基于Kinect的移動機器人同時目標跟蹤與避障

基于Kinect的移動機器人同時目標跟蹤與避障一、本文概述Overviewofthisarticle隨著和機器人技術(shù)的飛速發(fā)展，移動機器人在眾多領(lǐng)域如工業(yè)自動化、智能家居、醫(yī)療輔助等的應用越來越廣泛。在機器人的自主導航過程中，同時實現(xiàn)目標跟蹤與避障是確保機器人安全、高效完成任務的關(guān)鍵。微軟公司推出的Kinect傳感器以其低廉的價格和強大的功能，為機器人視覺感知提供了新的解決方案。本文旨在探討基于Kinect的移動機器人如何實現(xiàn)在復雜環(huán)境中的同時目標跟蹤與避障，以提高機器人的智能化水平，拓寬其應用領(lǐng)域。Withtherapiddevelopmentofroboticstechnology,theapplicationofmobilerobotsinmanyfieldssuchasindustrialautomation,smarthomes,medicalassistance,etc.isbecomingincreasinglywidespread.Intheautonomousnavigationprocessofrobots,achievingtargettrackingandobstacleavoidancesimultaneouslyisthekeytoensuringthesafeandefficientcompletionoftasks.TheKinectsensorlaunchedbyMicrosoftprovidesanewsolutionforrobotvisualperceptionwithitslowpriceandpowerfulfunctionality.ThisarticleaimstoexplorehowKinectbasedmobilerobotscanachievesimultaneoustargettrackingandobstacleavoidanceincomplexenvironments,inordertoimprovetheintelligencelevelofrobotsandexpandtheirapplicationareas.本文將首先介紹Kinect傳感器的工作原理及其在移動機器人中的應用價值。接著，詳細闡述基于Kinect的同時目標跟蹤與避障算法的設計和實現(xiàn)過程，包括目標檢測、跟蹤、路徑規(guī)劃以及避障策略等方面。然后，通過實驗結(jié)果分析算法的性能和穩(wěn)定性，驗證其在不同環(huán)境下的適用性?？偨Y(jié)本文的主要貢獻，并對未來的研究方向進行展望。ThisarticlewillfirstintroducetheworkingprincipleofKinectsensorsandtheirapplicationvalueinmobilerobots.Next,thedesignandimplementationprocessofaKinectbasedsimultaneoustargettrackingandobstacleavoidancealgorithmwillbeelaboratedindetail,includingtargetdetection,tracking,pathplanning,andobstacleavoidancestrategies.Then,analyzetheperformanceandstabilityofthealgorithmthroughexperimentalresultstoverifyitsapplicabilityindifferentenvironments.Summarizethemaincontributionsofthisarticleandprovideprospectsforfutureresearchdirections.本文的研究成果將為移動機器人實現(xiàn)更高級別的自主導航提供理論支持和技術(shù)指導，推動機器人在更多領(lǐng)域的應用和發(fā)展。Theresearchresultsofthisarticlewillprovidetheoreticalsupportandtechnicalguidanceformobilerobotstoachievehigher-levelautonomousnavigation,andpromotetheapplicationanddevelopmentofrobotsinmorefields.二、相關(guān)工作Relatedwork近年來，隨著計算機視覺和機器人技術(shù)的飛速發(fā)展，移動機器人的目標跟蹤與避障問題成為了研究的熱點。Kinect作為一款集成了深度相機、彩色相機和麥克風陣列的傳感器設備，因其低成本和高精度，被廣泛應用于人機交互、機器人導航、增強現(xiàn)實等領(lǐng)域?；贙inect的移動機器人目標跟蹤與避障技術(shù)，結(jié)合了計算機視覺、機器學習、機器人控制等多個學科的知識，為機器人賦予了在復雜環(huán)境中自主導航和交互的能力。Inrecentyears,withtherapiddevelopmentofcomputervisionandroboticstechnology,targettrackingandobstacleavoidanceofmobilerobotshavebecomeahotresearchtopic.Kinect,asasensordevicethatintegratesdepthcameras,colorcameras,andmicrophonearrays,iswidelyusedinfieldssuchashuman-computerinteraction,robotnavigation,andaugmentedrealityduetoitslowcostandhighaccuracy.Kinectbasedmobilerobottargettrackingandobstacleavoidancetechnologycombinesknowledgefrommultipledisciplinessuchascomputervision,machinelearning,androbotcontrol,endowingrobotswiththeabilitytoautonomouslynavigateandinteractincomplexenvironments.