[机械模具数控自动化专业毕业设计外文文献及翻译]【期刊】解耦,分布式AUV控制架构-外文文献

A decoupled, distributed AUV control architectureStefanB.Williams,PaulNewman,GaminiDissanayake,JulioRosenblatt,HughDurrant-WhyteAustralianCentreforFieldRoboticsUniversityofSydneyNSW2006,AustraliaAbstractCurrentworkonunderseavehiclesattheAustralianCentreforFieldRoboticsconcentratesonthedevelop-mentofterrain-aidednavigationtechniques,sensorfu-sionandvehiclecontrolarchitecturesforreal-timeplat-form control. Accurate position and attitude estima-tion and control methods use information from scan-ning sonar to complement a limited vehicle dynamicmodelandunobservableenvironmentaldisturbances. Inthis paper we present the vehicle control architecturecurrentlyrunningontheOberonsubmersible. Thisar-chitecture is based on the Distributed Architecture forMobile Navigation. We use a distributed, decoupledcontrol paradigm to facilitate the tuning of individualcontrolmodes. Anumberofbehaviourshavebeencre-atedtodirectthemotionofthevehicle. Weshowthatwhen coupled with a low-level terrain-aided navigationscheme,eectivecontrolofthevehiclecanbeachieved.I. IntroductionCurrentworkonunderseavehiclesattheAustralianCentre for Field Robotics concentrates on the devel-opment of terrain-aided navigation techniques, sensorfusion and vehicle control architectures for real-timeplatformcontrol. Accuratepositionandattitudeesti-mationandcontrolmethodsuseinformationfromscan-ning sonar to complement a limited vehicle dynamicmodel and unobservable environmental disturbances.Keyelementsofthisworkincludethedevelopmentofsonar feature models, the tracking and use of thesemodels in mapping and position estimation, and thedevelopmentoflow-speedplatformmodelsusedinve-hiclecontrol.One of the key technologies being developed in thecontextofthisworkisSimultaneous Localisation andMap Building (SLAM). The robot typically starts atan unknown location with no a priori knowledge oflandmarklocations. Fromrelativeobservationsofland-marks, it simultaneously computes an estimate of ve-hicle location and an estimate of landmark locations.While continuing in motion, the robot builds a com-pletemapoflandmarksandusesthesetoprovidecon-tinuousestimatesofvehiclelocation.Map building represents only one of a number offunctionalitiesthatarerequiredinorderforarobottooperateautonomouslyinadynamicenvironment. AnAUVmustalsohavetheabilitytomakedecisionsaboutthecontrolactionstobetakeninorderforittoachieveitsgoals. Thesegoalsmaychangeasthemissionpro-gresses and there must be a mechanism in place forthe robot to deal with unforeseen circumstances thatmayoccurduringthecourseofamission. Wehavede-veloped a distributed, decoupled control architecturebasedontheDistributedArchitectureforMobileNav-igation(DAMN)asproposedin10.Inthispaperwepresentthevehiclecontrolarchitec-turecurrentlyusedontheOberonsubmersiblefollowedbyresultsoftheapplicationofSimultaneousLocalisa-tionandMapbuildingtoestimatethemotionoftheve-hicle. SectionIIpresentsthevehiclesensorsandactu-atorswhilesectionIIIdescribesthedistributed,decou-pledcontrolschemecurrentlyoperatingonthevehicle.InSectionIVwepresentasummaryoftheSimultane-ousLocalisationandMapbuildingAlgorithm. InSec-tionVwedescribeaseriesoftrialsandshowtheresultsofapplyingthistechniquetodatacollectedinanaturalterrainenvironment. Finally,SectionVIconcludesthepaperbysummarizingtheresultsanddiscussingfutureresearchtopicsaswellason-goingwork.II. TheOberonVehicleTests of our underwater navigation techniques areperformed on a mid-size submersible robotic vehiclecalled Oberon designed and built at the Centre (seeFigure1). Thevehicleisequippedwithtwoscanninglowfrequencyterrain-aidingsonarsandacolourCCDcamera,togetherwithbathyometricdepthsensorsandaberopticgyroscope12. Thisdeviceisintendedpri-marilyasaresearchplatformuponwhichtotestnovelsensing strategies and control methods. Autonomousnavigation using the information provided by the ve-hicleson-boardsensorsrepresentsoneoftheultimategoalsoftheproject8.A. EmbeddedcontrollerAttheheartoftherobotcontrolsystemisanembed-dedcontroller. Figure2showsaschematicdiagramofthevehiclesensorsandtheirconnections. TheOberonrobotusesaCompactPCIsystemrunningWindowsNTthatinterfacesdirectlytothehardwareandisusedtocontrolthemotionoftherobotandtoacquiresensordata. While the Windows operating system doesntsupport hard real-time performance, it is suitable forsoft real-time applications and the wide range of de-Fig. 1. Oberon at Seavelopmentanddebuggingtoolsmakeitanidealenvi-ronment in which to test new navigation algorithms.Time-criticaloperations,suchassamplingoftheana-log to digital converters, are performed on the hard-waredevicesthemselvesandusemanufacturersupplieddevice drivers to transfer the data to the appropriateprocesses.EthernetPencilBeamScanningSonarFan BeamScanningSonarA/DFiberOpticGyroscopePressureTransucerD/A8Channel8ChannelServoMotorControllerServoMotorControllerServoMotorControllerServoMotorControllerServoMotorControllerTiltSensorSerialSerialPCI Bus PCI BusColourCCDCameraVideoFrameGrabberCoaxial CableTether to SurfaceSurface Computer Surface ComputerEmbeddedCompactPCIFig. 2. Vehicle System DiagramThe sensor data is collated and sent to the surfaceusinganethernetconnectionwhereanetworkofcom-puters are used for further data processing, data log-gingandtoprovidetheuserwithfeedbackaboutthestate of the sub. Communications between the com-putersatthesurfaceandthesubareviaatether. Thistetheralsoprovidespowertotherobot,acoaxialcablefor transmitting video data and a leak detection cir-cuit designed to shut o power to the vehicle in caseofaleak. Whilesomeeortmighthavebeenspentoneliminatingthetether,itwasfeltthatthedevelopmentofthenavigationaltechniqueswasofmoreimmediateinterest.B. SonarSonaristheprimarysensorofinterestontheOberonvehicle. There are currently two sonars on the robot.AnImagenexsonarunitoperatingat675kHzhasbeenmounted at the front of the vehicle. It is positionedsuch that its scanning head can be used as a forwardanddownwardlookingbeamThisenablesthealtitudeabove the sea oor as well as the proximity of obsta-clestobedeterminedusingthewideanglebeamofthesonar.The second sonar is a Tritech SeaKing. This is animagingsonarandhasadualfrequencynarrowbeamsonarheadthatismountedontopofthesubandisusedtoscantheenvironmentinwhichthesubisoperating.Itcanachieve360oscanratesontheorderof0.25Hz.The information returned from this sonar is used tobuildandmaintainafeaturemapoftheenvironment.C. InternalSensorsAn Andrews Laser Gyro has been included in theOberonrobottoallowtherobotsorientationtobede-termined. This sensor provides yaw rate informationandisusedtocontroltheheadingofthesub. Wecur-rentlyestimatethebiasinthegyroscopereadingpriorto a mission. The bias compensated yaw rate is thenintegrated to provide an estimate of vehicle heading.Because the yaw rate signal will inevitably be noisy,the integration of this signal will cause the estimatedheadingtodriftwithtime. Atpresent,missionsdonottypicallyrunforlongerthan30minutesandyawdriftdoesnotposeasignicantproblemonthistimeframe.In the future, a compass will be added to the systemtoallowustoperiodicallyresettheheadingforlongermissions. This will also allow us to