【文章內(nèi)容簡(jiǎn)介】 級(jí)帶輪傳動(dòng)比為=1小手臂擺動(dòng)電機(jī)蝸輪蝸桿的傳動(dòng)比為 由傳動(dòng)比為 =30 查表得： 蝸桿頭數(shù)=2 =60蝸桿40Cr 45—50HRC 蝸輪103鋁青銅 估計(jì) =5m/s 查表得 =140Mpa由式 可得查表得： m=1 =18 q=18 =60（1）由 =帶輪的主動(dòng)論轉(zhuǎn)速為=3r/min由式 V一般在5——25m/s取V=20m/s 查表得：選帶輪為Y型帶=20又=1 取V帶基準(zhǔn)長(zhǎng)度 查表得： 取 =200 [8] 手腕擺動(dòng)電機(jī)處蝸輪蝸桿、帶傳動(dòng)比的確定由手腕擺動(dòng)處選用電機(jī)為36BF003型可得，運(yùn)行頻率為f=27000HZ=又 =5r/min 總傳動(dòng)比 i==又大手臂擺動(dòng)電機(jī)確定的一級(jí)帶輪傳動(dòng)比為=小手臂擺動(dòng)電機(jī)確定的二級(jí)帶輪傳動(dòng)比為=1取三級(jí)傳動(dòng)比為=1蝸輪蝸桿的傳動(dòng)比為 由傳動(dòng)比為 =18 查表得： 蝸桿頭數(shù)=2 =36蝸桿40Cr 45—50HRC 蝸輪103鋁青銅 估計(jì) =5m/s 查表得 =140Mpa由式 可得查表得： m=1 =18 q=18 =36[8] 小手臂擺動(dòng)處軸的強(qiáng)度較核（1）小手臂擺動(dòng)軸的受力分析圖如下： 小手臂擺動(dòng)軸的受力分析圖[9]其中： （1）繪制垂直面內(nèi)的彎矩圖由式 垂直面內(nèi)彎矩圖如下所示： 垂直面內(nèi)彎矩圖[9]（1） 繪制水平面內(nèi)的彎矩圖由式 可得水平面內(nèi)的彎矩圖如下所示： 水平面內(nèi)的彎矩圖[9]（2） 繪制合成彎矩圖在C和D處 在E處 在C處 在E處 在D處 軸的合成彎矩圖如下所示： 軸的合成彎矩圖[9]軸的扭矩圖如下所示： 軸的扭矩圖[9]軸的危險(xiǎn)截面為中心E點(diǎn) 當(dāng)量彎矩為 軸的材料選用45鋼，調(diào)質(zhì)處理查表得： 軸的直徑取d=5能夠滿(mǎn)足強(qiáng)度要求。5 總結(jié)與展望歷經(jīng)一個(gè)學(xué)期的努力，六自由度機(jī)械手終于設(shè)計(jì)成功。在這段時(shí)間內(nèi)，我溫習(xí)和鞏固了大學(xué)四年所學(xué)的專(zhuān)業(yè)知識(shí)，綜合運(yùn)用了所學(xué)的機(jī)械和電子方面的知識(shí)，極大的提高了我分析問(wèn)題，解決問(wèn)題的能力?；仡欉^(guò)去的兩個(gè)多月，感覺(jué)收獲頗豐：1) 通過(guò)對(duì)機(jī)械手的整體方案設(shè)計(jì)，典型結(jié)構(gòu)設(shè)計(jì)，使我對(duì)大學(xué)四年所學(xué)的機(jī)械方面的知識(shí)以及專(zhuān)業(yè)方面的知識(shí)有了更深一步的了解和認(rèn)識(shí)，而不像以前一樣僅僅停留在書(shū)本的概念上。2) 掌握了機(jī)械結(jié)構(gòu)整體方案設(shè)計(jì)的原則和要求，在設(shè)計(jì)過(guò)程中熟練的查取了相關(guān)的設(shè)計(jì)手冊(cè)，為以后工作上的需要打下了堅(jiān)實(shí)的基礎(chǔ)。3) 通過(guò)對(duì)各個(gè)典型機(jī)構(gòu)的設(shè)計(jì)，充分的理解和掌握了機(jī)械設(shè)計(jì)方面的知識(shí)，并且也對(duì)專(zhuān)業(yè)上的智能控制和誤差控制方面有了更加深刻的認(rèn)識(shí)。由于論文的研究時(shí)間、本人的能力和知識(shí)范圍有限，本論文的研究工作還存在著一些不足之處，存在一些需要完善和改進(jìn)的地方：1) 因?yàn)榱杂啥葯C(jī)械手控制系統(tǒng)是一個(gè)開(kāi)環(huán)控制系統(tǒng)，所以機(jī)器手工作過(guò)程中存在著丟步、失態(tài)問(wèn)題，所以在時(shí)間和條件允許的情況下，希望能做成閉環(huán)系統(tǒng)，以提高系統(tǒng)精度。2) 系統(tǒng)幾個(gè)主要模塊尚未進(jìn)行過(guò)實(shí)際考核，在工作可靠性、抗干擾性能等方面有待進(jìn)一步完善和提高。此外系統(tǒng)在總體布局和結(jié)構(gòu)設(shè)計(jì)上離實(shí)際應(yīng)用還有一些待完善之處。隨著科技和社會(huì)的進(jìn)步，智能機(jī)器人在人們生活的各個(gè)領(lǐng)域發(fā)揮著越來(lái)越大的作用。因此，了解機(jī)器人、研究機(jī)器人、并最終設(shè)計(jì)制造更先進(jìn)、更科學(xué)、更人性化的機(jī)器人就成為我們機(jī)電專(zhuān)業(yè)最為重要的任務(wù)之一。謝 辭經(jīng)過(guò)半年的忙碌和工作，本次畢業(yè)設(shè)計(jì)已經(jīng)接近尾聲，作為一個(gè)本科生的畢業(yè)設(shè)計(jì)，由于所學(xué)知識(shí)有限，經(jīng)驗(yàn)的匱乏，難免有許多考慮不周全的地方，如果沒(méi)有導(dǎo)師的督促指導(dǎo)，以及同學(xué)們的支持，想要完成這個(gè)設(shè)計(jì)是難以想象的。 在這里首先要感謝我的導(dǎo)師黃老師。黃老師平日里工作繁多，但在我做畢業(yè)設(shè)計(jì)的每個(gè)階段，從查閱資料，設(shè)計(jì)草案的確定和修改，中期檢查答辯，后期詳細(xì)設(shè)計(jì)，裝配草圖等整個(gè)過(guò)程中都給予了我悉心的指導(dǎo)。我的設(shè)計(jì)較為復(fù)雜煩瑣，但是黃老師仍然細(xì)心地糾正圖紙中的錯(cuò)誤與論文中的誤點(diǎn)。除了敬佩黃老師的專(zhuān)業(yè)水平外，他的治學(xué)嚴(yán)謹(jǐn)和科學(xué)研究的精神也是我永遠(yuǎn)學(xué)習(xí)的榜樣，并將積極影響我今后的學(xué)習(xí)和工作。 其次要感謝和我一組作畢業(yè)設(shè)計(jì)的其他同學(xué)，我們?cè)诒敬卧O(shè)計(jì)中相互學(xué)習(xí)，相互鼓勵(lì)。如果我們之間的相互幫助，此次設(shè)計(jì)的完成將變得非常困難。 然后還要感謝大學(xué)四年來(lái)所有的老師，指導(dǎo)我們打下專(zhuān)業(yè)知識(shí)的基礎(chǔ)；同時(shí)還要感謝所有的同學(xué)們，正是因?yàn)橛辛四銈兊闹С趾凸膭?lì)。此次畢業(yè)設(shè)計(jì)才會(huì)順利完成。感謝父母對(duì)我的關(guān)愛(ài)和教誨。 最后感謝機(jī)械與電氣工程學(xué)院和我的母?！不战ㄖI(yè)學(xué)院四年來(lái)對(duì)我的大力栽培。[參考文獻(xiàn)][1] 《工業(yè)機(jī)械手》[M].上海:上?？茖W(xué)技術(shù)出版社,[2] [D].哈爾濱工業(yè)大學(xué)：機(jī)械電子工程學(xué)院，2005[3] [M].天津：天津科技出版社，[4] 楊柯楨，(第四版)[M].北京：高等教育出版社,[5] [M].北京：機(jī)械工業(yè)出版社，[6] (第三版 第5卷)[M].北京：化學(xué)工業(yè)出版社，[7] [M].北京：機(jī)械工業(yè)大學(xué)出版，[8] 龐振基，[M].北京：機(jī)械工業(yè)出版社，[9] (Ⅰ)[M]. 