在目標跟蹤方面，研究者們利用Kinect提供的深度信息和彩色圖像，通過背景建模、特征提取、目標匹配等算法實現(xiàn)對動態(tài)目標的持續(xù)跟蹤。其中，基于濾波算法（如卡爾曼濾波、粒子濾波）和機器學習算法（如支持向量機、深度學習）的目標跟蹤方法得到了廣泛關(guān)注。這些方法能夠有效地處理目標遮擋、快速運動等復雜情況，提高跟蹤的穩(wěn)定性和準確性。Intermsoftargettracking,researchersuseKinect'sdepthinformationandcolorimagestoachievecontinuoustrackingofdynamictargetsthroughalgorithmssuchasbackgroundmodeling,featureextraction,andtargetmatching.Amongthem,targettrackingmethodsbasedonfilteringalgorithms(suchasKalmanfilter,particlefilter)andmachinelearningalgorithms(suchassupportvectormachine,deeplearning)havereceivedwidespreadattention.Thesemethodscaneffectivelyhandlecomplexsituationssuchastargetocclusionandrapidmotion,improvingthestabilityandaccuracyoftracking.避障技術(shù)方面，研究者們通常利用Kinect獲取的環(huán)境深度信息，結(jié)合機器人的運動學模型和路徑規(guī)劃算法，實現(xiàn)機器人的自主避障。常見的避障策略包括基于規(guī)則的方法、基于勢場的方法、基于優(yōu)化算法的方法等。這些方法能夠在不同程度上保證機器人在遇到障礙物時能夠安全避讓，同時保持較高的運動效率。Intermsofobstacleavoidancetechnology,researchersusuallyuseKinecttoobtainenvironmentaldepthinformation,combinedwiththerobot'skinematicmodelandpathplanningalgorithm,toachieveautonomousobstacleavoidanceoftherobot.Commonobstacleavoidancestrategiesincluderule-basedmethods,potentialfieldbasedmethods,andoptimizationalgorithmbasedmethods.Thesemethodscanensuretovaryingdegreesthatrobotscansafelyavoidobstacleswhilemaintaininghighmotionefficiency.將目標跟蹤與避障技術(shù)相結(jié)合，是實現(xiàn)移動機器人自主導航的關(guān)鍵。在這一方面，研究者們提出了多種基于Kinect的同時目標跟蹤與避障算法。這些算法通常涉及到多傳感器信息融合、多目標優(yōu)化等復雜問題，需要在保證跟蹤精度的實現(xiàn)高效的避障策略。隨著深度學習和強化學習等技術(shù)的發(fā)展，基于深度學習的同時目標跟蹤與避障算法也展現(xiàn)出了巨大的潛力和優(yōu)勢。Thecombinationoftargettrackingandobstacleavoidancetechnologyiscrucialforachievingautonomousnavigationofmobilerobots.Inthisregard,researchershaveproposedmultipleKinectbasedsimultaneoustargettrackingandobstacleavoidancealgorithms.Thesealgorithmstypicallyinvolvecomplexproblemssuchasmulti-sensorinformationfusionandmulti-objectiveoptimization,requiringefficientobstacleavoidancestrategiestoensuretrackingaccuracy.Withthedevelopmentoftechnologiessuchasdeeplearningandreinforcementlearning,simultaneoustargettrackingandobstacleavoidancealgorithmsbasedondeeplearninghavealsoshowngreatpotentialandadvantages.基于Kinect的移動機器人同時目標跟蹤與避障技術(shù)是一個涉及多個學科領(lǐng)域的綜合性問題。隨著相關(guān)技術(shù)的不斷發(fā)展和完善，我們有理由相信，未來的移動機器人將能夠在更加復雜和多變的環(huán)境中實現(xiàn)更加智能和高效的自主導航。ThesimultaneoustargettrackingandobstacleavoidancetechnologyofmobilerobotsbasedonKinectisacomprehensiveprobleminvolvingmultipledisciplines.Withthecontinuousdevelopmentandimprovementofrelatedtechnologies,wehavereasontobelievethatfuturemobilerobotswillbeabletoachievemoreintelligentandefficientautonomousnavigationinmorecomplexandever-changingenvironments.