[机械模具数控自动化专业毕业设计外文文献及翻译]【期刊】黄瓜采摘机器人的无碰撞规划-外文文献

Biosystems Engineering (2003) 86(2), 135144doi:10.1016/S1537-5110(03)00133-8Available online at AEAutomation and Emerging TechnologiesCollision-free Motion Planning for a Cucumber Picking RobotE.J. Van Henten; J. Hemming; B.A.J. Van Tuijl; J.G. Kornet; J. BontsemaDepartmentofGreenhouseEngineering,InstituteofAgricultural andEnvironmental Engineering(IMAGB.V.),P.O. Box43,NL-6700AAWageningen,TheNetherlands; e-mailofcorrespondingauthor:eldert.vanhentenwur.nl(Received 26 April 2002; accepted in revised form 8 July 2003; published online 29 August 2003)One of the most challenging aspectsof the development, at the Institute ofAgricultural and EnvironmentalEngineering (IMAG B.V.), of an automatic harvesting machine for cucumbers was to achieve a fast andaccurateeyehandco-ordinationduringthepickingoperation.Thispaperpresentsaproceduredevelopedforthecucumberharvestingrobottopursuethisobjective.Theprocedurecontainstwomaincomponents.Firstof all acquisition of sensory information about the working environment of the robot and, secondly, aprogramtogeneratecollision-freemanipulatormotionstodirecttheend-effectortoandfromthecucumber.Thispaperelaboratesonthelatter.Collision-freemanipulatormotionsweregeneratedwithaso-calledpathsearchalgorithm.InthisresearchtheA*-searchalgorithmwasused.Withsomenumericalexamplesthesearchprocedure is illustrated and analysed in view of application to cucumber harvesting. It is concluded thatcollision-free motionscan be calculated for the seven degrees-of-freedom manipulator used in the cucumberpicking device. The A*-search algorithm is easy to implement and robust. This algorithm either produces asolutionorstopswhenasolutioncannotbefound.Thisfavourableproperty,however,makesthealgorithmprohibitively slow. The results showed that the algorithm does not include much intelligence in the searchprocedure.Itisconcludedthattomeettherequired10sforasingleharvestcycle,furtherresearchisneededtond fast algorithms that produce solutions using as much information about the particular structure of theproblem as possible and give a clear message ifsucha solution can not be found.#2003SilsoeResearchInstitute.All rightsreservedPublished byElsevierLtd1. IntroductionIn 1996, the Institute of Agricultural and Environ-mentalEngineering(IMAGB.V.)beganresearchonthedevelopment of an autonomous cucumber harvestingrobot supported by the Dutch Ministry of Agriculture,FoodandFishery(Gieling et al.,1996;VanKollenburg-Crisan et al., 1997). The task of designing robots foragriculturalapplicationsraisesissuesnotencounteredinother industries. The robot has to operate in a highlyunstructured environment in which no two scenes arethe same. Both crop and fruit are prone to mechanicaldamageandshouldbehandledwithcare.Therobothasto operate under adverse climatic conditions, such ashigh relative humidity and temperature as well aschanging light conditions. Finally, to be cost effective,therobotneedstomeethighperformancecharacteristicsin terms of speed and success rate of the pickingoperation. In this project, these challenging issues havebeen tackled by an interdisciplinary approach in whichmechanical engineering, sensor technology (computervision), systems and control engineering, electronics,software engineering, logistics, and, last but not least,horticultural engineering partake (Van Kollenburg-Crisan et al., 1997; Bontsema et al., 1999; Meulemanet al., 2000).One of the most challenging aspects of the develop-mentofanautomaticharvestingmachineistoachieveafastandaccurateeyehandco-ordination, i.e. toachievean effective interplay between sensory informationacquisitionandrobotmotioncontrolduringthepickingoperation, just like people do. In horticultural practice,a trained worker needs only 36s to pick and store asingle fruit and that performance is hard to beat.Fortunately, in terms of picking speed a roboticharvester does not have to achieve such high perfor-mancecharacteristics.Ataskanalysisrevealedthat,foreconomicfeasibility,asingleharvestoperationmaytakeARTICLE IN PRESS1537-5110/$30.00 135 #2003SilsoeResearch Institute.All rightsreservedPublishedbyElsevierLtdup to 10s (Bontsema et al., 1999). Still, robot motionsshould be as fast as possible while preventing collisionsofthemanipulator,end-effectorandharvestedfruitwiththecrop,thegreenhouseconstructionandtherobotitself(such as the vehicle and vision system). In a Dutchcucumber production facility, the robot operates in avery tight