Robotics Development Environment RDE for multirobot and multiuser applications.docx

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Robotics Development Environment RDE for multirobot and multiuser applications.docx

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Robotics Development Environment RDE for multirobot and multiuser applications.docx

RoboticsDevelopmentEnvironmentRDEformultirobotandmultiuserapplications

Thesoftwaretoolmainlyteachesstudentsthenavigationproblemsofamobilerobotavoidingobstaclesinastaticenvironmentusingdifferentalgorithms.Thesimulationenvironmentisofamenu-drivenonewherestudentscandrawobstaclesofstandardshapesandsizesandassignthestartingpointofthemobilerobot.Therobotwillthennavigateamongtheseobstacleswithouthittingthemandreachthegoalpointgivenbytheuser.Parametersassociatedwiththedifferentalgorithmsmayalsobechangedtoobservetheireffectswhichwillfurtherenablecomprehensionofcharacteristicsofdifferentpathplanningalgorithms.

Healthinsuranceandthelaborsupplydecisionsofolderworkers:

EvidencefromaU.S.DepartmentofVeteransAffairsexpansion OriginalResearchArticle

JournalofPublicEconomics,Volume94,Issues7-8,August2010,Pages467-478

MelissaA.Boyle,JoannaN.Lahey

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Abstract

Thispaperexploitsamajormid-1990sexpansionintheU.S.DepartmentofVeteransAffairshealthcaresystemtoprovideevidenceonthelabormarketeffectsofexpandinghealthinsuranceavailability.UsingdatafromtheCurrentPopulationSurvey,weemployadifference-in-differencesstrategytocomparethelabormarketbehaviorofolderveteransandnon-veteransbeforeandaftertheVAhealthbenefitsexpansiontotesttheimpactofpublichealthinsuranceonlaborsupply.Wefindthatolderworkersaresignificantlymorelikelytodecreaseworkbothontheextensiveandintensivemarginsafterreceivingaccesstonon-employerbasedinsurance.Workerswithsomecollegeeducationoracollegedegreearemorelikelytotransitionintoself-employment,aresultconsistentwith“job-lock”effects.However,less-educatedworkersaremorelikelytoleaveself-employment,aresultsuggestingthatthepositiveincomeeffectfromreceivingpublicinsurancedominatesthe“job-lock”effectfortheseworkers.Somerelativelydisadvantagedsub-populationsmayalsoincreasetheirlaborsupplyaftergaininggreateraccesstopublicinsurance,consistentwithcomplementarypositivehealtheffectsofhealthcareaccessordecreasedworkdisincentivesforthesegroups.Weconcludethatthisreformhasaffectedemploymentandretirementdecisions,andsuggestthatfuturemovestowarduniversalcoverageorexpansionsofMedicarearelikelytohavesignificantlabormarketeffects.

ArticleOutline

1.Introduction

2.Predictedeffects

3.DescriptionofVAprogram

4.Dataandempirics

4.1.Data

4.2.Mainspecification

4.3.Identificationassumptions

5.Results

5.1.Mainresults

5.2.Intensityoftreatmentandjointlabormarkettransitions

5.2.1.Intensity

5.2.2.Jointlabormarkettransitions

5.3.Robustnesschecks

6.Implicationsanddiscussion

7.Conclusion

Acknowledgements

References

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Twofaultdetectionandisolationschemesforrobotmanipulatorsusingsoftcomputingtechniques OriginalResearchArticle

AppliedSoftComputing,Volume10,Issue1,January2010,Pages125-134

TolgaYüksel,AbdullahSezgin

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Abstract

Withgrowingtechnology,faultdetectionandisolation（FDI）havebecomeoneoftheinterestingandimportantresearchareasinmoderncontrolandsignalprocessing.AccomplishmentofspecificmissionslikewastetreatmentinnuclearreactorsordatacollectioninspaceandunderwatermissionsmakereliabilitymoreimportantforroboticsandthisdemandforcesresearcherstoadaptavailableFDIstudiesonnonlinearsystemstorobotmanipulators,mobilerobotsandmobilemanipulators.

Inthisstudy,twomodel-basedFDIschemesforrobotmanipulatorsusingsoftcomputingtechniques,asanintegratorofNeuralNetwork（NN）andFuzzyLogic（FL）,areproposed.BothschemesuseM-ANFISforrobotmodelling.Thefirstschemeisolatesfaultsbypassingresidualsignalsthroughaneuralnetwork.Thesecondschemeisolatesfaultsbymodellingfaultyrobotmodelsfordefinedfaultsandcombiningthesemodelsasageneralizedobserversscheme（GOS）structure.Performancesoftheseschemesaretestedonasimulatedtwo-linkplanarmanipulatorandsimulationresultsandacomparisonaccordingtosomeimportantFDIspecificationsarepresented.

