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英文文献原文
编号:
毕业设计外文翻译
(原文)
学院:
机电工程学院
专业:
机械设计制造及其自动化
学生姓名:
何源
学号:
1201110310
指导教师单位:
机电工程学院
姓名:
郭中玲
职称:
高级工程师
2016年6月3日
RoboticsandComputer-IntegratedManufacturing
Duetoageneralshiftinmanufacturingparadigmfrommassproductiontowardsmasscustomization,reconfigurableautomationtechnologies,suchasrobots,arerequired.However,currentindustrialrobotsolutionsarenotoriouslydifficulttoprogram,leadingtohighchangeovertimeswhennewproductsareintroducedbymanufacturers.Inordertocompeteonglobalmarkets,thefactoriesoftomorrowneedcompleteproductionlines,includingautomationtechnologiesthatcaneffortlesslybereconfiguredorrepurposed,whentheneedarises.Inthispaperwepresenttheconceptofgeneral,self-assertingrobotskillsformanufacturing.Weshowhowarelativelysmallsetofskillsarederivedfromcurrentfactoryworkerinstructions,andhowthesecanbetransferredtoindustrialmobilemanipulators.Generalrobotskillscannotonlybeimplementedontheserobots,butalsobeintuitivelyconcatenatedtoprogramtherobotstoperformavarietyoftasks,throughtheuseofsimpletask-levelprogrammingmethods.Wedemonstratevariousapproachestothis,extensivelytestedwithseveralpeopleinexperiencedinrobotics.Wevalidateourfindingsthroughseveraldeploymentsofthecompleterobotsysteminrunningproductionfacilitiesatanindustrialpartner.Itfollowsfromtheseexperimentsthattheuseofrobotskills,andassociatedtask-levelprogrammingframework,isaviablesolutiontointroducingrobotsthatcanintuitivelyandontheflybeprogrammedtoperformnewtasksbyfactoryworkers.
1.Introduction
Inordertoremaincompetitiveinaglobalizedenvironment,manufacturingcompaniesneedtoconstantlyevolvetheirproductionsystemsandaccommodatethechangingdemandsofmarkets.Currently,productionisexperiencingaparadigmshiftfrommassproductiontomasscustomizationofproducts.Theimpactofthistrendonproductionsystemsisthattheyshouldadapttohandlemoreproductvariation,smallerlifecycles,andsmallerbatchsizes–ideallybatchsize1.Today,robot-basedproductionisanessentialpartoftheindustrialmanufacturingbackbone.However,theconceptofanindustrialrobotstaticallyplacedinacellandcontinuouslyrepeatingacarefullypredefinedsequenceofactionshasremainedpracticallyunchangedformanydecades.Notsurprisingly,typicalindustrialrobotsarenotflexibleandthussuchadegreeoftransformableproductionisbeyondthecapabilitiesofcurrentsystems.
AsimplifiedillustrationofthesituationisshowninFig.1.Traditionalmanufacturingsystemsareautomatedtoalargedegree,butcanonlybereconfiguredwithgreatdifficulty.Ontheotherhand,traditionalmanuallaborisveryflexible,butnoteconomicallyviableforlargescaleproduction,especiallysoinhighwagecountries.Inthefuture,masscustomizationwillmakeitnecessarytocombinehighreconfigurabilitywithalargedegreeofautomation.Thisgoalcanbeachievedintwodistinctways:
workerscaneitherbeequippedwithbetterautomationtools,suchasintuitiveon-the-flyprogrammingofrobots,therebyincreasingtheirproductivity,orthereconfigurabilityoftraditionalautomatedproductionlinescanbeimproved,e.g.throughtheuseofmultipurposerobots.
Fig.1.Aclearrequirementforthefactoriesoftomorrowisthatofatransformableproductionsystem,showingahighdegreeofflexibility,whilestillapplyingahighdegreeofautomation.
Roboticsisexpectedtobeoneofthemainenablersofthistransitiontothetransformablefactoryoftomorrow.Toreachthedemandedlevelofflexibilityrobots,ormoregenerallymobilemanipulators,needtobeabletomoveautonomously,copewithuncertaintyininteractionswithhumansandpartiallyknownenvironments,handleavarietyofdifferenttasks,andbeabletobereprogrammedfastbynon-robotexpertswhenanewtaskinthefactoryarises.
So,thequestionemerges:
Whatshouldbethecharacteristicsofaframeworkthatwouldallowrobotstobeflexibleenoughtohandleuncertaintyandchangingproductiontasks,whilebeingintuitiveenoughtobeusedandre-programmedbynon-robotexperts?
Wearguethattheanswerliesinthetightintegrationofsensingandactionwithinasmallsetofmodularandparametrizable“robotskills”.Contrarytotraditionalrobotmacros,ourskillsarecharacterizedasbeinggeneral,inthattheycanhandleavarietyofobjects,andself-asserting,inthattheycontainpre-andpostconditionchecks.Finally,thissetofskillsforagivenindustrycanbenaturallyextractedfromacarefulanalysisofindustrialstandardoperatingprocedures(SOP).