detect changes inthe yaw rate bias that typically occur as a result ofchangesintheinternaltemperatureoftheunit.Apressuresensormeasurestheexternalpressureex-periencedbythevehicle. Thissensorprovidesavoltagesignal proportional to the pressure and is sampled byananaloguetodigitalconverterontheembeddedcon-troller. Thepressurecanthenbeconvertedtoadepthbelow the surface of the ocean. Feedback from thissensorisusedtocontrolthedepthofthesub.D. CameraA Panasonic camera in an underwater housing ismountedexternallyonthevehicle. Itisusedtoprovidevideo feedback of the underwater scenes in which therobotoperates. Thisisacolourcameraandsendsthevideo signal to the surface via the tether. A MatroxMeteorcardisthenusedtoacquirethevideosignalforfurtherimageprocessing.E. ThrustersTherearecurrently5thrustersontheOberonvehi-cle. Threeoftheseareorientedintheverticaldirectionwhiletheremainingtwoaredirectedhorizontally. Thisgivesthevehicletheabilitytomoveitselfupanddown,controlitsyaw,pitchandrollandmoveforwardsandbackwards. Thisnon-holonomicthrustercongurationdoesnotallowforlateralmotionbutthisdoesnotposeaproblemforthemissionsenvisagedforthisvehicle.III. VehicleControlSystemControlofamobilerobotinsixdimensionalspaceinanunstructured,dynamicenvironmentsuchasisfoundunderwatercanbeadauntingandcomputationallyin-tensiveendeavour. Navigationandcontrolbothpresentdicultchallengesinthesubseadomain. Wehavede-velopedadistributed,decoupledcontrolarchitecturetohelpsimplifythecontrollerdesign.Thevehiclecontrolarchitecturecurrentlyrunningonthe sub is based on the Distributed Architecture forMobileNavigation(DAMN)asproposedin10. Thisbehaviour-basedcontrolarchitectureusesacentralisedarbitertocombinevotesfromvariousbehavioursrun-ning in the system in order to determine the optimalcourse of action to pursue. A behaviour encapsulatesthe perception, planning and task execution capabili-ties necessary to achieve one specic aspect of robotcontrol,andreceivesonlythatdataspecicallyrequiredforthattask2.A. DecoupledcontrolBy decoupling the control problem, individual con-troller design is greatly simplied. In the case of theOberonvehicle, vertical motion iscontrolled indepen-dentlyofitslateralmotionusingtwoseparatePIDcon-trollers. Thesecontrollersarethentunedtoprovidetherequiredperformanceineachcase.Thisdivisionofcontrolhasbeenselectedasittsinwithmanyoftheanticipatedmissionstobeundertakenbythevehicle. AtypicalmissionmightseethevehicleperformingasurveyofanareaoftheGreatBarrierReefwhilemaintainingaxedheightabovetheseaoor12.The task of performing the survey can then be madeindependentofmaintaining thevehicle altitude. Thisalsoallowsustooptimizetheperformanceofthedepthcontrollerpriortodeployingthenavigationalgorithmsusedformappingoftheenvironmenttoensurethatthevehiclehaslittleriskofhittingtheseaoor.The behaviours that run on the vehicle are also di-videdintohorizontalandverticalbehavioursandthereareconsequentlytwoarbiterscurrentlypresent. Oneisresponsibleforsettingthedesireddepthofthevehiclewhiletheothersetsthedesiredyawandforwardosettoachievehorizontalmotion.+-a) Horizontal Low level controlYawdPIDYawOutput ofYawArbiterGyro Yaw RateHorizontalThrustersForward OffsetdOutput ofFwdOffsetArbiterKDepthdPIDDepthOutput ofDepthArbiterPressureSensorPressureVerticalThrustersb) Vertical Low level controlFig. 