working environment. Finally, to assure thequality of the harvested fruit, constraints have to beimposedonthetravellingspeedandaccelerationsofthemanipulatorduringvariousportionsofthemotionpath.To achieve the desired eyehand co-ordination, oneneeds (real-time) acquisition of sensory information ofthe environment as well as algorithms to calculatecollision-free motions for the manipulator. In thisproject,thesensorysystemisbasedoncomputervision,as reported by Meuleman et al. (2000). This paperfocuses on the fast generation of collision-free motiontrajectoriesforthemanipulatorofthepickingmachine.Thisissuehasnotreceivedmuchattentioninagricultur-alengineeringresearchdespitethefactthatconsiderableresearch effort has been spent on automatic harvestingofvegetablefruits(see e.g. Kondo et al.,1996;Hayashi& Sakaue, 1996; Arima & Kondo, 1999).The paper is outlined as follows. In Section 2, theharvesting robot is described. In Section 3, a tasksequence for a single harvest operation is presented.ThenSection4presentsthecomponentsofanautomaticalgorithmforcollision-freemotionplanning.Toobtaininsightintotheoperationofthealgorithm,inSection5,theapproachisillustratedonatwo-degrees-of-freedom(DOF) manipulator. Section 6 contains results of amotion planning experiment with the six-DOF RV-E2Mitsubishi manipulator used in the harvest robot.Finally, Section 7 contains concluding remarks andsuggestionsfor future research.2. The harvesting robotIn Fig. 1 afunctionalmodeloftheharvestingrobotisshown. It consists of an autonomous vehicle used forcoarsepositioningoftheharvestingmachineintheaislesofthegreenhouse.Thisvehicleusestheheatingpipesasa rail for guidance and support. It serves as a mobileplatform for carrying power supplies, a pneumaticpump, various electronic hardware for data-acquisitionand control, a wide-angle camera system for detectionand localisation of cucumbers in the crop and a seven-DOF manipulator for positioning of the end-effector.The manipulator consists of a linear slide on top ofwhich a six-DOF Mitsubishi RV-E2 manipulator ismounted. The RV-E2 manipulator includes an anthro-pomorphic arm and a spherical wrist. The manipulatorhas a steady-state accuracy of C60C12mm and meetsgeneral requirements with respect to hygiene and theoperation under adverse greenhouse climate conditions(high relative humidity and high temperature). Themanipulator carries an end-effector that contains twoparts:agrippertograspthefruitandacuttingdevicetoseparate the fruit from the plant. The end-effectorcarries a laser ranging system or a small camera. Theyare used to obtain sensory information for ne motioncontrol of the end-effector in the neighbourhood of thecucumber, if needed.3. Task sequence of a single harvest operationA task sequence of a single harvest operation ispresented in Fig. 2. Approaching the cucumber duringthe picking operation is considered to be a two-stageprocess. First, with the camera system mounted on thevehicle, the cucumber fruit is detected, its ripeness isassessed and its location is determined. In case it isARTICLE IN PRESS(b) (c)(d)(i) (a) (f)(g)(h) (e)Fig. 1. A functional model of the cucumber harvest robot; (a)vehicle; (b) wide angle camera; (c) seven-degrees-of-freedommanipulator; (d) end-effector; (e) laser ranger and position ofcamera for local imaging; (f) computer and electronics; (g) reelwith 220 V power line; (h) pneumatic pump; (i) heating pipeNotationc transitioncosts between two nodesf cost functiong cost of the motion path from node S to thecurrent nodeG goal nodeh optimistic estimate of the cost to go to thegoal from the current noden noden0successorof nodeS start nodeE.J.VANHENTEN ET AL.136decidedtopickthecucumber,thelow-resolutionimagesofthe vehicle-mounted camera areusedfor positioningtheend-effectorintheneighbourhoodofthecucumber.Oncetheend-effectorhasarrivedintheneighbourhoodofthecucumber,then,usingthelaserrangingsystemorcamerasystemmountedontopoftheend-effector,high-resolution information of the local environment of thecucumberisobtainedforthenalaccurateapproachofthe cucumber. The end-effector grips and cuts the stalkof the fruit. The gripper xes the detached fruit andnallytheharvestedfruitismovedtothestoragecrate.Obstacleavoidancemotionplanningwillbeusedbothfor the initial approach of the cucumber as well as thereturnjourneyoftheharvestedcucumbertothecrate,toassure that other objects in the work space such as therobotvehicleitselfbutalsostemsand,ifpresent,leavesand parts of the greenhouse