ArticleOutline

1.Introduction

2.Literatureoverview

3.FDIschemeusingM-ANFISandNN

3.1.ResidualgenerationwithM-ANFIS

3.2.ResidualevaluationwithNNwithresilientpropagation

4.FDIschemeusinggeneralizedobserverswithM-ANFIS

5.Simulationresults

5.1.Case1:

FDIwithM-ANFISandNN

5.2.Case2:

FDIwithgeneralizedobserverscheme

6.Comparisonandconclusions

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Simultaneouscontroloftrafficlightsandbusdepartureforpriorityoperation OriginalResearchArticle

TransportationResearchPartC:

EmergingTechnologies,Volume18,Issue3,June2010,Pages288-298

LuizAlbertoKoehler,WernerKrausJr

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Abstract

Thisarticlepresentsabusprioritymethodfortrafficlightcontrolbasedontwomodesofoperation:

immediateandcontrolleddeparture.Theimmediatedeparturemodeisastandardprocedureinwhichtheintersectioncontrollergrantspriorityuponrequestofthebus.Controlleddepartureactstoavoidasecondstopofthebusattheendofthequeueformedduringredbyholdingthebusatthebusstop,whilestillgrantingprioritytothebuslane.Selectionofoneofthetwomodesisbasedonintersectioncostthatincludesbusdelayandtheimpactontheoveralltrafficneartheintersection.Themethodisappliedinaconstantcyclescenariowheregreenrecallandgreenextensioncanonlybegrantedwithincertainlimits.Numericalexamplesillustratetheapplicationoftheapproach.

ArticleOutline

1.Introduction

2.Busdeparturemanagementsystem

3.Mathematicalmodeloftheintersectioncontrolproblem

3.1.Trafficflowpattern

3.2.Intersectioncostfunction

4.Busdeparturecontrolwithpriority

4.1.Casefortd_nom

td_max（caseII）

4.2.Casefortd_max < tr

td_ext（caseIII）

4.3.Casefor0 < tr < td_nomortd_ext < tr

C（caseIaandIb）

5.Immediate（id）andcontrolled（cd）departureoperation

5.1.Immediatedeparture（ID）operation（caseIa）

5.2.Controlleddeparture（CD）operation（caseIa）

5.3.Choiceofdeparturemode（ID × CD）（caseIa）

5.4.Caseforpriorityrequestatr1nom < tr < td_nom（caseIb）

5.5.Sparecapacityforpreventingtrafficdisruption

6.Concludingremarks

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Two-timescalelearningusingidiotypicbehaviourmediationforanavigatingmobilerobot OriginalResearchArticle

AppliedSoftComputing,Volume10,Issue3,June2010,Pages876-887

AmandaM.Whitbrook,UweAickelin,JonathanM.Garibaldi

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Abstract

Acombinedshort-termlearning（STL）andlong-termlearning（LTL）approachtosolvingmobile-robotnavigationproblemsispresentedandtestedinboththerealandvirtualdomains.TheLTLphaseconsistsofrapidsimulationsthatuseageneticalgorithmtoderivediversesetsofbehaviours,encodedasvariablesetsofattributes,andtheSTLphaseisanidiotypicartificialimmunesystem.ResultsfromtheLTLphaseshowthatsetsofbehavioursdevelopveryrapidly,andsignificantlygreaterdiversityisobtainedwhenmultipleautonomouspopulationsareused,ratherthanasingleone.Thearchitectureisassessedundervariousscenarios,includingremovaloftheLTLphaseandswitchingofftheidiotypicmechanismintheSTLphase.ThecomparisonsprovidesubstantialevidencethatthebestoptionistheinclusionofboththeLTLphaseandtheidiotypicsystem.Inaddition,thispapershowsthatstructurallydifferentenvironmentscanbeusedforthetwophaseswithoutcompromisingtransferability.

ArticleOutline

1.Introduction

2.Backgroundandmotivation

3.Testenvironmentsandproblems

4.Long-termlearning（GA）systemarchitecture

4.1.Antigensandantibodies

4.2.GAsystemstructure

4.3.GAdetails

4.4.Reinforcementlearninginthelong-termlearningphase

5.Short-termlearning（AIS）systemarchitecture

5.1.Creatingtheparatopeandidiotopematrices

5.2.Antibodyselectionprocess

5.3.Reinforcementlearningwithintheshort-termlearningphase

6.Experimentalproceduresandresults

6.1.Long-termlearninggeneralprocedures

6.2.Measuringantibodydiversity

6.3.Long-termlearningphaseresults

6.4.Short-termlearninggeneralprocedures

6.5.Short-termlearningphaseresults

6.6.Representationoftheantigenspace

6.7.Discussion

7.Conclusions

Acknowledgements

References

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Designoflimited-stopservicesforanurbanbuscorridorwithcapacityconstraints OriginalResearchArticle

TransportationResearchPartB:

Methodological,Volume44,Issue10,December2010,Pages1186-1201

CarolaLeiva,JuanCarlosMuñoz,RicardoGiesen,HomeroLarrain

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Abstract

Inhigh-demandbusnetworks,limited-stopservicespromisebenefitsforbothusersandoperators,and

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