3. The low level control processes that run on the embeddedcontroller. These processes include processes for samplingthe internal sensor readings,computing the PID control out-puts and driving the thrusters.Forasurveymission,theverticalbehaviourswouldtypically be responsible for keeping the sub from col-lidingwiththeseaoor. Theverticalbehavioursthatrun on the vehicle include maintain minimum depth,maintainminimumaltitude,maintaindepthandmain-tainaltitude. Thecombinationoftheoutputsofthesebehaviours determines the depth at which the vehiclewill operate. A large negative vote by the maintainminimumaltitudebehaviourwillkeepthevehicleataminimumdistancefromtheseaoor.Thehorizontalbehavioursthatwouldrunonasur-vey mission include follow line (sonar and/or vision),avoidobstacles, movetolocationandperformsurvey.Thecombinationoftheoutputsofthesebehavioursde-termines the orientation maintained by the vehicle aswell as the forward oset applied to the two horizon-talthrusters. Thisallowsthevehicletomoveforwardwhilemaintainingitsheading.B. LowandHighLevelControlThecontrolofthesubisfurtherdecoupledintolow-level and high-level control processes. The low-levelprocessesrunontheembeddedcontrollerandareusedto interface directly with the hardware (see gure 3).Inaddition,thePIDcontrollershavebeenimplementedatthislevel. ThisallowsthePIDcontrollerstorespondquicklytochangesinthestateofthesubwithoutbeingaected by the data processing and high-level controlalgorithmsrunningatthesurface.The high-level processes run on a pair of Pentiummachines at the surface control station. Informationfrom the subs sensors is fed to a series of processesrunning on these machines. These processes use thedatasuppliedbythesensorstodeterminethedesiredsetpointsforthelow-levelcontrolprocesses. Therawsensordataispre-processedtoproducevirtualsensorinformation - information that is of interest to multi-SurveyAreaRaw PingsRange/BearingdYawImagingSonarGyroSLAMFeatureExtractionPressureSensorAltitudeForwardLookSonarProximity toObstacleMinimumAltitudeMaintainAltitudeMaintainDepthAvoidObstacleFollowLineMinimumDepthDepthArbiterYawArbiterYaw Votes/Fwd OffsetVotesDepth VotesYawdFwdOffsetdDepthdVisionLineTrackingVideo Orientation of LineMoveToPositionAltitudeObstacle DistancePositionEstimatesSensors Virtual Sensors Behaviours ArbitersCommands Sent toLow LevelControllersCommands Sent toLow LevelControllersMissionPlannerFig. 4. The high level process and behaviours that run the vehicle. The sensor data is pre-processed toproduce virtual sensor information available to the behaviours. The behaviours receive the virtual sensorinformation and send votes to the arbiters who send control signals to the low level controllers. Thegreyed out boxes are the SLAM processes that will be detailed in section IV.plebehavioursinthesystem. Thebehavioursregistertheirinterestinthedatabeinggeneratedbythevirtualsensorsandsendvotestothearbiterstospecifytheirdesiredcourseofaction. Atask-levelmissionplannerisusedtoenableanddisablebehavioursinthesystemdepending on the current state of the mission and itsdesiredobjectives. Thearbitercombinesthevotesfromthebehavioursandselectstheoptimalactiontosatisfythegoalsofthesystem. Aschematicrepresentationofthe data ow within the system is shown in gure 4.Thiscontrolstrategyreliesontheabilityofthesubtogather information about its environment and reasonaboutitsdesiredactions. Bycontinuouslymonitoringthestateoftheenvironment,thesubisabletorespondtochangesastheyoccur.C. DistributedcontrolA number of processes have been developed to ac-complishthetasksofgatheringdatafromtherobotssensors, processing this data and reasoning about thecourseofactiontobetakenbytherobot. Thesepro-cesses are distributed across a network of