三、Kinect傳感器介紹IntroductiontoKinectsensorsKinect是微軟公司開發(fā)的一款深度感應攝像頭，它集成了視頻攝像頭、紅外攝像頭以及多陣列麥克風等多種傳感器，通過捕捉并解析人體運動以及聲音信號，實現(xiàn)了用戶與電子設備之間更為自然和直觀的交互。自推出以來，Kinect在人機交互、機器人技術(shù)、游戲娛樂等領(lǐng)域得到了廣泛應用。KinectisadeepsensingcameradevelopedbyMicrosoft,whichintegratesvarioussensorssuchasvideocameras,infraredcameras,andmultiarraymicrophones.Bycapturingandanalyzinghumanmotionandsoundsignals,itenablesmorenaturalandintuitiveinteractionbetweenusersandelectronicdevices.Sinceitslaunch,Kinecthasbeenwidelyusedinfieldssuchashuman-computerinteraction,roboticstechnology,gamingandentertainment.Kinect傳感器中最核心的部分是深度感應器，它能夠發(fā)射出紅外線并接收其反射信號，通過計算光線傳播的時間差來精確測量物體與攝像頭之間的距離，從而生成深度圖像。Kinect還配備有高分辨率的RGB攝像頭，用于捕捉彩色視頻信息，結(jié)合深度圖像，可以為研究者提供豐富的三維空間數(shù)據(jù)。ThecorepartofKinectsensorsisthedepthsensor,whichcanemitinfraredraysandreceivetheirreflectedsignals.Bycalculatingthetimedifferenceoflightpropagation,itaccuratelymeasuresthedistancebetweenobjectsandcameras,therebygeneratingdepthimages.Kinectisalsoequippedwithahigh-resolutionRGBcameratocapturecolorvideoinformation,combinedwithdepthimages,whichcanprovideresearcherswithrichthree-dimensionalspatialdata.在移動機器人領(lǐng)域，Kinect傳感器提供了一種非接觸式的感知手段，使得機器人能夠?qū)崟r獲取周圍環(huán)境的三維信息。通過處理和分析這些數(shù)據(jù)，機器人可以同時實現(xiàn)目標跟蹤和避障功能。具體來說，通過對深度圖像的處理，機器人可以識別出環(huán)境中的障礙物，從而規(guī)劃出安全的運動路徑；而彩色視頻信息則有助于機器人識別并跟蹤特定的目標物體，如人臉、手勢等。Inthefieldofmobilerobots,Kinectsensorsprovideanon-contactsensingmethod,enablingrobotstoobtainreal-timethree-dimensionalinformationofthesurroundingenvironment.Byprocessingandanalyzingthisdata,robotscansimultaneouslyachievetargettrackingandobstacleavoidancefunctions.Specifically,byprocessingdepthimages,robotscanidentifyobstaclesintheenvironmentandplansafemotionpaths;Colorvideoinformationhelpsrobotsrecognizeandtrackspecifictargetobjects,suchasfaces,gestures,etc.基于Kinect的移動機器人系統(tǒng)具有較高的靈活性和可擴展性。一方面，Kinect傳感器本身具有較高的性價比和易用性，使得基于該傳感器的機器人系統(tǒng)具有較高的性價比；另一方面，隨著計算機視覺和機器人技術(shù)的不斷發(fā)展，基于Kinect的機器人系統(tǒng)可以通過引入更先進的算法和技術(shù)來不斷提升其性能。Kinectbasedmobilerobotsystemshavehighflexibilityandscalability.Ontheonehand,Kinectsensorsthemselveshavehighcost-effectivenessandeaseofuse,makingrobotsystemsbasedonthesesensorshighlycost-effective;Ontheotherhand,withthecontinuousdevelopmentofcomputervisionandroboticstechnology,Kinectbasedrobotsystemscancontinuouslyimprovetheirperformancebyintroducingmoreadvancedalgorithmsandtechnologies.因此，Kinect傳感器在移動機器人同時目標跟蹤與避障等任務中發(fā)揮著重要作用，它不僅能夠為機器人提供豐富的環(huán)境感知信息，還能夠為研究者提供一種便捷、高效的實驗手段。隨著技術(shù)的不斷進步和應用領(lǐng)域的不斷拓展，基于Kinect的移動機器人系統(tǒng)將在未來展現(xiàn)出更加廣闊的應用前景。Therefore,Kinectsensorsplayanimportantroleintaskssuchastargettrackingandobstacleavoidanceformobilerobots.Theynotonlyproviderichenvironmentalperceptioninformationforrobots,butalsoprovideresearcherswithaconvenientandefficientexperimentalmethod.Withthecontinuousprogressoftechnologyandthecontinuousexpansionofapplicationfields,Kinectbasedmobilerobotsystemswillshowbroaderapplicationprospectsinthefuture.