外文翻译机械手的机械和控制系统
机械外文翻译外文文献英文文献机械臂动力学与控制的研究
外文出处：Ellekilde, L. -., & Christensen, H. I. (2009). Control of mobile manipulator using the dynamical systems approach. Robotics and Automation, Icra 09, IEEE International Conference on (pp.1370 - 1376). IEEE.机械臂动力学与控制的研究拉斯彼得Ellekilde摘要操作器和移动平台的组合提供了一种可用于广泛应用程序高效灵活的操作系统,特别是在服务性机器人领域。
在机械臂众多挑战中其中之一是确保机器人在潜在的动态环境中安全工作控制系统的设计。
在本文中,我们将介绍移动机械臂用动力学系统方法被控制的使用方法。
该方法是一种二级方法, 是使用竞争动力学对于统筹协调优化移动平台以及较低层次的融合避障和目标捕获行为的方法。
I介绍在过去的几十年里大多数机器人的研究主要关注在移动平台或操作系统，并且在这两个领域取得了许多可喜的成绩。
今天的新挑战之一是将这两个领域组合在一起形成具有高效移动和有能力操作环境的系统。
特别是服务性机器人将会在这一方面系统需求的增加。
大多数西方国家的人口统计数量显示需要照顾的老人在不断增加,尽管将有很少的工作实际的支持他们。
这就需要增强服务业的自动化程度，因此机器人能够在室内动态环境中安全的工作是最基本的。
图、1 一台由赛格威RMP200和轻重量型库卡机器人组成的平台这项工作平台用于如图1所示,是由一个Segway与一家机器人制造商制造的RMP200轻机器人。
其有一个相对较小的轨迹和高机动性能的平台使它适应在室内环境移动。
库卡工业机器人具有较长的长臂和高有效载荷比自身的重量,从而使其适合移动操作。
当控制移动机械臂系统时,有一个选择是是否考虑一个或两个系统的实体。
在参考文献[1]和[2]中是根据雅可比理论将机械手末端和移动平台结合在一起形成一个单一的控制系统。
机械手控制系统
机械手电气控制系统1.机械手及其应用机械手：mechanical hand，也被称为自动手，auto hand能模仿人手和臂的某些动作功能，用以按固定程序抓取、搬运物件或操作工具的自动操作装置。
它可代替人的繁重劳动以实现生产的机械化和自动化，能在有害环境下操作以保护人身安全，因而广泛应用于机械制造、冶金、电子、轻工和原子能等部门。
机械手主要由手部、运动机构和控制系统三大部分组成。
手部是用来抓持工件（或工具）的部件，根据被抓持物件的形状、尺寸、重量、材料和作业要求而有多种结构形式，如夹持型、托持型和吸附型等。
运动机构，使手部完成各种转动（摆动）、移动或复合运动来实现规定的动作，改变被抓持物件的位置和姿势。
运动机构的升降、伸缩、旋转等独立运动方式，称为机械手的自由度。
为了抓取空间中任意位置和方位的物体，需有6个自由度。
自由度是机械手设计的关键参数。
自由度越多，机械手的灵活性越大，通用性越广，其结构也越复杂。
一般专用机械手有2～3个自由度。
1.1 国内外机械工业、铁路部门中机搬运械手主要应用于以下几方面1.热加工方面的应用热加工是高温、危险的笨重体力劳动，很久以来就要求实现自动化。
为了提高工作效率，和确保工人的人身安全，尤其对于大件、少量、低速和人力所不能胜任的作业就更需要采用机械手操作2.冷加工方面的应用冷加工方面机械手主要用于柴油机配件以及轴类、盘类和箱体类等零件单机加工时的上下料和刀具安装等。
进而在程序控制、数字控制等机床上应用，成为设备的一个组成部分。
最近更在加工生产线、自动线上应用，成为机床、设备上下工序联接的重要于段。
3. 拆修装方面拆修装是铁路工业系统繁重体力劳动较多的部门之一，促进了机械手的发展。
目前国内铁路工厂、机务段等部门，已采用机械手拆装三通阀、钩舌、分解制动缸、装卸轴箱、组装轮对、清除石棉等，减轻了劳动强度，提高了拆修装的效率。
近年还研制了一种客车车内喷漆通用机械手，可用以对客车内部进行连续喷漆，以改善劳动条件，提高喷漆的质量和效率。
多自由度机械手毕业论文中英文资料外文翻译文献
毕业论文中英文资料外文翻译文献专业机械设计制造及其自动化课题多自由度机械手机械设计英文原文Automated Tracking and Grasping of a Moving Object with a RoboticHand-Eye 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 real-time vision system capable of tracking moving objects in 3-D 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 hand-eye 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 organism'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 hand-eye coordination for grasping moving objects: fast computation of 3-D 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 complex 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 real-time vision systems capable of tracking moving objects in 3-D and a robot arm equipped with a dexterous hand that can be used to intercept, grasp, and pick up a movingobject. We are interested in exploring the interplay of hand-eye 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 organism'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 complex 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 hand-eye coordination for grasping moving objects: fast computation of 3-D 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 variety of complex robotic tasks that require visual feedback for arm and hand control.Our work also addresses a very fundamental and limiting problem that is inherent in building integrated sensing actuation systems; integration of systems with different sampling and processing rates. Most complex robotic systems are actually amalgams of different processing devices, connected by a variety of methods. For example, our system consists of three separate computation systems: a parallel image processing computer; a host computer that filters, triangulates, and predicts 3-D position from the raw vision data; and a separate arm control system computer that performs inverse kinematic transformations and joint-level servicing. Each of these systems has its own sampling rate, noise characteristics, and processing delays, which need to be integrated to achieve smooth and stable real-time performance. In our case, this involves overcoming visual processing noise and delays with a predictive filter basedupon a probabilistic analysis of the system noise characteristics. In addition, real-time arm control needs to be able to operate at fast servo rates regardless of whether new predictions of object position are available.The system consists of two fixed cameras that can image a scene containing a moving object (Fig. 1). A PUMA-560 with a parallel jaw gripper attached is used to track and pick up the object as it moves (Fig. 2). The system operates as follows:1) The imaging system performs a stereoscopic optic-flow calculation at each pixel in the image. From these optic-flow fields, a motion energy profile is obtained that forms the basis for a triangulation that can recover the 3-D position of a moving object at video rates.2) The 3-D