【正文】 手腕擺動(dòng)電機(jī)處渦輪蝸桿及帶傳動(dòng)的確定 …………………………24 小手臂擺動(dòng)處軸的校核 ……………………………………………………25 5 總結(jié)與展望 ………………………………………………………………………29謝辭 …………………………………………………………………………………30[參考文獻(xiàn)] …………………………………………………………………………31附錄一 科技文獻(xiàn)翻譯………………………………………………………………32附錄二 畢業(yè)設(shè)計(jì)任務(wù)書(shū)與開(kāi)題報(bào)告………………………………………………46多自由度機(jī)械手機(jī)械設(shè)計(jì)1 緒 論機(jī)械手 (manipulator)是一種能按給定的程序或要求，自動(dòng)地完成物體(材料 、工件、零件或工具等 )傳送或操作作業(yè)的機(jī)械裝置，它能部分地代替人來(lái)進(jìn)行繁重、危險(xiǎn)、重復(fù)等手工作業(yè)。該機(jī)械手主要由底座，腰部，主板，大手臂，小手臂，手腕，夾爪組成，采用步進(jìn)電機(jī)驅(qū)動(dòng)，單片機(jī)控制。文中對(duì)機(jī)械手進(jìn)行了正運(yùn)動(dòng)學(xué)分析, 采用齊次坐標(biāo)變換法得到了機(jī)械手末端位置和姿態(tài)隨關(guān)節(jié)夾角之間的變換關(guān)系,并完成了總體機(jī)械結(jié)計(jì)、步進(jìn)電機(jī)選型、蝸輪蝸桿及帶傳動(dòng)比的確定以及部分重要零件的設(shè)計(jì)。工業(yè)機(jī)械手也是工業(yè)機(jī)器人的一個(gè)重要分支。這種系統(tǒng)的主要特點(diǎn)在于它的通用性和靈活性。機(jī)器人控制器的選擇，是由機(jī)器人所執(zhí)行的任務(wù)決定的。變結(jié)構(gòu)控制系統(tǒng)的特點(diǎn)是，在動(dòng)態(tài)控制過(guò)程中，系統(tǒng)的結(jié)構(gòu)根據(jù)系統(tǒng)當(dāng)時(shí)的狀態(tài)偏差及其各階導(dǎo)數(shù)的變化，以躍變的方式按設(shè)定的規(guī)律作相應(yīng)的改變，它是一類特殊的非線性控制系統(tǒng) [3]。在電動(dòng)執(zhí)行裝置中，有直流(DC)電機(jī)、交流(AC)電機(jī)、步進(jìn)電機(jī)和直接驅(qū)動(dòng)(DD)電機(jī)等實(shí)現(xiàn)旋轉(zhuǎn)運(yùn)動(dòng)的電動(dòng)機(jī)，以及實(shí)現(xiàn)直線運(yùn)動(dòng)的直線電機(jī)。它本身又包括動(dòng)力源、傳動(dòng)(減速)機(jī)構(gòu)、滾輪或連桿機(jī)構(gòu)。(3)工業(yè)機(jī)器人控制系統(tǒng)向基于PC機(jī)的開(kāi)放型控制器方向發(fā)展，便于標(biāo)準(zhǔn)化、網(wǎng)絡(luò)化。因此迫切需要解決產(chǎn)業(yè)化前期的關(guān)鍵技術(shù)，對(duì)產(chǎn)品進(jìn)行全面規(guī)劃，搞好系列化、通用化、模塊化設(shè)計(jì)，積極推進(jìn)產(chǎn)業(yè)化進(jìn)程。其次，它的制作完成一定可以極大的激發(fā)同學(xué)們對(duì)機(jī)器人技術(shù)的熱愛(ài)，提高對(duì)機(jī)器人技術(shù)的濃厚興趣，并吸引更多的同學(xué)投入到機(jī)器人設(shè)計(jì)與制作行列中來(lái)。 (4)多關(guān)節(jié)型機(jī)機(jī)械手 [1]。4) 機(jī)身，采用一個(gè)步進(jìn)電機(jī)和一對(duì)蝸輪蝸桿機(jī)構(gòu)來(lái)實(shí)現(xiàn)底座的回轉(zhuǎn)運(yùn)動(dòng)。3 機(jī) 械 手 的 數(shù) 學(xué) 模 型 機(jī)器人的數(shù)學(xué)基礎(chǔ)為了描述機(jī)器人本身各連桿之間、機(jī)器人和環(huán)境之間的運(yùn)動(dòng)關(guān)系，通常將它們當(dāng)成剛體，進(jìn)而研究各剛體之間的運(yùn)動(dòng)關(guān)系。 機(jī)器人的運(yùn)動(dòng)學(xué)方程本文研究的機(jī)械手是具有6個(gè)自由度的空間開(kāi)鏈機(jī)構(gòu)，它由一系列連桿通過(guò)轉(zhuǎn)動(dòng)關(guān)節(jié)串聯(lián)而成，關(guān)節(jié)的相對(duì)轉(zhuǎn)動(dòng)導(dǎo)致連桿的運(yùn)動(dòng)。（2） 移動(dòng)型 移動(dòng)型即兩手指相對(duì)支座作往復(fù)運(yùn)動(dòng)。夾緊裝置選擇常開(kāi)式夾緊裝置，它在電機(jī)的驅(qū)動(dòng)力的作用下機(jī)械手手抓實(shí)現(xiàn)張開(kāi)和閉和 [5] 。maxjT 小手臂擺動(dòng)電機(jī)的選擇 初步估計(jì)小手臂重量為 3Kg,設(shè)擺動(dòng)速度為 3r/min則小手臂折算到中軸上的轉(zhuǎn)動(dòng)慣量為 ?????????小手臂折算到中軸上的轉(zhuǎn)動(dòng)慣量為：??????小手臂擺動(dòng)電機(jī)的最大靜轉(zhuǎn)矩為 ?????選用常州寶馬集團(tuán)前楊電機(jī)電器有限公司的 45BF003 型電機(jī)? 其最大靜轉(zhuǎn)矩為 = ，能夠滿足機(jī)構(gòu)的要求 [7]。i1i帶輪的主動(dòng)論轉(zhuǎn)速為 =3r/min1n由式 V 一般在 5——25m/s160dV??取 V=20m/s ??查表得：選帶輪為 Y 型帶 =20d又 =1 ?2i 20????????取???V 帶基準(zhǔn)長(zhǎng)度 ????????查表得： 取 =200d [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=?mn又 =5r/min 總傳動(dòng)比 i= =?s ?msn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 =12i取三級(jí)傳動(dòng)比為 =13i蝸輪蝸桿的傳動(dòng)比為 ?39。