvironment;forinstance,adogcanbetrainedtolearnthataringingbellprecedesthearrivalofashock,untilthebellitselfwillcausethedogtobeafraid.Inthecaseofoperantconditioning,ananimallearnstheconsequencesofitsactions.Morespecifically,theanimallearnstoexhibitmorefrequentlyabehaviorthathasledtoareward,andtoexhibitlessfrequentlyabehaviorthathasledtopunishment.Forexample,apigeoncanbetrainedtopeckatanilluminatedkeyinordertoreceiveasmallfoodreward.In1971,Grossbergproposedamodelofclassicalandoperantconditioning,whichwasdesignedtoaccountforavarietyofbehavioraldataonlearninginvertebrates.Themodelwasrefinedinseveralsubsequentpublications.In1987,Grossberg&Levinedescribedacomputersimulationofamajorcomponentoftheconditioningcircuit.Thismodelwasusedtoexplainanumberofphenomenafromclassicalconditioning.OurimplementationofGrossberg’sconditioningcircuit,whichfollowscloselythatofGrossberg&Levine[11],isshowninfig.1.InthepresentmodelthenodesattheupperleftofFig.1receiveactivationfromtherobot’srangesensors.Notethatthereisnoknowledgebuiltintothenetworkabout(1)thekindofsensorinformation(e.g.,infraredorsonar),or(2)thepositionofthesensorontherobot’sbody.SPCSCS2

CR

UCRCS1

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Angular VelocityMap

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SensorReadings

CollisionD

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Figure1:Conditioningmodelforobstacleavoidance.TherangesensoractivitiesrepresenttheCSs.Acollisiondetec-toractivatestheUCS.Motorlearningoccursatapopulationcodingtherobot’stargetangularvelocity.Afterconditioning,thepatternofactivityacrosstherangesensorscanpredictacollisionandmodifytherobot’sangularvelocitytoavoidtheobstacle.

(a)(b)Figure2:OverheadviewoftheKheperainitsenvironmentduringobstacleavoidance(a)andlightapproaching(b)behaviors.

AtthebottomofFig.1thedrivenodeisrepresented:conditioningcanonlyoccurwhenthedrivenodeisactive.Inourmodelthishappenswhentherobotcollideswithanobstacle,whichcouldbedetectedthroughabumpsensor,orwhenanyoneoftherangesensorsindicatesthatanobstacleiscloserthanthesensor’sminimumrange.Finally,theneuronsatthefarrightofthefigurerepresenttheresponse(conditionedorunconditioned),andarethusconnectedtothemotorsystem.Inourmodelthemotorpopulationconsistsofnodes(i.e.,neurons)encodingdesiredangularvelocities,i.e.,theactivityofagivennodecorrespondstoaparticulardesiredangularvelocityfortherobot.Forinstance,theleftmostnodecorrespondstoturningleftatthemaximumrate,thecentralnodecorrespondstostraightahead,andsoon.WehaveusedtheneuralnetworkofFig.1totrainaKheperaminiaturemobilerobot(K-TeamSA,Preverenges,Switzerland)toavoidobstaclesandapproachlights.Therobotistrainedbyallowingittomakerandommovementsinaclutteredenvironment.Whenevertherobotcollideswithanobstacleduringoneofthesemovements(orcomesveryclosetoit),thenodescorrespondingtothelargest(closest)rangesensormeasurementsjustpriortothecollisionwillbeactive.Activationofthedrivenode(whichinthiscaserepresentsaversion)allowstwodifferentkindsoflearningtotakeplace:thelearningthatcouplessensorynodes(infraredorultrasound)withthedrivenode(thecollision),andthelearningoftheangularvelocitypatternthatexistedjustbeforethecollision.Likewise,therobot’slightsensorscanbeusedtolearnlightapproachingbehaviorbyactivatingaseconddrivenode(appetitivedrive)wheneverthelightsensorsreadasignificantincreaseinlightlevel.Theonlydifferencebetweenapproachandavoidancelearningisthatactivationoftheapproachdrivenodeleadstoexcitatoryconnectionstothepopulationthatgeneratesangularvelocities,whileactivationoftheavoidancedrivenodeleadstoinhibitoryconnections.Hence,asaresultofitsownexperiences,therobotlearnstogeneratemoreoftenthosemovementsthatleadtoincreasedlightlevels,whilesuppressingthosemovementsthatleadtocollision.Additionaldetailsonthisneuralnetworkcanbefoundelsewhere[7].Figure2showssomeoftheresults.EachpanelshowstheKheperarobotinitsenvironmentasseenfromanoverheadcamera.Aworkstationtrackstherobot’s