position of the moving object computed by step 1 is initially smoothed to remove sensor noise, and a nonlinear filter is used to recover the correct trajectory parameters which can be used for forward prediction, and the updated position is sent to the trajectory-planner/arm-control system.3) The trajectory planner updates the joint-level servos of the arm via kinematic transform equations. An additional fixed-gain filter is used to provide servo-level control in case of missed or delayed communication from the vision and filtering system.4) Once tracking is stable, the system commands the arm to intercept the moving object and the hand is used to grasp the object stably and pick it up.The following sections of the paper describe each of these subsystems in detail along with experimental results.П. PREVIOUS WORKPrevious efforts in the areas of motion tracking and real-time control are too numerous to exhaustively list here. We instead list some notable efforts that have inspired us to use similar approaches. Burt et al. [9] have focused on high-speed feature detection and hierarchical scaling of images in order to meet the real-time demands of surveillance and other robotic applications. Related work has been reported by. Lee and Wohn [29] and Wiklund and Granlund [43] who uses image differencing methods to track motion. Corke, Paul, and Wohn [13] report afeature-based tracking method that uses special-purpose hardware to drive a servocontroller of an arm-mounted camera. Goldenberg et al. [16] have developed a method that uses temporal filtering with vision hardware similar to our own. Luo, Mullen, and Wessel [30] report a real-time implementation of motion tracking in 1-D based on Horn and Schunk’s method. Vergheseetul. [41] Report real-time short-range visual tracking of objects using a pipelined system similar to our own. Safadi [37] uses a tracking filter similar to our own and a pyramid-based vision system, but few results are reported with this system. Rao and Durrant-Whyte [36] have implemented a Kalman filter-based decentralized tracking system that tracks moving objects with multiple cameras. Miller [31] has integrated a camera and arm for a tracking task where the emphasis is on learning kinematic and control parameters of the system. Weiss et al. [42] also use visual feedback to develop control laws for manipulation. Brown [8] has implemented a gaze control system that links a robotic “head” containing binocular cameras with a servo controller that allows one to maintain a fixed gaze on a moving object. Clark and Ferrier [12] also have implemented a gaze control system for a mobile robot. A variation of the tracking problems is the case of moving cameras. Some of the papers addressing this interesting problem are [9], [15], [44], and [18].The majority of literature on the control problems encountered in motion tracking experiments is concerned with the problem of generating smooth, up-to-date trajectories from noisy and delayed outputs from different vision algorithms.Our previous work [4] coped with that problem in a similar way as in [38], using an cy- p - y filter, which is a form of steady-state Kalman filter. Other approaches can be found in papers by [33], [34], [28], [6]. In the work of Papanikolopoulos et al. [33], [34], visual sensors are used in the feedback loop to perform adaptive robotic visual tracking. Sophisticated control schemes are described which combine a Kalman filter’s estimation and filtering power with an optimal (LQG) controller which computes the robot’s motion. The vision system uses an optic-flow computation based on the SSD (sum of squared differences) method which, while time consuming, appears to be accurate enough for the tracking task. Efficient use of windows in the image can improve the performance of this method. The authors have presented good tracking results, as well as stated that the controller is robust enough so the use ofmore complex (time-varying LQG) methods is not justified. Experimental results with the CMU Direct Drive Arm П show that the methods are quite accurate, robust, and promising.The work of Lee and Kay [28] addresses the problem of uncertainty of cameras in the robot’s coordinate frame. The fact that cameras have to be strictly fixed in robot’s frame might be quite annoying since each time they are (most often incidentally) displaced; one has to undertake a tedious job of their recalibration. Again, the estimation of the moving object’s position and orientation is done in the Cartesian space and a simple error model is assumed. Andersen et al. [6] adopt a 3rd-order Kalman filter in order to allow a robotic system (consisting of two degrees of freedom) to play the labyrinth game. A somewhat different approach has been explored in the work of Houshangi [24] and Koivo et al. [27]. In these works, the autoregressive (AR) and auto grassier moving-average with exogenous input (ARMAX) models are investigated for visual tracking.