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基于RBF神经网络的混凝土泵车臂架运动学逆解.docx

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基于RBF神经网络的混凝土泵车臂架运动学逆解为题目，写不少于1200的论文 1.Introduction Nowadays,theconstructionindustryisrapidlydevelopingworldwide,andtheconcretepumptruckisconsideredasignificantdeviceintheconstructionsite.Itiscrucialequipmentthathelpstoplaceready-mixconcreteatdifferentelevationswithhighefficiencyandaccuracy.Theconcretepumptruckconsistsofmultiplemechanisms,wherethearmframeplaysafundamentalroleinlifting,moving,andplacingconcreteatvarioussites. Theinversekinematicsoftheconcretepumptruckarmframeisessentialtodeterminetheanglesandpositionsofitsdifferentsegments.Theaccuratemeasurementofthesevariablesisnecessarytoensureproperplacementoftheconcretewithoutcausinganydamage.Therefore,inthispaper,weproposeanRBFneuralnetwork-basedapproachtoobtaintheinversekinematicsoftheconcretepumptruckarmframe. 2.Background Theinversekinematicsofaroboticarmisdefinedastheprocessofdeterminingthejointvariablesthatachieveadesiredendeffectorposition.Inthecaseoftheconcretepumptruckarmframe,thejointvariablescorrespondtotheanglesandpositionsofthedifferentsegmentsofthearmframe,whiletheendeffectoristheconcreteplacementnozzle. Atraditionalapproachtosolvetheinversekinematicsproblemisthroughanalyticalmethodssuchasthegeometricmethodoralgebraicmethod.However,thecomplexityoftheconcretepumptruckarmframemakesthistaskchallengingandlaborious.Moreover,theanalyticalapproachrequiressignificantmathematicskillsandistime-consuming. Incontrast,theneuralnetwork-basedapproachoffersamoreefficientandaccuratesolution.TheRBFneuralnetworkisapopularchoiceinsolvinginversekinematicsproblemsduetoitsexcellentapproximationcapabilitiesandflexibility.TheRBFnetworkcanmodelnon-linearrelationshipsbetweentheinputandoutputvariables,makingitsuitableforcomplexproblemssuchastheinversekinematicsoftheconcretepumptruckarmframe. 3.Methodology TheproposedRBFneuralnetwork-basedapproachinvolvestrainingthenetworktolearntherelationshipbetweenthejointvariablesandtheendeffectorposition.Theinputofthenetworkisthejointvariables,whiletheoutputisthecorrespondingendeffectorpo