Novel idea of charging car users for using bus lanes.docx

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Novelideaofchargingcarusersforusingbuslanes

Intelligentomni-directionalvision-basedmobilerobotfuzzysystemsdesignandimplementation  

ExpertSystemswithApplications,Volume37,Issue5,May2010,Pages4009-4019

Hsuan-MingFeng,Chih-YungChen,Ji-HweiHorng

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AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences

Abstract

Anevolutionalparticleswarmoptimization（PSO）-learningalgorithmisproposedtoautomaticallygeneratefuzzydecisionrules.Duetothedevelopmentofthefuzzyrule-basedsystem,itactuallyregulatestheomni-directionalvision-basedmobilerobotforobstacleavoidanceanddesiredtargetapproximationassoonaspossible.Intheproposedimageprocessingalgorithm,animagedirecttransformationmethodisappliedtoconverttheomni-directionalsceneintopanoramicnormal-view.Thus,theobjectspositionsofobstacleandtargetaredetectedbytheproposedcolorimagesegmentation.Humanknowledge-basedfuzzysystemsdemonstratetheirwelladaptabilityfornonlinearandtime-variantfeaturesofthemobilerobottoactuallyapproachthedesiredlocationwhateveritissurroundedinaknownorunknownenvironment.Insoftwaresimulations,theomni-directionalmobilerobotcanmovetowarddesiredtargetsfromdifferentinitialpositionsandvariousblocksizes.Inhardwareimplementations,thefuzzycontrolsystemembeddedinactualmobilerobotplatformisusedtoreal-timemanipulatetheomni-directionalwheelsthroughthemotordriversbythecapturedimagepositionsoftheobstacleandtarget.Theselectedfuzzyrulesareefficienttocontrolthedirectionandspeedofomni-directionalwheelstoachievethedesiredtargets.

ArticleOutline

1.Introduction

2.Architectureofvision-basedfuzzymobilerobotsystem

2.1.On-lineimagedetectingstagedesign

2.2.Robotmodelingstagedesign

3.Mobilerobotsimulationsandimplementations

3.1.Softwaresimulations

3.2.Hardwareimplementation

4.Conclusions

Acknowledgements

References

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2

Kinematicparametercalibrationofacar-likemobilerobottoimproveodometryaccuracy  OriginalResearchArticle

Mechatronics,Volume20,Issue5,August2010,Pages582-595

KooktaeLee,WoojinChung,KwanghyunYoo

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Abstract

Odometryprovidesfundamentalposeestimatesforwheeledvehicles.Foraccurateandreliableposeestimation,systematicandnonsystematicerrorsofodometryshouldbereduced.Inthispaper,wefocusonsystematicerrorsourcesofacar-likemobilerobot（CLMR）andwesuggestanovelcalibrationmethod.KinematicparametersoftheCLMRcanbesuccessfullycalibratedbyonlyacoupleoftestdriving.Afterreducingdeterministicerrorsbycalibration,odometryaccuracycanbefurtherimprovedbyredundantodometryfusionwiththeextendedKalmanfilter（EKF）.Odometryfusionreducesnonsystematicorstochasticerrors.Experimentalverificationsarecarriedoutusingaradio-controlledminiaturecar.

ArticleOutline

1.Introduction

2.Calibrationofsystematicodometryerrors

2.1.KinematicsandsystematicerrorsourcesoftheCLMR

2.2.OdometryoftheCLMR

2.3.Testtrackforodometrycalibration

2.4.TypeAerror:

uncalibratedtread

2.5.TypeBerror:

uncalibratedwheeldiameter

2.6.Systematicerrorcalibrationequation

2.7.Theproposedsystematicerrorcalibrationscheme

3.Reductionofthenonsystematicerror

3.1.Systemmodel

3.2.Measurementmodel

3.3.TheEKFalgorithm

3.3.1.Predictionstage

3.3.2.Correctionstage

4.Simulationsandexperiments

4.1.Simulations

4.2.Experimentalsystemsandsteeringcalibration

4.3.Systematicerrorcalibrationresults

4.4.Nonsystematicerrorreductionresult

5.Conclusion

Acknowledgements

References

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3

Anovellow-cost,limited-resourceapproachtoautonomousmulti-robotexplorationandmapping  OriginalResearchArticle

RoboticsandAutonomousSystems,Volume58,Issue2,28February2010,Pages186-202

ChristopherM.Gifford,RussellWebb,JamesBley,DanielLeung,MarkCalnon,JosephMakarewicz,BryanBanz,ArvinAgah

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Abstract

Mobilerobotsarebecomingmoreheavilyusedinenvironmentswherehumaninvolvementislimited,impossible,ordangerous.TheserobotsperformsomeofthemorelaborioushumantasksonEarthandthroughoutthesolarsystem,simultaneouslysavingresourcesandofferingautomation.Higherlevelsofautonomyarealsobeingsoughtintheseapplications,suchasdistributedexplorationandmappingofunknownareas.