computersandcommunicateasynchronouslyviaaTCP/IPsocket-basedinterfaceusingamessagepassingprotocoldevel-opedattheCentre.Acentralcommunicationshubisresponsibleforrout-ingmessagesbetweenthedistributedprocessesrunningonthevehicleandonthecommandstation. Processesregistertheirinterestinmessagesbeingsentbyotherprocessesinthesystemandthehubroutesthemessageswhentheyarrive. Whilethiscommunicationsstructurehassomedrawbacks,suchaspotentialcommunicationsbottlenecksandrelianceontheperformanceofthecen-tralhub, it doesprovidesome interesting possibilitiesfor exible conguration, especially during the devel-opmentcycleofthesystem. AsshowninFigure5,thelow-levelprocessesthatcontrolthevehiclecaneasilybereplacedbyadataplaybackprocesstoreplaymissiondata or by a simulator to develop closed-loop controlalgorithmspriortovehicledeployment.IV. SimultaneousLocalisationandMapBuilding(SLAM)SimultaneousLocalisationandMapBuilding(SLAM)is the process of concurrently building up a featurebasedmapoftheenvironmentandusingthistoobtainestimates of the location of the vehicle 345611.Therobottypicallystartsatanunknownlocationwithnoaprioriknowledgeoflandmarklocations. Fromrel-ativeobservationsoflandmarks,itsimultaneouslycom-putes an estimate of vehicle location and an estimateoflandmarklocations. Whilecontinuinginmotion,therobot builds a complete map of landmarks and usesthese to provide continuous estimates of vehicle loca-tion.The localisation and map building process consistsof a recursive, three-stage update procedure compris-ingprediction, observationandupdatestepsusinganTCP/IPTCP/IPTCP/IP TCP/IPTCP/IPTCP/IPTCP/IPBaseGUISea KingGUISea KingFeatureExtractorSea KingSlamSea KingRemoteOberonLow LevelDataLoggerSea KingSonarRS-232A/DGyroD/AThrusters_ TCP/IPImagenexRemoteImagenexSonarRS-232BaseCommPressure(a) Vehicle controllerTCP/IPTCP/IPTCP/IP TCP/IPTCP/IPTCP/IPBaseGUISea KingGUISea KingFeatureExtractorSea KingSlamDataPlaybackDataLogger BaseComm_(b) Data PlaybackTCP/IPTCP/IPTCP/IP TCP/IPTCP/IPTCP/IPBaseGUISea KingGUISea KingFeatureExtractorSea KingSlamOberonSimulationDataLogger BaseComm_(c) SimulationFig. 5. a) The low-level processes that control the vehicle can easily be replaced by b) a data playbackprocess to replay mission data or by c) a simulator to develop closed-loop control algorithms prior tovehicle deployment.Extended Kalman Filter (EKF). The state estimatex(k) is the augmented state vector consisting of thetwo dimensional pose of the vehicle xv(k), made upof the position (xv,yv) and orientation v, togetherwiththeestimatesofthepositionsofthe N landmarksxi,i=1.N 4. Pointlandmarksarecurrentlytrackedinthemapalthoughthestateformulationoftheesti-matescanaccommodatehigherorderfeaturessuchaslines,curvesandpolylines.V. ResultsThissectiondescribesresultsfromthedeploymentofthevehicleduringtestinginanaturalterrainenviron-mentalongSydneyscoast. Weshowthatthecontrolarchitectureappearstobeworkingeectivelybyshow-ing the altitude versus depth plots over a particularrun. WealsoshowsomeoftheresultsachievedusingtheSimultaneousLocalisationandMapBuildingtech-niques. Ascanbeseen,thevehicleisabletonavigateinastraightlinerelativetothesonartargetsdeployedintheeld.A. Altitude/DepthcontrolBycouplingthedepthcontrollerwiththedepthsen-sor,whichiscontinuouslysamplingthedepth,thesubcanquicklyrespondtodisturbancesinitsdepth. Oneof the key behaviours envisaged for the vehicle is theability to maintain a constant altitude above the seaoor. TheImagenexsonarthatcurrentlyprovidesalti-tudeestimatesoperatesatamuchslowerratethanthedepthsensor(ontheorderof1Hz)andwouldbeun-abletosupplytheinformationataratesucientlyhightorejectdisturbancesindepth. TheImagenexsonarisalsosharedbyboththetaskthatmaintainstheal