英文文献-基于视觉的轧钢机位置控制系统的设计

452Proceedingsofthe26thChineseControlConferenceJuly26-31,2007,Zhangjiajie,Hunan,ChinaADesignofVision-basedLocationControlSystemforSteelRollingMillChenWei,FangKangling,LiuXinhaiCollegeofInformationScienceandEngineering,WuhanUniversityofScienceandTechnology,Wuhan430081,P.R.ChinaE-mail:,,Abstract:Thispaperdescribesaclosedloopcontrolsystembasedonmachinevisioninthepositioncontrolofthehotbilletofsteel.Itisbasedonanindustrialcameraasavisualsensorthatprovidesthepositioninformationofthesteelbilletsintheheatingkiln,andisusedtodetectthemovingbillets.Tomeettherequirementofarobust,real-timedetectionofmovingbillet,thispaperusesthesimplybackgroundsubtractionmodeltodetectthemovingbillets.Thedifferencegrayprofileprojectisappliedtodetecttheedgeofthebillet,andtheeffectiswell.Inthepracticalapplication,thevideocontrolsystemhasobtainedgoodcontrolperformanceundertheindustrialenvironment.KeyWords:MachineVision,ObjectLocation,BackgroundSubtraction,FeedbackControl1INTRODUCTIONIntheSteelMill,thepreprocessingofsteelbilletsisheatedto10001200intheheatingkiln,beforetheyarerolledflat-blocks.Thelayoutofbilletsinthekilnistwobilletsinonerow.Becausethelengthofthekilnis27.9m,andthewidthofthekilnis12.9m.Theoperatorscannotobversethemovingofbilletsinthekilndirectly.Inordertoavoidtocollidingamongthebillets,thepositionofmovingbilletsshouldbecontrolledwhenthebilletsaresentintotheheatingkiln.Furthermore,thecommoncontactpositionsensorscannotaffordthehightemperatureinthekiln,orisdestroyedbythecollidingofthebilletseasily.Thefeasiblemethodisanon-contactdetectionmethod:acamerawhichissetonthesideofthekiln(showsinFig.1)obtainstheinformationofthebillets.Fig.1ThecameraissetonthekilnThecamerahas610170resolutionandcanacquire30fullframespersecond.Plantoperatorsusethevideocamerawhentheymonitorandcontrolthepositionofbillet.ButhumanoperatorsadjustthepositionofbilletsbasedontheobservationbyindustrialTVissubjective,experience-dependent,andlaborintensive5.Therearesomeproblemsinpracticaloperation,suchasthelowprecision(2minute),andsoon.2THEPROBLEMANDCONTROLSYSTEMThereisaprobleminthepositioncontrolofthebillets.Thatis,therearetwobilletsinonerow;sotherearetwodistanceswhichmustbecontrolledinthepositioncontrolofthebillets.OneisthedistancebetweenthebilletAandthebilletB.Anotheristhedistancebetweenbilletandthewallofkiln(showsFig.2).Fig.2ThepositioncontrolofbilletsDiscrepanciesbetweenbilletAandbilletB,orbetweenthebilletsandthekiln,whichisconsecutivestands,canleadtothecollidingofthebilletswhentheymoveinthekiln.Ifoneofthemexceedscertainlimits:thisleadstoacollisionbetweentwobillets,orbetweenthewallofkilnandthebilletsduringthebilletsmoving.Sometimescatastrophicfailureoccurswiththecollisions:itleadstoacompleteinterruptionofproductionandrequiresthepositionofbilletstobeadjustedbyhand.Inordertosimplifythecontrolproblem,wechoosethecenterofthekilnisthemiddlepointofposition.Thenwecangettheexpecteddeviationis1(2)3Ls=(1)whereListhewidthofkiln,sisthelengthofbillets,istheexpecteddeviationbetweenthekilnandthebillet,orbetweenthebilletandthebillet.Thecamerainthekilnisregardedasavisualsensorinthevision-basedcontrolsystem,thusthebilletofpositioncanbefeedbacktothePIDcontroller.Sincethecamerahasafiniteview,therearetwofeedbacksignals.Oneisthevideosignal;anotheristhesignalofimpulsecounter.Fig.3showstheframeofthevision-basedlocationcontrolsystem.453Fig.3Theframeofvision-basedlocationcontrolsystem3THEVIDEOPOSITIONALGORITHMThemostcommonmethodtodetectmotioninarunningsequenceofimagesisbackgroundsubtractingthatisachievedbytakingabsolutedifferencesbetweeneachincomingframeandabackgroundmodelofthescene2,3.However,themethodissensitivetochangesofdynamicsceneduetolightingandextraneouseventswhichmayreducefalsedetection.Inordertosolvetheproblem,weuseadifferencegrayprofileprojectalgorithmtodetectthepositionofbillets8.3.1Imagede-noisingTherearesomanydisturbancesintheindustrialfieldthatthequalityoftheimagecapturedisbad.Moreover,thereisnotafixedlightinginthekiln.Thelightwouldbechangewiththechangeoftemperature.Conventionally,thefirststepofimageprocessingistoeliminateortodecreasetheinfluenceofnoise.Becauseoftheharshenvironmentinthekiln,itdoesnteliminatenoisethoroughly.Therearealotofnoisestilloccasionallyappearsintheimagecaptured(showsFig.3).Inordertoprocesstheimagereal-time,weonlytakeasimplyde-noisingbyhardwaremethod.Theexperimentalresultsshowedthatmeetstherequestofimageprocessinginusingthebackgroundprojectsubtractionalgorithm.Fig.4Thetypicalimagecapturedbythecamera3.2ExtractingfeatureandedgeDifferenceisaneffectivemethodinthedetectingofamovingobject.Thereisahighcorrelationintheseriesimage.Ifthecorrelationcanbereduce,themovingtargetiseasiertobesegmentedfromthebackground.Thedifferentialalgorithmcanreducethecorrelationoftheseriesframeofvideos.Let),(jifBisabackgroundimage,and),(jifkisthecurrentimage(,3,2,1=K).Thenthedifferentimage),(jifcis:),(),(),(jifjifjifkBc=(2)Astatisticmethodhasbeenappliedtodetecttheedgeofbillet.Itisknownthatthecolumngrayvalueofthemovingregionwouldbesignificantlydifferentfromthecolumngrayvalueofthebackgroundimage,whentheobjectmovesinthescene.Fig.5showsthechangeofcolumnverticalprojectiongrayvalueoftheimage.Whenthereisamovingobjectwhichappearsintheimage,ordoesntappearintheimage;thenthegrayvaluewillchangestronglyatpoint2tor3t.Fig.5ThechangeofgraywhenthebilletmovesLetthecolumnprojectiongrayvalueofthedifferentimageis:=11),(1),(MicjcjifMjif(3)where),(jifjcisthejthcolumnverticalprojectiongrayvalue.BecausethefinalpositionofthebilletAisthebackedge,thelefthandsideofimageisarelativemovingregion(thegray-scalechangesslowly).However,thefinalpositionofthebilletBistheleadingedge,therighthandsideofimageisarelativemovingregionforthebilletB.Thetechniqueofslidingwindowisusedtoextractthefeatureofedgeforimprovingthereal-timeandtheprecisionofthebilletslocation.Theslidingwindowisa170hwindow,whereh(50)isanadjustingcoefficient.Therearemanydisturbancesintheindustrialcondition.Itimprovestherobustofalgorithmtocomputethechangeofeverycolumnverticalprojectiongrayvalueandthechangeofdifferencebetweentwocolumnsintheslidingwindow.Let