Ш. VISION SYSTEMIn a visual tracking problem, motion in the imaging system has to be translated into 3-D scene motion. Our approach is to initially compute local optic-flow fields that measure image velocity at each pixel in the image. A variety of techniques for computing optic-flow fields have been used with varying results includingmatching-based techniques [5], [ 10], [39], gradient-based techniques [23], [32], [ 113, and patio-temporal, energy methods [20], [2]. Optic-flow was chosen as the primitive upon which to base the tracking algorithm for the following reasons.·The ability to track an object in three dimensions implies that there will be motion across the retinas (image planes) that are imaging the scene. By identifying this motion in each camera, we can begin to find the actual 3-D motion.·The principal constraint in the imaging process is high computational speed to satisfy the update process for the robotic arm parameters. Hence, we needed to be able to compute image motion quickly and robustly. The Hom-Schunck optic-flow algorithm (described below) is well suited for real-time computation on our PIPE image processing engine.·We have developed a new framework for computing optic-flow robustly using anestimation-theoretic 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 Horn-Schunck method of computing optic-flow [22]. The underlying assumption of this method is theoptic-flow 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 higher-order terms, we obtain the form of the constraint we need to compute 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 component of velocity normal to this constraint lineA 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 computationally appealing, this method of determining optic-flow has some inherent problems. First, the computation is done on a pixel-by-pixel basis, creating a large computational demand. Second, the information on optic flow is only available in areas where the gradients defined above exist.We have overcome the first of these problems by using the PIPE image processor [26], [7]. The PIPE is a pipelined parallel image processing computer capable of processing 256 x 256 x 8 bit images at frame rate speeds, and it supports the operations necessary for optic-flow computation in a pixel parallel method (a typical image operation such as convolution, warping, addition subtraction of images can be done in one cycle-l/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 differencing methodswhich are too prone to noise and will make location of image movement difficult. Hence, a set of normal velocities at strong gradients is adequate for our task, precluding the need to iteratively propagate velocities in the image.A. Computing Normal Optic-Flow in Real-TimeOur goal is to track a single moving object in real time. We are using two fixed cameras that image the scene and need to report motion in 3-D to a robotic arm control program. Each camera is calibrated with the 3-D scene, but there is no explicit need to use registered (i.e., scan-line coherence) cameras. Our method computes the normal component of optic-flow for each pixel in each camera image, finds a centurion of motion energy for each image, and then uses triangulation to intersect the back-projected centurions of image motion in each camera. Four processors are used in parallel on the PIPE. The processors are assigned as four per camera-two each for the calculation of X and Y motion energy centurions in each image. We also use a special processor board (ISMAP) to perform real-time histogram. The steps below correspond to the numbers in Fig. 3.1) The camera images the scene and the image is sent to processing stages in the PIPE.2) The image is smoothed by convolution with a Gaussian mask. The convolution operator is a built-in operation in the PIPE and it can be performed in one frame cycle. 