construction are not hit.Clearly, the harvested cucumber increases the size oftheend-effector,whichshouldbeconsideredduringthereturn motion of the manipulator to the storage. Theaverage length ofa cucumber is 300mm.4. A collision-free motion planning algorithmFigure 3 illustratesthecomponentsofaprogramthatautomatically generates collision-free motions for thecucumber picking robot based on the work of Herman(1986). Collision-free motion planning relies on three-dimensional (3D) information about the physicalstructureoftherobotaswellastheworkspaceinwhichthe robot has to operate. So, the rst step in collision-free robot motion planning is the 3D world descriptionacquisition. This description is based on sensoryinformation such as machine vision as well as a prioriknowledge about, for instance, the 3D kinematicstructure of the harvesting robot, e.g. 3D models,contained in a database. With this information, duringthe task definition phase,theoveralltaskoftherobotisplanned. It is decided which nal position and orienta-tionoftheend-effectorresultinthebestapproachofthecucumber. Also specic position and orientation con-straints are dened during this phase. In the inversekinematics phasethegoalpositionandorientationoftheend-effector, dened during task definition, are trans-lated into the goal conguration of the manipulator.The goal conguration is represented as a combinationof a translation of the linear slide and six rotations ofthejointsoftheseven-DOFmanipulator.Thisinforma-tion is used by the path planner.Thepath planneremploysasearch technique tond acollision-free pathfrom the start conguration of the manipulator to itsgoalconguration.Oncethecollision-freepathplanninghas been completed successfully, the trajectory plannerARTICLE IN PRESSInitialise allsystemsMove vehicledelta x furtheron the railTake stereoimagesImageprocessing(fruit detection)Camera at endposition?Path planningMove TCP tocutting pointGrip fruit andcut stalkMove with fruitto the crate andrelease itMove TCP tohome positionHarvestcompletedMove vehicle tohome positionyesMove globalcamera to 1stpositionMove globalcamera oneposition furthernoyesnoyesyesnoyesno3D localisationnoMove TCP closeto cutting pointMini camerastereo imagesImage processing(high res. 3Dlocalisation)Fruit(s) detected?Vehicle at endof rail?Other fruit inglobal image?Fruit mature?Fig. 2. Task sequence of a single harvest operation: 3D, three dimensional; TCP, tool-centre-pointCUCUMBERPICKINGROBOT 137convertsthecollision-freepathintoatrajectorythatcanbe executed by the manipulator. Typically, the pathplanning process is concerned only with collision-freecongurations in space, but not with velocity, accelera-tion and smoothness of motion. The trajectory plannerdealswiththesefactors.The trajectory planner producesthe motion commands for the servomechanisms of therobot. These commands are executed during theimplementation phase.Somecomponentsofthemotionplanningsystemwillbe described hereafter in more detail.4.1. World description (acquisition)The machine vision based world description acquisi-tion for the cucumber picking robot is described in apaper by Meuleman et al. (2000). The vision system isable to detect green cucumbers within a green canopy.Moreover, the vision system determines the ripeness ofthe cucumber. And nally, using stereovision techni-ques,thecameravisionsystemproduces3Dmapsoftheworkspace within the viewing angle of the camera. Inthiswaytherobotisabletodealwiththevariabilityinthe working environment with which itis confronted.As stated above, also a priori knowledge about, forinstance,thephysicalstructureoftherobotisneededforcollision-free motion planning. As an example, Fig. 4shows a 3D model of the six-DOF Mitsubishi RV-E2manipulator implemented in MATLAB1. The 3Dstructureofthemanipulatorisrepresentedbypolygonsconstructed of rectangles and triangles. The model isused for evaluation of motion strategies during simula-tionsandservesasabasisforthedetectionofcollisionsof the manipulator with structural components in theworkspace ofthe robot during motion planning.4.2. Inverse kinematicsThe inverse manipulator kinematics deal with thecomputation of the set of joint angles and translationsthatresultinthedesiredpositionandorientationofthetool-centre-point (TCP) of the manipulator (Craig,1989). The TCP is a pre-dened point on the end-effector. For the six-DOF Mitsubishi RV-E2 manip-ulatorVanDijk(1999)obtainedananalyticsolutionoftheinversemanipulatorkinematics.Fortheseven-DOFmanipulator, i.e. the Mitsubishi RV-E2 manipulatormounted on a linear slide, a straightforward analyticsolution of the inverse kinematics does not exist due tothe inherent redundancy in the kinematic chain.Recently a mixed analytic-numerical solution of theARTICLE IN PRESS1000800600400200050050050050000X plane,mmY plane, mmZ plane, mmFig. 