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)。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。 integration of systems with different sampling and processing rates. Most plex robotic systems are actually amalgams of different processing devices, connected by a variety of methods. For example, our system consists of three separate putation systems: a parallel image processing puter。We have developed a new framework for puting opticflow robustly using an estimationtheoretic framework [40]. While this work does not specifically use these ideas, we have future plans to try to adapt this algorithm to such a framework.Our method begins with an implementation of the HornSchunck method of puting opticflow [22]. The underlying assumption of this method is the opticflow constraint equation, which assumes image irradiance at time t and t+σt will be the same:If we expand this constraint via a Taylor series expansion, and drop second and higherorder terms, we obtain the form of the constraint we need to pute normal velocity:Where u and U are the velocities in image space, and Ix, Iy, and It are the spatial and temporal derivatives in the image. This constraint limits the velocity field in an image to lie on a straight line in velocity space. The actual velocity cannot be determined directly from this constraint due to the aperture problem, but one can recover the ponent of velocity normal to this constraint line A second, iterative process is usually employed to propagate velocities in image neighborhoods, based upon a variety of smoothness and heuristic constraints. These added neighborhood constraints allow for recovery of the actual velocities u, v in the image. While putationally appealing, this method of determining opticflow has some inherent problems. First, the putation is done on a pixelbypixel basis, creating a large putational demand. Second, the information on optic flow is only available in areas where the gradients defined above exist.We have overe the first of these problems by using the PIPE image processor [26], [7]. The PIPE is a pipelined parallel image processing puter capable of processing 256 x 256 x 8 bit images at frame rate speeds, and it supports the operations necessary for opticflow putation in a pixel parallel method (a typical image operation such as convolution, warping, addition subtraction of images can be done in one cyclel/60 s). The second problem is alleviated by our not needing to know the actual velocities in the image. What we need is the ability to locate and quantify gross image motion robustly. This rules out simple diff。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 anism39。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，