3-4) In the next two cycles, two more images are read in, smoothed and buffered, yielding smoothed images Io and I1 and I2.The ability to buffer and pipeline images allows temporal operations on images, albeit at the cost of processing delays (lags) on output. There are now three smoothed images in the PIPE, with the oldest image lagging by 3/60 s.5) Images Io and I2, are subtracted yielding the temporal derivative It.6) In parallel with step 5, image I1is convolved with a 3 x 3 horizontal spatial gradient operator, returning the discrete form of I,. In parallel, the vertical spatial gradient is calculated yielding I, (not shown).7-8)The results from steps 5 and 6 are held in buffers and then are input to alook-up table that divides the temporal gradient at each pixel by the absolute value of the summed horizontal and vertical spatial gradients [which approximates thedenominator in (3)]. This yields the normal velocity in the image at each pixel. These velocities are then threshold and any isolated (i.e., single pixel motion energy) blobs are morphologically eroded. The above threshold velocities are then encoded as gray value 255. In our experiments, we threshold all velocities below 10 pixels per 60 ms to zero velocity.9-10) In order to get the centurion of the motion information, we need the X and Y coordinates of the motion energy. For simplicity, we show only the situation for the X coordinate. The gray-value ramp in Fig. 3 is an image that encodes the horizontal coordinate value (0-255) for each point in the image as a gray value.Thus, it is an image that is black (0) at horizontal pixel 0 and white (255) at horizontal pixel 255. If we logically and each pixel of the above threshold velocity image with the ramp image, we have an image which encodes high velocity pixels with their positional coordinates in the image, and leaves pixels with no motion at zero.11) By taking this result and histogram it, via a special stage of the PIPE which performs histograms at frame rate speeds, we can find the centurion of the moving object by finding the mean of the resulting histogram. Histogram the high-velocity position encoded images yields 256 16-bit values (a result for each intensity in the image). These 256 values can be read off the PIPE via a parallel interface in about 10 ms. This operation is performed in parallel to find the moving object’s Y censored (and in parallel for X and Y centurions for camera 2). The total associated delay time for finding the censored of a moving object becomes 15 cycles or 0.25 s.The same algorithm is run in parallel on the PIPE for the second camera. Once the motion centurions are known for each camera, they are back-projected into the scene using the camera calibration matrices and triangulated to find the actual 3-D location of the movement. Because of the pipelined nature of the PIPE, a new X or Y coordinate is produced every 1/60 s with this delay. While we are able to derive 3-D position from motion stereo at real-time rates, there are a number of sources of noise and error inherent in the vision system. These include stereo triangulation error, moving shadow s which are interpreted as object motion (we use no special lighting in the scene), and small shifts in censored alignments due to the different viewing angles of the cameras, which have a large baseline. The net effect of this is to create a 3-Dposition signal that is accurate enough for gross-level object tracking, but is not sufficient for the smooth and highly accurate tracking required for grasping the object.英文翻译自动跟踪和捕捉系统中的机械手系统摘要——许多机器人抓捕任务都被假设在了一个固定的物体上进行。
机械手外文翻译__修改版
密级分类号编号成绩本科生毕业设计 (论文)外文翻译原文标题Simple Manipulator And The Control Of It 译文标题简易机械手及控制作者所在系别机械工程系作者所在专业xxxxx作者所在班级xxxxxxxx作者姓名xxxx作者学号xxxxxx指导教师姓名xxxxxx指导教师职称副教授完成时间2012 年02 月北华航天工业学院教务处制译文标题简易机械手及控制原文标题 Simple Manipulator And The Control Of It作者机电之家译名JDZJ国籍中国原文出处机电之家中文译文：简易机械手及其控制随着社会生产力的持续进步和人们生活节奏的日益加快,人们对生产效率也提出了新要求。
而由于微电子技术和计算软、硬件技术的迅速发展和现代控制理论的不断完善,使得机械手技术也快速发展起来,其中气动机械手系统由于其介质来源简便且无污染、组件价格低廉、维修方便以及系统安全可靠等特点,已渗透到工业领域的各个部门,在工业发展中占有重要地位。
本文讲述的气动机械手由气控机械手、XY轴丝杠组、转盘机构、旋转基座等机械部分组成。
主要作用是完成机械部件的搬运工作,能使用于各种不同的生产线或物流流水线中,使得零件搬运、货物运输更快捷、便利。
一.四轴联动简易机械手的结构及动作过程机械手结构如下图1所示,有气控机械手(1)、XY轴丝杠组(2)、转盘机构(3)、旋转基座(4)等组成。
图1.机械手结构其运动控制方式为：(1)由伺服电机驱动可旋转角度为360°的气控机械手(有光电传感器确定起始0点);(2)由步进电机驱动丝杠组件使机械手沿X、Y轴移动(有x、y轴限位开关);(3)可回旋360°的转盘机构能带动机械手及丝杠组自由旋转(其电气拖动部分由直流电动机、光电编码器、接近开关等组成);(4)旋转基座主要支撑以上3部分;(5)气控机械手的张合由气压控制(充气时机械手抓紧,放气时机械手松开)。
铝合金机械手外文资料及中文译文
Aluminum multi-degreeof freedom manipulator Design and ImplementationMechanical hand, is also called from begins, auto hand can imitate the manpower and arm's certain holding function, with by presses the fixed routine to capture, the transporting thing 'OR' operation tool's automatic operation installment. It may replace person's strenuous labor to realize the production mechanization and the automation, can operate under the hostile environment protects the personal safety, thus widely applies in departments and so on machine manufacture, metallurgy, electron, light industry and atomic energy.The manipulator is mainly composed of the hand and the motion. The hand is uses for to grasp holds the work piece (or tool) the part, according to is