4. A three-dimensional model of the six-degrees-of-freedomMitsubishi RV-E2 manipulatorStartWorld descriptionacquisitionInversekinematicsPath planningTrajectoryplanningImplementationEndData baseTask definitionFig. 3. A program for automatic generation of collision-freemotionsE.J.VANHENTEN ET AL.138inverse kinematics of this redundant manipulator wasobtained (Schenk, 2000). Given the position of the ripecucumber, the algorithm produces a collision-freeharvest conguration of the seven-DOF manipulator.Also,itassuresthatthejointshavesufcientfreedomforne-motion control in the neighbourhood of thecucumber.4.3. Path plannerAlgorithmsforcollision-freepathplanninghavebeenthe object of much research. See e.g. Latombe (1991)and Hwang and Ahuja (1992) for an overview.A collision-free path planner essentially consists oftwo important components: a search algorithm and acollision detection algorithm. The search algorithmexplores the search space for a feasible, i.e. collision-free, motion from a start point to a goal point. Duringthesearch,thefeasibilityofeachstepinthesearchspaceis checked by the collision detection algorithm. Thisalgorithm checks for collisions of the manipulator withother structural components in the workspace of therobot.Itisimportanttonotethatformostpathplannersthesearch space is the so-called conguration space of therobot,whichcruciallydiffersfromthe3Dworkspaceoftherobot.Incaseoftheseven-DOFmanipulatorofthecucumber harvest machine, the congurationspaceisaseven-dimensionalspacespannedbythecombinationofone joint translation and six joint rotations. Then, thesearch for a collision-free motion from a start positionand orientation of the tool-centre-point to the goalposition and orientation in the 3D workspace boilsdown to a search for a collision-free motion of a singlepoint through the seven-dimensional congurationspace from the start conguration to the goal cong-uration. In this way problems with redundancy in thekinematicchainareeasilycircumvented.Thereisaone-to-onemappingofapointinthecongurationspacetoapositionandorientationofthetool-centre-pointintheworkspace. However, for most manipulators, theopposite does not hold. Then a single position andorientationofthetool-centre-pointintheworkspacecanbe reproduced by several congurations of the manip-ulator. Due to its unique representation a search in thecongurationspaceispreferred.Forcollisiondetection,however, one needs to describe the physical posture ofthemanipulatorinrelationwithotherobjectsinthe3Dworkspace. Because every conguration represents asinglepostureofthemanipulatorinthe3Dworkspace,collisions can be easily veried. Then, given theparticular kinematic structure of the robot, the work-spaceobstaclescanbemappedintocongurationspaceobstacles aswill beshown later.4.3.1. The search algorithmApathsearchalgorithmshouldbeefcientandndasolutionifoneexists.Thelatterpropertyisreferredtoascompleteness (Pearl, 1984). Usually, algorithms thatguarantee completeness are not computationally ef-cient. Computational efciency, however, is crucialwhen on-line application is required. To obtain insightinto the various aspects of motion planning, in thisresearch, completeness of the algorithm was favouredabove computational efciency. The main reason forthat choice is that a complete algorithm will either ndthe solution or stop using a clearly dened stoppingcriterion if a solution cannot be found. This is not truefor algorithms that do not assure completeness. Theyeither supply a solution or get stuck without furthernotice.Inthisresearchtheso-calledA*-searchalgorithmwasused(Pearl,1984; Kondo, 1991; Russell &Norvig,1995).Itiseasytoimplementandguaranteescomplete-ness. Moreover, it minimises a cost criterion thatincludes a measure for the distance travelled in thesearch space. The algorithm was implemented inMATLAB1.To use the A*algorithm for motion planning, theconguration space of the robot is discretised using axed grid structure as shown in Fig. 5. The user candeneboththesizeandresolutionofthegrid.ThentheA*algorithmsearchesapathfromthestartgridpointtothe goal grid point while minimising a cost function f :Thiscostfuncti