四、基于Kinect的同時目標跟蹤與避障方法AKinectbasedsimultaneoustargettrackingandobstacleavoidancemethod在本文中，我們提出了一種基于Kinect傳感器的移動機器人同時目標跟蹤與避障方法。該方法利用Kinect的深度圖像和彩色圖像信息，結(jié)合目標檢測、跟蹤和避障算法，實現(xiàn)了機器人在復雜環(huán)境中的自主導航。Inthisarticle,weproposeamobilerobotsimultaneoustargettrackingandobstacleavoidancemethodbasedonKinectsensors.ThismethodutilizesKinect'sdepthandcolorimageinformation,combinedwithobjectdetection,tracking,andobstacleavoidancealgorithms,toachieveautonomousnavigationofrobotsincomplexenvironments.我們利用Kinect的深度圖像進行目標檢測。通過設定合適的閾值，將深度圖像中的物體與背景進行分離，提取出目標物體的輪廓信息。然后，我們利用彩色圖像對目標物體進行識別，通過顏色、形狀等特征對目標進行分類，從而實現(xiàn)準確的目標檢測。WeuseKinect'sdepthimagesforobjectdetection.Bysettinganappropriatethreshold,theobjectsinthedepthimageareseparatedfromthebackgroundtoextractthecontourinformationofthetargetobject.Then,weusecolorimagestorecognizethetargetobject,classifythetargetbasedonfeaturessuchascolorandshape,andachieveaccuratetargetdetection.接下來，我們采用基于濾波器的目標跟蹤算法對目標物體進行持續(xù)跟蹤。在連續(xù)的圖像幀中，我們利用濾波器的預測功能，預測目標物體在下一幀中的位置，并通過匹配算法將預測位置與實際檢測到的目標位置進行匹配，從而實現(xiàn)對目標物體的連續(xù)跟蹤。Next,wewilluseafilterbasedtargettrackingalgorithmtocontinuouslytrackthetargetobject.Incontinuousimageframes,weutilizethepredictionfunctionoffilterstopredictthepositionofthetargetobjectinthenextframe,andmatchthepredictedpositionwiththeactualdetectedtargetpositionthroughmatchingalgorithms,therebyachievingcontinuoustrackingofthetargetobject.在避障方面，我們利用Kinect的深度圖像構(gòu)建機器人周圍的環(huán)境模型。通過分析深度圖像中的障礙物信息，我們可以獲取障礙物的位置、形狀和距離等關(guān)鍵信息。然后，我們結(jié)合機器人的運動學模型和動力學模型，規(guī)劃出機器人的避障路徑。在規(guī)劃過程中，我們充分考慮了機器人的運動約束和避障要求，確保機器人在避障過程中能夠保持平穩(wěn)、安全的運動。Intermsofobstacleavoidance,weuseKinect'sdepthimagestoconstructanenvironmentmodelaroundtherobot.Byanalyzingobstacleinformationindepthimages,wecanobtainkeyinformationsuchastheposition,shape,anddistanceofobstacles.Then,wecombinethekinematicanddynamicmodelsoftherobottoplanitsobstacleavoidancepath.Intheplanningprocess,wefullyconsiderthemotionconstraintsandobstacleavoidancerequirementsoftherobottoensurethatitcanmaintainsmoothandsafemovementduringobstacleavoidance.我們將目標跟蹤與避障算法進行融合，實現(xiàn)了機器人在復雜環(huán)境中的同時目標跟蹤與避障。在實際應用中，機器人可以通過不斷地獲取新的深度圖像和彩色圖像信息，對目標物體進行持續(xù)跟蹤，并在遇到障礙物時及時調(diào)整運動軌跡，從而實現(xiàn)自主導航。Weintegratetargettrackingandobstacleavoidancealgorithmstoachievesimultaneoustargettrackingandobstacleavoidanceforrobotsincomplexenvironments.Inpracticalapplications,robotscancontinuouslyacquirenewdepthandcolorimageinformation,continuouslytracktargetobjects,andadjusttheirmotiontrajectoryinatimelymannerwhenencounteringobstacles,therebyachievingautonomousnavigation.本文提出的基于Kinect的同時目標跟蹤與避障方法，充分利用了Kinect的深度圖像和彩色圖像信息，結(jié)合目標檢測、跟蹤和避障算法，實現(xiàn)了機器人在復雜環(huán)境中的自主導航。