grasped holds the thing shape, the size, the weight, the material and the work request has many kinds of structural styles, like the clamp, the request hold and the adsorption and so on. The motion, causes the hand to complete each kind of rotation (swinging), the migration or the compound motion realizes the stipulation movement, changes is grasped holds the thing position and the posture. Motion's fluctuation, the expansion, revolving and so on independence movement way, is called manipulator's degree-of-freedom. In order to capture in the space the optional position and the position object, must have 6 degrees-of-freedom. The degree-of-freedom is the key parameter which the manipulator designs. The degree-of-freedom are more, manipulator's flexibility is bigger, the versatility is broader, its structure is also more complex. Generally the special-purpose manipulator has 2~3 degrees-of-freedom.The manipulator's type, may divide into the hydraulic pressure type, the air operated according to the drive type, electromotive type, the mechanical manipulator; May divide into the special-purpose manipulator and the general-purpose manipulator two kinds according to the applicable scope; May divide into the position control and the continuous path according to the path control mode controls the manipulator and so on.The manipulator usually serves as the engine bed or other machine's add-on component, like on the automatic machine or the automatic production line loading and unloading and the transmission work piece, replaces the cutting tool in the machining center and so on, generally does not have the independent control device. Some operating equipment needs by the person direct control, if uses in the host who the atomic energy department manages the dangerous goods from the type operator also often being called the manipulator.Robot is a type of mechantronics equipment which synthesizes the last research achievement of engine and precision engine, micro-electronics and computer, automation control and drive, sensor and message dispose and artificial intelligence and so on. With the development of economic and the demand for automation control, robot technology is developed quickly and all types of the robots products are come into being. The practicality use of robot products not only solves the problems whichare difficult to operate for human being, but also advances the industrial automation program. At present, the research and development of robot involves several kinds of technology and the robot system configuration is so complex that the cost at large is high which to a certain extent limit the robot abroad use. To development economic practicality and high reliability robot system will be value to robot social application and economy development.With the rapid progress with the control economy and expanding of the modern cities, the let of sewage is increasing quickly: With the development of modern technology and the enhancement of consciousness about environment reserve, more and more people realized the importance and urgent of sewage disposal. Active bacteria method is an effective technique for sewage disposal，The lacunaris plastic is an effective basement for active bacteria adhesion for sewage disposal. The abundance requirement for lacunaris plastic makes it is a consequent for the plastic producing with automation and high productivity. Therefore, it is very necessary to design a manipulator that can automatically fulfill the plastic holding.With the analysis of the problems in the design of the plastic holding manipulator and synthesizing the robot research and development condition in recent years, a economic scheme is concluded on the basis of the analysis of mechanical configuration, transform system, drive device and control system and guided by the idea of the characteristic and complex of mechanical configuration, electronic, software and hardware. In this article, the mechanical configuration combines the character of direction coordinate and the arthrosis coordinate which can improve the stability and operation flexibility of the system. The main function of the transmission mechanism is to transmit power to implement department and complete the necessary movement. In this transmission structure, the screw transmission mechanism transmits the rotary motion into linear motion. Worm gear can give vary transmission ratio. Both of the transmission mechanisms have a characteristic of compact structure. The design of drive system often is limited by the environment condition and the factor of cost and technical lever. ''''The step motor can receive digital signal directly and has the ability to