1Thisthepeakgraythreshold,2Thisthecolumndifferentgraythreshold,then111(,),then1jcfijThFF=+(4)2(,)(,),jjccfijfitjTh+then221FF=+(5)where1Fisthenumberofchangingcolumn,2Fisthenumberofcolumndifferentchanging,tisthenumberofslidingcolumn.Accordingtothetheoryofnearestneighbordecision1,thediscriminatedfunctionis:2212()()MFaFbTh=+(6)whereThisthefeaturechangethreshold,Vab=isthefeaturevectorofedgewhentheedgeofbillet454appearsintheimage,whichgetsthroughtrainingoff-line.Whenthediscriminatedfunctionismin,theedgeofbilletappearsintheslidingwindow.Thenthemovingdistanceofbilletsis:chiLFF=(7)whereiListhemovingdistanceduringthesampletimeintervalt,cFisthemaxchangingcolumnofcurrentframe,hFisthemaxchangingcolumnofformerframe.3.3BackgroundMaintenanceThispaperusesaperiodicalupdatingthebackgroundstrategytospeeduptheimageprocessing.Thebackgroundupdatingalgorithmadoptsthefirst-orderrecursivefiltermethodisusedtointegratenewincominginformationtothecurrentbackgroundimage4.ThebackgroundupdateseveryMframeas:(,)(1)(,)(,1)BkBfijkfijfijk=+(8)where(,)Bfijkisthebackgroundimage,isasmallpositivenumber,(,)kfijisthecurrentimage.4THECONTROLALGORITHMBecausetheweightofabilletis1800,theinertiaofthebilletisaanotherprobleminthepositioncontrol.Itmakesthebilletstoslidewhenthesteppermotorhasstopped.Thispaperadoptsatwostagecontrolstrategytosolvetheslidingproblemofthebillets.Firstly,thebilletsarerapidlylocatedatsomecertainpositioninthekiln.Thenthebilletsarepreciselylocatedtheexpectedpositionbyavision-basedfeedbackcontrolalgorithm.TheflowoflocationcontrolalgorithmisillustratedinFig.6.HardwareDe-noisingDetectingEdgeCapturetheimageRenewtheBackgroundNYDifferimageSlidewindowExtractingfeaturePIDFastLocationAlgorithmExpectedPosition1?NYNYCapturetheimageHardwareDe-noisingDifferimageSlidewindowExtractingfeaturePIDPreciselyLocationAlgorithmExpectedPosition?TheEndBilletLocationFig.6TheflowoflocationcontrolalgorithmFig.7,8showstheresultoffastlocationandthepreciselocation.Fig.7TheresultoffastlocationFig.8Theresultofpreciselocation5APPLICATIONAANDCONCLUDEThevisionlocationcontrolsystemwhichdescribesinthisarticlehasbeensuccessfullyputintopracticalapplicationfortwoyears,andobtainedgoodcontrolperformanceundertheindustrialenvironment(showsFig.8).Fig.9showstheinterfaceofcontrolsystem.Fig.9TheinterfaceofcontrolsystemTab.1showsthecontroleffectofthevideofeedbackcontrolsystem.Tab.1TheErrorrateofposition(5cm)Errorrate/%Oldcontrolsystem76Videofeedbackcontrolsystem9697Thevision-basedlocationcontrolsystemcandetecttheedgeofthemovingbilletsintheframeofimage.Statisticalprojectmethodusestodetecttheedgeofthemovingbillets.Theresultofpracticaloperationprovesthatthedifferencegrayprofileprojectalgorithmisfeasibleundertheindustrialenvironment.Butthevision-basedlocationcontrolsystemmaybemakingamistakebecauseofverypooroperateenvironment,becausethedifferencebetweentheobjectandthebackgroundisnotsignificant.455Thereal-timeandrobustofmovingobjectdetectingalgorithmincomplexindustrialconditionshouldneedtoresearchfurther.REFERENCES1Bianzhaoqi,Zhangxuegong.Patter