該方法具有實時性好、魯棒性強等優(yōu)點，對于推動移動機器人技術(shù)的發(fā)展具有重要意義。TheproposedKinectbasedsimultaneoustargettrackingandobstacleavoidancemethodfullyutilizesKinect'sdepthandcolorimageinformation,combinedwithobjectdetection,tracking,andobstacleavoidancealgorithms,toachieveautonomousnavigationofrobotsincomplexenvironments.Thismethodhastheadvantagesofgoodreal-timeperformanceandstrongrobustness,whichisofgreatsignificanceforpromotingthedevelopmentofmobilerobottechnology.五、實驗與分析ExperimentandAnalysis本部分將詳細描述我們?yōu)轵炞C基于Kinect的移動機器人同時目標跟蹤與避障系統(tǒng)的性能所進行的實驗，以及從這些實驗中獲得的結(jié)果和分析。ThissectionwillprovideadetaileddescriptionoftheexperimentsweconductedtoverifytheperformanceofKinectbasedmobilerobotsimultaneoustargettrackingandobstacleavoidancesystems,aswellastheresultsandanalysisobtainedfromtheseexperiments.我們的實驗在一個模擬的室內(nèi)環(huán)境中進行，其中包含了多種障礙物（如桌子、椅子、靜態(tài)和動態(tài)的人等）以及一個或多個需要被跟蹤的目標（例如，一個帶有特定顏色標記的物體或人）。移動機器人裝備了Kinect傳感器，該傳感器用于獲取深度圖像和RGB圖像，并通過我們的算法進行目標跟蹤和避障。Ourexperimentisconductedinasimulatedindoorenvironment,whichincludesvariousobstacles(suchastables,chairs,staticanddynamicpeople,etc.)aswellasoneormoretargetsthatneedtobetracked(suchasanobjectorpersonwithaspecificcolormark).ThemobilerobotisequippedwithKinectsensors,whichareusedtoobtaindepthandRGBimages,anduseouralgorithmsfortargettrackingandobstacleavoidance.在實驗中，我們首先初始化系統(tǒng)，讓機器人通過Kinect傳感器獲取環(huán)境的初始圖像。然后，我們指定一個或多個目標，讓機器人開始跟蹤這些目標。在機器人移動的過程中，它會不斷地通過Kinect獲取新的圖像，并利用我們的算法更新目標的位置和避障路徑。Intheexperiment,wefirstinitializethesystemandhavetherobotobtaininitialimagesoftheenvironmentthroughKinectsensors.Then,wespecifyoneormoretargetsandhavetherobotstarttrackingthem.Duringtherobot'smovement,itcontinuouslyobtainsnewimagesthroughKinectandusesouralgorithmtoupdatethetarget'spositionandobstacleavoidancepath.實驗結(jié)果顯示，我們的系統(tǒng)能夠有效地同時進行目標跟蹤和避障。在目標跟蹤方面，系統(tǒng)能夠準確地識別并跟蹤目標，即使在目標移動或改變姿態(tài)的情況下也能保持穩(wěn)定的跟蹤效果。在避障方面，系統(tǒng)能夠準確地識別障礙物，并實時規(guī)劃出避障路徑，確保機器人在移動過程中不會與障礙物發(fā)生碰撞。Theexperimentalresultsshowthatoursystemcaneffectivelyperformbothtargettrackingandobstacleavoidancesimultaneously.Intermsoftargettracking,thesystemcanaccuratelyidentifyandtrackthetarget,andmaintainstabletrackingperformanceevenwhenthetargetmovesorchangesitsattitude.Intermsofobstacleavoidance,thesystemcanaccuratelyidentifyobstaclesandplanobstacleavoidancepathsinrealtime,ensuringthattherobotdoesnotcollidewithobstaclesduringmovement.從實驗結(jié)果來看，我們的系統(tǒng)能夠?qū)崿F(xiàn)基于Kinect的移動機器人同時目標跟蹤與避障的功能。這主要得益于我們使用的深度學習和計算機視覺技術(shù)，以及優(yōu)化的避障算法。然而，我們也注意到，在某些復雜的環(huán)境或目標快速移動的情況下，系統(tǒng)的性能可能會有所下降。這提示我們，未來可能需要進一步優(yōu)化我們的算法，以提高系統(tǒng)在這些情況下的性能。