北京：機(jī)械工業(yè)出版社，[10] (美)尼庫(kù)(Niku,)著；[M].北京:電子工業(yè)出版社,[11] 附錄一 英文科技文獻(xiàn)翻譯英文原文Automated Tracking and Grasping of a Moving Object with a Robotic HandEye SystemAbstractMost robotic grasping tasks assume a stationary or fixed object. In this paper, we explore the requirements for tracking and grasping a moving object. The focus of our work is to achieve a high level of interaction between a realtime vision system capable of tracking moving objects in 3D and a robot arm with gripper that can be used to pick up a moving object. There is an interest in exploring the interplay of handeye coordination for dynamic grasping tasks such as grasping of parts on a moving conveyor system, assembly of articulated parts, or for grasping from a mobile robotic system. Coordination between an organism39。s sensing modalities and motor control system is a hallmark of intelligent behavior, and we are pursuing the goal of building an integrated sensing and actuation system that can operate in dynamic as opposed to static environments.The system we have built addresses three distinct problems in robotic handeye coordination for grasping moving objects: fast putation of 3D motion parameters from vision, predictive control of a moving robotic arm to track a moving object, and interception and grasping. The system is able to operate at approximately human arm movement rates, and experimental results in which a moving model train is tracked is presented, stably grasped, and picked up by the system. The algorithms we have developed that relate sensing to actuation are quite general and applicable to a variety of plex robotic tasks that require visual feedback for arm and hand control.I. INTRODUCTIONThe focus of our work is to achieve a high level of interaction between realtime vision systems capable of tracking moving objects in 3D and a robot arm equipped with a dexterous hand that can be used to intercept, grasp, and pick up a moving object. We are interested in exploring the interplay of handeye coordination for dynamic grasping tasks such as grasping of parts on a moving conveyor system, assembly of articulated parts, or for grasping from a mobile robotic system. Coordination between an organism39。s sensing modalities and motor control system is a hallmark of intelligent behavior, and we are pursuing the goal of building an integrated sensing and actuation system that can operate in dynamic as opposed to static environments.There has been much research in robotics over the last few years that address either visual tracking of moving objects or generalized grasping problems. However, there have been few efforts that try to link the two problems. It is quite clear that plex robotic tasks such as automated assembly will need to have integrated systems that use visual feedback to plan, execute, and monitor grasping.The system we have built addresses three distinct problems in robotic handeye coordination for grasping moving objects: fast putation of 3D motion parameters from vision, predictive control of a moving robotic arm to track a moving object, and interception and grasping. The system is able to operate at approximately human arm movement rates, using visual feedback to track, intercept, stably grasp, and pick up a moving object. The algorithms we have developed that relate sensing to actuation are quite general and applicable to a