Inthisworkwepresentaconceptualmodelofthe“skills”andhowthisconceptcanbeimplementedandsuccessfullyappliedinreal-worldindustrialscenarios.Theconductedexperimentsshowthatasmallsetofskillscanbeintuitivelyparametrizedbyeitherkinestheticteaching,pointinggestures,orautomatictaskplannerstosolvedifferentindustrialtasks,suchaspartfeeding,transportation,qualitycontrol,assembly,andmachinetending.
Themaincontributionsofthispaperarethat?
Weproposeaconceptualmodelofrobotskillsandshowhowthisdiffersfromtraditionalmacros.
Weshowhowthisapproachcanenablenon-expertstoutilizeadvancedroboticsystemsbyencapsulatingadvancedcapabilities,implementedaprioribyexperts,insideeachindividualskill.
Concreteapplicationsoftheapproacharepresented,inwhichadvancedrobotsystemshavebeenappliedinseveralrealindustrialscenarios.
1.1.Relatedwork
Thegoalofachievingeasyandquickreprogrammingofrobotsbynon-expertshasbeenconsistentlypursuedusingthetask-levelprogrammingparadigm.Task-levelprogrammingconstitutesahigherabstractionlevelthantraditionalrobotprogramming.Itspecifiesagiventaskasasequenceofactions,butavoidsdescribingeachactioninfulldetail.Thesequenceofactionsleadingtothefulfillmentofthistaskcaneitherbeplannedorspecifiedbyanoperator.SuchanapproachissimilartothesymbolicrepresentationoftasksoftheShakeyrobotthatusedaSTRIPSplannertoreasonaboutwhichactionsmayleadtoaccomplishingagoal,ortheconceptofObject-ActionComplexes(OACs)thatallowedcognitiverobotstoidentifypossibleactionsbasedonobjectaffordances.
Task-levelprogrammingisbasedonlowerlevelentities,usuallycalledrobotskills,thatinstantiateactions.Variousrepresentationsofrobotskillsthatwouldbesuitablefortask-levelprogramminghavebeenpursuedduringthelastdecades.Whatmostoftheaforementionedattemptshaveincommon,isthatrobotskillsare,inturn,composedofprimitivesetsofrobotmotions,calledactionormotionprimitives.Theseskillprimitivesaresimple,atomicrobotmovementsthatcanbecombinedtoformmorecomplexbehaviors.Asystematicsurveyoftherichrelevantliteraturerevealshowfragmentedtheconceptofskillsis,lackingawidelyaccepted,strictdefinition.
Incontrasttotheskillsthemselves,theskillprimitivesareratherwelldefinedintheroboticscommunity;althoughseveraldifferentdescriptionsexist,mostofthemlooselyfollowMason'sworkoncompliantcontrollers,whichpavedthegroundfortheTaskFrameFormalism(TFF)introducedbyBruyninckxandDeSchutter.Recentworkhasexpandeduponthisidea,forinstanceenablingtheuseofanysensorandcontrollerinthesametaskframe(e.g.visualservoingcombinedwithforcecontrol),andoperatinginadynamictaskframe.
Ourdefinitionofskillsintegratespre-andpost-conditionschecksintheexecution,whichmakestheinterconnectionofskillsintotasksanintuitiveandcontrolledprocess.Inthatsense,ourapproachiscomparabletothemanipulationprimitivenets.However,thisapproachrequireslow-levelparametrizationofthemanipulationprimitives,makingitaccessibleonlytorobotexperts.Anotherconcept,similartoournotionofskills,isthatofActionRecipesasdefinedintheRoboEarthproject.Differenttoourskillsthough,ActionRecipeskeepsensingandactingseparateandmainlyfocusonknowledgesharingbetweenrobotswithsimilarcapabilities.Alltheaforementionedapproachesrequirearobotexperttoprogramandusethesystem.Thisisalsooneofthereasonswhytheseworks,incontrasttoours,wereneverdeployedinarealindustrialsetting.
Easyprogrammingofindustrialrobotsrequires,apartfromtheunderlyingconcepts,intuitiveHuman–RobotInteraction(HRI)mechanisms.HRIinindustrialsettingsfocusesonofflineprogrammingbyusingCADmodelsoronlineprogrammingusingaugmentedreality(AR).Theapparentlimitationofsuchmethodsisthattheyassumethepresenceofanexpertforreprogramming,whichmakesthemunsuitablefortheenvisionedtransformablefactories.Incontrasttothemajorityofpublishworksandsimilartoourapproach,theauthorsofcombinedskillprimitivesandspeechrecognitionforprogramminggraspingtasksbyspeech,withotherwork.Thisworkincorporatestask-levelprogramming,however,theprogrammingisacombinationofprimitives,withnohigher-levelskillsbetweentheseandthetasklayer.Ingeneral,theliteratureofHRIinindustrialsettingsisratherlimited.Onepossiblereasonisthatindustrialroboticsapplicationstraditionallyfocusedonaccuracyandspeedwithouttheneedforexternalsensors.However,latelythistrendisgivingwaytomoreintuitiveanduser-friendlyinterfacesthatlargelyrelyonsensing.Furthermore,giventhenatureoffactoriesofthefuture,thatareonlysemi-structured,thereisahighneedforaccurate,externalsensorsaswellasintuitiveHRI.