Fromtheexperimentalresults,itcanbeseenthatoursystemcanachievebothtargettrackingandobstacleavoidancefunctionsforKinectbasedmobilerobots.Thisismainlyduetothedeeplearningandcomputervisiontechnologiesweuse,aswellasoptimizedobstacleavoidancealgorithms.However,wehavealsonoticedthatinsomecomplexenvironmentsorsituationswheretargetsmoverapidly,theperformanceofthesystemmaydecrease.Thissuggeststhatinthefuture,wemayneedtofurtheroptimizeouralgorithmstoimprovetheperformanceofthesysteminthesesituations.我們還注意到，Kinect傳感器的性能對系統(tǒng)的性能有重要影響。例如，如果傳感器的精度不夠高，或者受到光照條件的影響，那么獲取的深度圖像和RGB圖像可能會有誤差，這可能會影響到目標跟蹤和避障的準確性。因此，在選擇和使用Kinect傳感器時，需要充分考慮其性能特點和限制。WealsonoticedthattheperformanceofKinectsensorshasasignificantimpactonthesystem'sperformance.Forexample,iftheaccuracyofthesensorisnothighenoughorisaffectedbylightingconditions,theobtaineddepthimagesandRGBimagesmayhaveerrors,whichmayaffecttheaccuracyoftargettrackingandobstacleavoidance.Therefore,whenselectingandusingKinectsensors,itisnecessarytofullyconsidertheirperformancecharacteristicsandlimitations.我們的實驗結(jié)果表明，基于Kinect的移動機器人同時目標跟蹤與避障系統(tǒng)具有較大的潛力和應用價值。然而，也需要注意到系統(tǒng)的一些限制和挑戰(zhàn)，以便在未來的研究中進一步改進和優(yōu)化。OurexperimentalresultsindicatethatKinectbasedmobilerobotsimultaneoustargettrackingandobstacleavoidancesystemshavegreatpotentialandapplicationvalue.However,itisalsonecessarytopayattentiontosomelimitationsandchallengesofthesysteminordertofurtherimproveandoptimizeitinfutureresearch.六、結(jié)論與展望ConclusionandOutlook本文研究了基于Kinect的移動機器人同時目標跟蹤與避障的問題，提出了一種結(jié)合深度信息與彩色信息的多傳感器融合方法。通過搭建實驗平臺，對所提出的方法進行了驗證，并得出了以下ThisarticlestudiestheproblemofsimultaneoustargettrackingandobstacleavoidanceformobilerobotsbasedonKinect,andproposesamulti-sensorfusionmethodthatcombinesdepthinformationandcolorinformation.Bybuildinganexperimentalplatform,theproposedmethodwasvalidatedandthefollowingresultswereobtained:基于Kinect的深度相機能夠為移動機器人提供豐富的深度信息，結(jié)合彩色相機可以獲取更加全面的環(huán)境感知能力。這使得機器人在進行目標跟蹤與避障時，能夠更準確地識別目標物體和障礙物，提高了機器人的運動性能。Kinectbaseddepthcamerascanproviderichdepthinformationformobilerobots,andcombinedwithcolorcameras,theycanobtainmorecomprehensiveenvironmentalperceptioncapabilities.Thisenablesrobotstomoreaccuratelyidentifytargetobjectsandobstaclesduringtargettrackingandobstacleavoidance,improvingtheirmotionperformance.本文所提出的多傳感器融合方法，實現(xiàn)了對目標物體的實時跟蹤與避障。實驗結(jié)果表明，該方法在多種場景下均表現(xiàn)出良好的穩(wěn)定性和魯棒性，能夠有效地應對目標物體的運動變化以及環(huán)境中的干擾因素。Themulti-sensorfusionmethodproposedinthisarticleachievesreal-timetrackingandobstacleavoidanceoftargetobjects.Theexperimentalresultsshowthatthismethodexhibitsgoodstabilityandrobustnessinvariousscenarios,andcaneffectivelycopewiththemotionchangesoftargetobjectsandinterferencefactorsintheenvironment.與傳統(tǒng)方法相比，本文所提出的方法具有更高的精度和更低的計算復雜度。這使得機器人在進行目標跟蹤與避障時，能夠更快地做出反應，提高了機器人的實時性能。Comparedwithtraditionalmethods,themethodproposedinthisarticlehashigheraccuracyandlowercomputationalcomplexity.Thisenablesrobotstoreactfasterduringtargettrackingandobstacleavoidance,improvingtheirr