response outer environment immediately and has no accumulation error, which often is used in driving system. In this driving system, open-loop control system is composed of stepping motor, which can satisfy the demand not only for control precision but also for the target of economic and practicality. On this basis，the analysis of stepping motor in power calculating and style selecting is also given.The analysis of kinematics and dynamics for object holding manipulator is given in completing the design of mechanical structure and drive system. Kinematics analysis is the basis of path programming and track control. The positive and reverse analysis of manipulator gives the relationship between manipulator space and drive space in position and speed. The relationship bet ween manipulator’s tip position and arthrosis angles is concluded by coordinate transform method. The geometry method is used in solving inverse kinematics problem and the result will provide theory evidence for control system. The f0unction of dynamics is to get the relationship between the movement and force and the target is to satisfy the demand of real time control. in thischamfer, Newton-Euripides method is used in analysis dynamic problem of七he cleaning robot and the arthrosis force and torque are given which provide the foundation for step motor selecting and structure dynamic optimal ting.Control system is the key and core part of the object holding manipulator system design which will direct effect the reliability and practicality of the robot system in the division of configuration and control function and also will effect or limit the development cost and cycle. With the demand of the PCL-839 card, the PC computer which has a. tight structure and is easy to be extended is used as the principal computer cell and takes the function of system initialization, data operation and dispose, step motor drive and error diagnose and so on. A t the same time, the configuration structure features, task principles and the position function with high precision of the control card PCL-839 are analyzed. Hardware is the matter foundation of the control. System and the software is the spirit of the control system. The target of the software is to combine all the parts in optimizing style and to improve the efficiency and reliability of the control system. The software design of the object holding manipulator control system is divided into several blocks such as system initialization block, data process block and error station detect and dispose model and so on. PCL-839 card can solve the communication between the main computer and the control cells and take the measure of reducing the influence of the outer signal to the control system.The start and stop frequency of the step motor is far lower than the maximum running frequency. In order to improve the efficiency of the step motor, the increase and decrease of the speed is must considered when the step motor running in high speed and start or stop with great acceleration. The increase and decrease of the motor’s spee d can be controlled by the pulse frequency sent to the step motor drive with a rational method. This can be implemented either by hardware or by software. A step motor shift control method is proposed, which is simple to calculate, easy to realize and the theory means is straightforward. The motor'''' s acceleration can fit the torque-frequency curve properly with this method. And the amount of calculation load is less than the linear acceleration shift control method and the method which is based on the exponential rule to change speed. The method is tested by experiment.A t last, the research content and the achievement are sum up and the problems and shortages in main the content are also listed. The development and application of robot in the future is expected.多自由度铝合金机械手的设计与实现能模仿人手和臂的某些动作功能，用以按固定程序抓取、搬运物件或操作工具的自动操作装置。
毕业设计--工业机器人机械手及其控制系统设计
毕业设计工业机器人机械手及其控制系统设计Design of industrial robot manipulator and its control system系别：机械与汽车工程系专业名称：机械设计制造及其自动化i毕业设计任务书摘要工业机器人技术是近年来新技术发展的重要领域之一，是以微电子技术为主导的多种新兴技术与机械技术交叉、融合而成的一种综合性的高新技术。
这一技术在工业、农业、国防、医疗卫生、办公自动化及生活服务等众多领域有着越来越多的应用。
工业机器人在提高产品质量、加快产品更新、提高生产效率、促进制造业的柔性化、增强企业和国家的竞争力等诸多方面有着举足轻重的地位。
而机械手是工业机器人系统中传统的任务执行机构，是机器人的关键部件之一；是现代控制理论与工业生产自动化实践相结合的产物，并以成为现代机械制造生产系统中的一个重要组成部分；是提高生产过程自动化、改善劳动条件、提高产品质量和生产效率的有效手段之一。
尤其在高温、高压、粉尘、噪声以及带有放射性和污染的场合，应用得更为广泛。
本课题将设计一台四自由度的工业机器人，将会被用作自动送料装置。
主要工作部件及设计重点就是机械手。
第一，本人将设计该机器人的底座、大臂、小臂以及执行机构机械手爪的结构和模型；第二，再设计出适合于该机器人的驱动、传动方式，以期构成其的结构平台。
最后，在此基础上再将其控制系统设计出来，由下面几个步骤组成：数据采集卡和伺服放大器的选择、反馈方式和反馈元件的选择、端子板电路的设计以及控制软件的设计。
其中重点要加强控制软件的可靠性和机器人运行过程的安全性，最终要实现的目标包括：关节的伺服控制和制动问题、实时监测机器人的各个关节的运动情况、机器人的示教编程和在线修改程序、设置参考点和回参考点。
关键词：工业机器人；机械手；驱动；控制AbstractIndustrial robot technology is one of the important fields in the development of new technologies in recent years, is a cross, a variety of emerging technology and mechanical technology integration with microelectronics technology as the leading into a comprehensive high and new technology. This technology has been used more and more in the fields of industry, agriculture, national defense, medical, office automation and service life. Industrial robots play a decisive role in improving the quality of products, to speed up the update products, improve production efficiency, promote manufacturing flexibility, strengthen enterprise and national competitiveness etc. The manipulator is the traditional task execution mechanism of industrial robot system, is one of the key components of the robot; is a product of modern control theory and automation of industrial production practice, and to become an important part of modern mechanical manufacturing system; it is one of the effective ways to improve the production process automation, improve working conditions, to improve the product quality and production efficiency. Especially with a radioactive pollution in high temperature, high pressure, dust, noise and occasions, more widely applied.This topic will be the design of industrial robot with a four degree of freedom, will be used for the automatic feeding device. The main working parts and design focus is manipulator. First, the base, I will design the robot big arm, small arm and gripper actuator structure and model; second, redesign drive, drive mode suitable for the robot, in order to form the structure of platform. Finally, on the basis of the designed control system, consisting of the following steps: the design of data acquisition card and servo amplifier selection, feedback system and the feedback component selection, terminal board circuit design and control software. The key to strengthen the security of operation reliability and robot control software, to achieve the ultimate goals include: Joint servo control and brake problems, real-time monitoring the movement of each joint of robot, robot teaching programming and online modify the program, set the reference point and the reference point return.Key Words：Industrial robot; Manipulator; Drive; Control目录1绪论 (1)1.1工业机器人简介 (1)1.1.1发展史 (1)1.1.2特点 (1)1.1.3构造分类 (2)1.1.4 应用 (3)1.2国内外发展状况 (4)1.2.1 国外发展 (4)1.2.2 国内发展 (5)1.3工业机器人发展趋势 (5)2 工业机器人试验平台及机械手设计 (6)2.1机械手设计 (6)2.1.1机械手简介 (6)2.1.2 机械手分类 (6)2.1.3具体结构设计 (7)2.2工业机器人基座与连杆设计 (9)2.2.1基座的设计 (9)2.2.2大臂设计 (9)2.2.3小臂设计 (10)2.3工业机器人自由度及关节的设计 (10)2.4选择合适的驱动方式 (11)2.4.1电机驱动 (11)2.4.2液压驱动 (12)2.4.3气压驱动 (12)2.4.4驱动方式的确定 (13)2.5选择合适的传动方式 (13)2.6选择合适的制动器 (14)3控制系统硬件的组成 (15)3.1选择合适的控制系统模式 (15)I3.2建立合适的控制系统模型 (16)4控制系统软件的选取和设计 (19)4.1预期实现动作 (19)4.2实现手段 (19)4.2.1 各关节运动控制及监测 (19)4.2.2 直流电机伺服控制 (20)4.2.3 电机自锁 (20)4.2.4 程序的在线修改与示教控制 (22)4.2.5 参考点的设置 (22)5总结 (22)5.1设计经验 (22)5.2 误差分析 (23)5.3 总体评价 (23)致谢 (23)参考文献 (24)1绪论1.1工业机器人简介1.1.1发展史1920年由著名捷克斯洛伐克作家查培克所作剧本《罗萨姆的万能机器人》里第一次出现了“机器人”这个名词，但最初”Robot”一词是苦力的意思，指的是一台类人的且具有特殊功能的机器，为一种人造苦力。
工业机械手外文文献翻译、中英文翻译
第一章概述1. 1机械手的发展历史人类在改造自然的历史进程中，随着对材料、能源和信息这三者的认识和用，不断创造各种工具（机器），满足并推动生产力的发展。
工业社会向信息社会发展，生产的自动化，应变性要求越来越高，原有机器系统就显得庞杂而不灵活，这时人们就仿造自身的集体和功能，把控制机、动力机、传动机、工作机综合集中成一体，创造了“集成化”的机器系统——机器人。
从而引起了生产系统的巨大变革，成为“人——机器人——劳动对象”，或者“人——机器人——动力机——工作机——劳动对象”。
机器人技术从诞生到现在，虽然只有短短三十几年的历史，但是它却显示了旺盛的生命力。
近年来，世界上对于发展机器人的呼声更是有增无减，发达国家竞相争先，发展中国家急起直追。
许多先进技术国家已先后把发展机器人技术列入国家计划，进行大力研究。
我国的机器人学的研究也已经起步，并把“机器人开发研究”和柔性制造技术系统和设备开发研究等与机器人技术有关的研究课题列入国家“七五”、“八五”科技发展计划以及“八六三”高科技发展计划。
工业机械手是近代自动控制领域中出现的一项新技术，并已经成为现代机械制造生产系统中的一个重要组成部分。
这种新技术发展很快，逐渐形成一门新兴的学科——机械手工程。
1. 2机械手的发展意义机械手的迅速发展是由于它的积极作用正日益为人们所认识：其一、它能部分地代替人工操作；其二、它能按照生产工艺的要求，遵循一定的程序、时间和位置来完成工件的传送和装卸；其三、它能操作必要的机具进行焊接和装配。
从而大大地改善工人的劳动条件，显著地提高劳动生产率，加快实现工业生产机械化和自动化的步伐。
因而，受到各先进工业国家的重视，投入大量的人力物力加以研究和应用。
近年来随着工业自动化的发展机械手逐渐成为一门新兴的学科，并得到了较快的发展。
机械手广泛地应用于锻压、冲压、锻造、焊接、装配、机加、喷漆、热处理等各个行业。
特别是在笨重、高温、有毒、危险、放射性、多粉尘等恶劣的劳动环境中，机械手由于其显著的优点而受到特别重视。
外文翻译机械手机械和控制系统
本科毕业设计外文翻译题目机械手的机械和控制系统姓名谢百松学号20051103006 专业机械设计制造及其自动化指导教师肖新棉职称副教授中国·武汉二○○九年二月机械手的机械和控制系统文章来源:Dirk Osswald, Heinz Wörn.Department of Computer Science , Institute for Process Control and Robotics (IPR).,Engler-Bunte-Ring 8 - Building 40.28.摘要:最近，全球内带有多指夹子或手的机械人系统已经发展起来了，多种方法应用其上，有拟人化的和非拟人化的。
不仅调查了这些系统的机械结构,而且还包括其必要的控制系统。
如同人手一样，这些机械人系统可以用它们的手去抓不同的物体，而不用改换夹子。
这些机械手具备特殊的运动能力（比如小质量和小惯性），这使被抓物体在机械手的工作范围内做更复杂、更精确的操作变得可能。
这些复杂的操作被抓物体绕任意角度和轴旋转。
本文概述了这种机械手的一般设计方法，同时给出了此类机械手的一个示例，如卡尔斯鲁厄灵巧手Ⅱ。
本文末介绍了一些新的构想，如利用液体驱动器为类人型机器人设计一个全新的机械手。
关键词：多指机械手；机器人手；精操作；机械系统；控制系统1.引言2001年6月在德国卡尔斯鲁厄开展的“人形机器人”特别研究，是为了开发在正常环境（如厨房或客厅）下能够和人类合作和互动的机器人系统。
设计这些机器人系统是为了能够在非专业、非工业的条件下（如身处多物之中），帮我们抓取不同尺寸、形状和重量的物体。
同时，它们必须能够很好的操纵被抓物体。
这种极强的灵活性只能通过一个适应性极强的机械人手抓系统来获得，即所谓的多指机械手或机器人手。
上文提到的研究项目，就是要制造一个人形机器人，此机器人将装备这种机器人手系统。
这个新手将由两个机构合作制造，它们是卡尔斯鲁厄大学的IPR（过程控制和机器人技术研究院）和c（计算机应用科学研究院）。
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本科毕业设计外文翻译题目机械手的机械和控制系统专业机械设计制造及其自动化机械手的机械和控制系统文章来源:Dirk Osswald, Heinz Wörn.Department of Computer Science , Institute for Process Control and Robotics (IPR).,Engler-Bunte-Ring 8 - Building 40.28.摘要:最近，全球内带有多指夹子或手的机械人系统已经发展起来了，多种方法应用其上，有拟人化的和非拟人化的。
不仅调查了这些系统的机械结构,而且还包括其必要的控制系统。
如同人手一样，这些机械人系统可以用它们的手去抓不同的物体，而不用改换夹子。
这些机械手具备特殊的运动能力（比如小质量和小惯性），这使被抓物体在机械手的工作范围内做更复杂、更精确的操作变得可能。
这些复杂的操作被抓物体绕任意角度和轴旋转。
本文概述了这种机械手的一般设计方法，同时给出了此类机械手的一个示例，如卡尔斯鲁厄灵巧手Ⅱ。
本文末介绍了一些新的构想，如利用液体驱动器为类人型机器人设计一个全新的机械手。
关键词：多指机械手；机器人手；精操作；机械系统；控制系统1.引言2001年6月在德国卡尔斯鲁厄开展的“人形机器人”特别研究，是为了开发在正常环境（如厨房或客厅）下能够和人类合作和互动的机器人系统。
设计这些机器人系统是为了能够在非专业、非工业的条件下（如身处多物之中），帮我们抓取不同尺寸、形状和重量的物体。
同时，它们必须能够很好的操纵被抓物体。
这种极强的灵活性只能通过一个适应性极强的机械人手抓系统来获得，即所谓的多指机械手或机器人手。
上文提到的研究项目，就是要制造一个人形机器人，此机器人将装备这种机器人手系统。
这个新手将由两个机构合作制造，它们是卡尔斯鲁厄大学的IPR（过程控制和机器人技术研究院）和c（计算机应用科学研究院）。
这两个组织都有制造此种系统的相关经验，但是稍有不同的观点。
IPR制造的卡尔斯鲁厄灵巧手Ⅱ（如图1所示），是一个四指相互独立的手爪，我们将在此文中详细介绍。
IAI制造的手（如图17所示）是作为残疾人的假肢。
图1.IPR的卡尔斯鲁厄灵巧手Ⅱ图2. IAI开发的流体手2.机器人手的一般结构一个机器人手可以分成两大主要子系统：机械系统和控制系统。
机械系统又可分为结构设计、驱动系统和传感系统，我们将在第三部分作进一步介绍。
在第四部分介绍的控制系统至少由控制硬件和控制软件组成。
我们将对这两大子系统的问题作一番基本介绍，然后用卡尔斯鲁厄灵巧手Ⅱ演示一下。
3.机械系统机械系统将描述这个手看起来如何以及由什么元件组成。
它决定结构设计、手指的数量及使用的材料。
此外，还确定驱动器（如电动机）、传感器（如位置编码器）的位置。
3.1 结构设计结构设计将对机械手的灵活度起很大的作用，即它能抓取何种类型的物体以及能对被抓物体进行何种操作。
设计一个机器人手的时候，必须确定三个基本要素：手指的数量、手指的关节数量以及手指的尺寸和安置位置。
为了能够在机械手的工作范围内安全的抓取和操作物件，至少需要三根手指。
为了能够对被抓物体的操作获得6个自由度（3个平移和3个旋转自由度），每个手指必须具备3个独立的关节。
这种方法在第一代卡尔斯鲁厄灵巧手上被采用过。
但是，为了能够重抓一个物件而无需将它先释放再拾取的话，至少需要4根手指。
要确定手指的尺寸和安置位置，可以采用两种方法：拟人化和非拟人化。
然后将取决与被操作的物体以及选择何种期望的操作类型。
拟人化的安置方式很容易从人手到机器人手转移抓取意图。
但是每个手指不同的尺寸和不对称的安置位置将增加加工费用，并且是其控制系统变得更加复杂，因为每个手指都必须分别加以控制。
对于相同手指的对称布置，常采用非拟人化方法。
因为只需加工和构建单一的“手指模块”，因此可减少加工费用，同时也可是控制系统简化。
3.2 驱动系统指关节的驱动器对手的灵活度也有很大的影响，因为它决定潜在的力量、精度及关节运动的速度。
机械运动的两个方面需加以考虑：运动来源和运动方向。
在这方面，文献里描述了有几种不同的方法，如文献[3]中说可由液压缸或气压缸产生运动，或者，正如大部分情况一样使用电动机。
在多数情况下，运动驱动器（如电机）太大而不能直接与相应的指关节结合在一起，因此，这个运动必须由驱动器（一般位于机器臂最后的连接点处）转移过来。
有几种不同的方法可实现这种运动方式，如使用键、传动带以及活动轴。
使用这种间接驱动指关节的方法，或多或少地降低了整个系统的强度和精度，同时也使控制系统复杂化，因为每根手指的不同关节常常是机械地连在一起，但是在控制系统的软件里却要将它们分别独立控制。
由于具有这些缺点，因此小型化的运动驱动器与指关节的直接融合就显得相当必要。
3.3 传感系统机器手的传感系统可将反馈信息从硬件传给控制软件。
对手指或被抓物体建立一个闭环控制是很必要的。
在机器手中使用了3种类型的传感器：1. 手爪状态传感器确定指关节和指尖的位置以及手指上的作用力情况。
知道了指尖的精确位置将使精确控制变得可能。
另外，知道手指作用在被抓物体上的力，就可以抓取易碎物件而不会打破它。
2. 抓取状态传感器提供手指与被抓物体之间的接触状态信息。
这种触觉信息可在抓取过程中及时确定与物体第一次接触的位置点，同时也可避免不正确的抓取，如抓到物体的边缘和尖端。
另外还能察觉到已抓物体是否滑落，从而避免物体因跌落而损坏。
3. 物体状态或姿态传感器用于确定手指内物体的形状、位置和方向。
如果在抓取物体之前并不清楚这些信息的情况下，这种传感器是非常必要的。
如果此传感器还能作用于已抓物体上的话，它也能控制物体的姿态（位置和方向），从而监测是否滑落。
根据不同的驱动系统，有关指关节位置的几何信息可以在运动驱动器或直接在关节处出测量。
例如，如在电动机和指关节之间有一刚性联轴器，那么就可以用电机轴上的一个角度编码器（在齿轮前或齿轮后）来测量关节的位置。
但是如果此联轴器刚度不够或着要获得很高的精度的话，就不能用这种方法。
3.4卡尔斯鲁厄灵巧手Ⅱ的机械系统为了能够获得如重抓等更加复杂的操作，卡尔斯鲁厄灵巧手Ⅱ（KDH Ⅱ）由4根手指组成，且每根手指由3个相互独立的关节组成。
设计该手是为了能够在工业环境中应用（图3所示）和操纵箱、缸及螺钉螺帽等物体。
因此，我们选用四个相同手指，将它们作对称、非拟人化配置，且每个手指都能旋转90°（图4所示）。
鉴于从第一代卡尔斯鲁厄灵巧手设计中得到的经验，比如因传动带而导致的机械问题以及较大摩擦因数导致的控制问题，卡尔斯鲁厄灵巧手Ⅱ采用了一些不同的设计决策。
每根手指的关节2和关节3之间的直流电机被整合到手指前部肢体中（图5所示）。
这种布置可使用很硬的球轴齿轮将运动传递到手指的关节处。
处在电机轴上的角度编码器（在齿轮前）此时可作为一个精度很高的位置状态传感器。
图3.工业机器人上的KDHⅡ图4. KDHⅡ的顶视图为了感知作用在物体上的手指力量，我们发明了一个六维力扭矩传感器（图6所示）。
这个传感器可当作手指末端肢体使用，且配有一个球形指尖。
它可以抓取较轻的物体，同时也能抓取3-5kg相近的较重物体。
此传感器能测量X、Y和Z方向的力及绕相关轴的力矩。
另外，3个共线的激光三角测量传感器被安置在KDHⅡ的手掌上（图5所示）。
因为有3个这样的传感器，因此不仅可以测量3单点之间的距离，如果知道物体的形状，还能测出被抓物体表面之间的距离和方向。
物体状态传感器的工作频率为1kHz，它能检测和避免物体的滑落。
图5. KDHⅡ的侧视图图6. 带应变计量传感器的六自由度扭转传感器4. 控制系统机器人手的控制系统决定哪些潜在的灵巧技能能够被实际利用，这些技能都是由机械系统所提供的。
如前所述，控制系统可分为控制计算机即硬件和控制算法即软件。
控制系统必须满足以下几个的条件：1. 必须要有足够的输入输出端口。
例如，一具有9个自由度的低级手，其驱动器至少需要9路模拟输出端口，且要有9路从角度编码器的输入端口。
如再加上每个手指上的力传感器、触觉传感器及物体状态传感器的话，则端口数量将增加号几倍。
2. 需具备对外部事件快速实时反应的能力。
例如，当检测到物体滑落时，能立即采取相应的措施。
3. 需具备较高的计算能力以应对一些不同的任务。
如可以对多指及物体并行执行路径规划、坐标转换及闭环控制等任务。
4. 控制系统的体积要小，以便能够将其直接集成到操作系统当中。
5. 在控制系统与驱动器及传感器之间必须要电气短接。
特别是对传感器来说，若没有的话，很多的干扰信号将会干扰传感器信号。
4.1 控制硬件为了应对系统的要求，控制硬件一般分布在几个专门的处理器中。
如可通过一个简单的微控制器处理很低端的输入输出接口（马达和传感器），因此控制器尺寸很小，能轻易地集成到操纵系统中。
但是较高水平的控制端口则需要较高的计算能力，且需要一个灵活实时操作系统的支持。
这可以通过PC机轻易地解决。
因此，控制硬件常由一个非均匀的分布式计算机系统组成，它的一端是微控制器，而另一端则是一个功能强大的处理器。
不同的计算单元则通过一个通信系统连接起来，比如总线系统。
4.2 控制软件机器人手的控制软件是相当复杂的。
必须对要对手指进行实时及平行控制，同时还要计划手指和物体的新的轨迹。
因此，为了减少问题的复杂性，就有必要将此问题分成几个子问题来处理。
另一方面涉及软件的开发。
机器人手其实是一个研究项目，它的编程环境如用户界面，编程工具和调试设施都必须十分强大和灵活。
这些只能使用一个标准的操作系统才能得到满足。
在机械人中普遍使用的分层控制系统方法都经过了修剪，以满足机械手的特殊控制要求。
4.3卡尔斯鲁厄灵巧手Ⅱ的控制系统如在4.1节中所说，对于卡尔斯鲁厄灵巧手Ⅱ的控制硬件，采用了一种分布式方法（图7所示）。
一个微控制器分别控制一个手指的驱动器和传感器，另外一个微控制器用于控制物体状态传感器（激光三角传感器）。
这些微控制器（图7左侧和右侧的外箱）直接安装在手上，所以可以保证和驱动器及传感器之间较短的电气连接。
这些微控制器都是使用串行总线系统和主控计算机连在一起的。
这个主控计算机（图7、图8中的灰色方块）是由六台工业计算机组成的一个并行计算机。
这些电脑都被排列在一个二维平面。
相邻电脑模块（一台电脑最多有8个相邻模块）使用双端口RAM 进行快速通信（图7中暗灰色方块所示）。
一台电脑用于控制一个手指。
另一台用于控制物体状态传感器及计算物体之间的位置。
其余的电脑被安在前面提到的电脑的周围。
这些电脑用于协调整个控制系统。
控制软件的结构反映了控制硬件的架构。
如图9所示。
图7. KDH II的控制硬件构架图8.控制KDH II的平行主计算机一个关于此手控制系统的三个最高层次的网上计划正在规划。