机械臂动力学——毕业设计外文文献翻译、中英文翻译

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。

机械臂的外文文献以及翻译附件1：外文资料翻译译文机械手机械手是近几十年发展起来的一种高科技自动化生产设备。

工业机械手是工业机器人的一个重要分支。

它的特点是可通过编程来完成各种预期的作业任务，在构造和性能上兼有人和机器各自的优点，尤其体现了人的智能和适应性。

机械手作业的准确性和各种环境中完成作业的能力，在国民经济各领域有着广阔的发展前景。

随着工业自动化的发展, 出现了数控加工中心,它在减轻工人的劳动强度的同时, 大大提高了劳动生产率。

但数控加工中常见的上下料工序, 通常仍采用人工操作或传统继电器控制的半自动化装置。

前者费时费工、效率低; 后者因设计复杂, 需较多继电器,接线繁杂, 易受车体振动干扰,而存在可靠性差、故障多、维修困难等问题。

可编程序控制器PLC控制的上下料机械手控制系统动作简便、线路设计合理、具有较强的抗干扰能力, 保证了系统运行的可靠性,降低了维修率, 提高了工作效率。

机械手技术涉及到力学、机械学、电气液压技术、自动控制技术、传感器技术和计算机技术等科学领域，是一门跨学科综合技术。

一、工业机械手的概述机械手是一种能自动化定位控制并可重新编程序以变动的多功能机器，它有多个自由度，可用来搬运物体以完成在各个不同环境中工作。

在工资水平较低的中国，塑料制品行业尽管仍属于劳动力密集型，机械手的使用已经越来越普及。

那些电子和汽车业的欧美跨国公司很早就在它们设在中国的工厂中引进了自动化生产。

但现在的变化是那些分布在工业密集的华南、华东沿海地区的中国本土塑料加工厂也开始对机械手表现出越来越浓厚的兴趣，因为他们要面对工人流失率高，以及为工人交工伤费带来的挑战。

随着我国工业生产的飞跃发展，特别是改革开发以后，自动化程度的迅速提高，实现工件的装卸、转向、输送或操作钎焊、喷枪、扳手等工具进行加工、装配等作业自化，已愈来愈引起我们重视。

机械手是模仿着人手的部分动作，按给定的程序、轨迹和要求实现自动抓取、搬运或操作的自动机械装置。

外文出处：《Manufacturing Engineering and Technology—Machining》附件1：外文原文ManipulatorRobot developed in recent decades as high-tech automated production equipment. I ndustrial robot is an important branch of industrial robots. It features can be program med to perform tasks in a variety of expectations, in both structure and performance a dvantages of their own people and machines, in particular, reflects the people's intellig ence and adaptability. The accuracy of robot operations and a variety of environments the ability to complete the work in the field of national economy and there are broad p rospects for development. With the development of industrial automation, there has be en CNC machining center, it is in reducing labor intensity, while greatly improved lab or productivity. However, the upper and lower common in CNC machining processes material, usually still use manual or traditional relay-controlled semi-automatic device . The former time-consuming and labor intensive, inefficient; the latter due to design c omplexity, require more relays, wiring complexity, vulnerability to body vibration inte rference, while the existence of poor reliability, fault more maintenance problems and other issues. Programmable Logic Controller PLC-controlled robot control system for materials up and down movement is simple, circuit design is reasonable, with a stron g anti-jamming capability, ensuring the system's reliability, reduced maintenance rate, and improve work efficiency. Robot technology related to mechanics, mechanics, elec trical hydraulic technology, automatic control technology, sensor technology and com puter technology and other fields of science, is a cross-disciplinary integrated technol ogy.First, an overview of industrial manipulatorRobot is a kind of positioning control can be automated and can be re-programmed to change in multi-functional machine, which has multiple degrees of freedom can be used to carry an object in order to complete the work in different environments. Low wages in China, plastic products industry, although still a labor-intensive, mechanical hand use has become increasingly popular. Electronics and automotive industries thatEurope and the United States multinational companies very early in their factories in China, the introduction of automated production. But now the changes are those found in industrial-intensive South China, East China's coastal areas, local plastic processin g plants have also emerged in mechanical watches began to become increasingly inter ested in, because they have to face a high turnover rate of workers, as well as for the workers to pay work-related injuries fee challenges.With the rapid development of China's industrial production, especially the reform and opening up after the rapid increase in the degree of automation to achieve the wor kpiece handling, steering, transmission or operation of brazing, spray gun, wrenches a nd other tools for processing and assembly operations since, which has more and mor e attracted our attention. Robot is to imitate the manual part of the action, according to a given program, track and requirements for automatic capture, handling or operation of the automatic mechanical devices.In real life, you will find this a problem. In the machine shop, the processing of part s loading time is not annoying, and labor productivity is not high, the cost of producti on major, and sometimes man-made incidents will occur, resulting in processing were injured. Think about what could replace it with the processing time of a tour as long a s there are a few people, and can operate 24 hours saturated human right? The answer is yes, but the robot can come to replace it.Production of mechanical hand can increase the automation level of production and labor productivity; can reduce labor intensity, ensuring product quality, to achieve saf e production; particularly in the high-temperature, high pressure, low temperature, lo w pressure, dust, explosive, toxic and radioactive gases such as poor environment can replace the normal working people. Here I would like to think of designing a robot to be used in actual production.Why would a robot designed to provide a pneumatic power: pneumatic robot refers to the compressed air as power source-driven robot. With pressure-driven and other en ergy-driven comparison have the following advantages: 1. Air inexhaustible, used late r discharged into the atmosphere, does not require recycling and disposal, do not pollu te the environment. (Concept of environmental protection) 2. Air stick is small, the pipeline pressure loss is small (typically less than asphalt gas path pressure drop of one-thousandth), to facilitate long-distance transport. 3. Compressed air of the working pre ssure is low (usually 4 to 8 kg / per square centimeter), and therefore moving the mate rial components and manufacturing accuracy requirements can be lowered. 4. With th e hydraulic transmission, compared to its faster action and reaction, which is one of th e advantages pneumatic outstanding. 5. The air cleaner media, it will not degenerate, n ot easy to plug the pipeline. But there are also places where it fly in the ointment: 1. A s the compressibility of air, resulting in poor aerodynamic stability of the work, resulti ng in the implementing agencies as the precision of the velocity and not easily control led. 2. As the use of low atmospheric pressure, the output power can not be too large; i n order to increase the output power is bound to the structure of the entire pneumatic s ystem size increased.With pneumatic drive and compare with other energy sources drive has the followin g advantages:Air inexhaustible, used later discharged into the atmosphere, without recycling and disposal, do not pollute the environment. Accidental or a small amount of leakage wo uld not be a serious impact on production. Viscosity of air is small, the pipeline pressu re loss also is very small, easy long-distance transport.The lower working pressure of compressed air, pneumatic components and therefor e the material and manufacturing accuracy requirements can be lowered. In general, re ciprocating thrust in 1 to 2 tons pneumatic economy is better.Compared with the hydraulic transmission, and its faster action and reaction, which is one of the outstanding merits of pneumatic.Clean air medium, it will not degenerate, not easy to plug the pipeline. It can be saf ely used in flammable, explosive and the dust big occasions. Also easy to realize auto matic overload protection.Second, the composition, mechanical handRobot in the form of a variety of forms, some relatively simple, some more complic ated, but the basic form is the same as the composition of the , Usually by the implem enting agencies, transmission systems, control systems and auxiliary devices composed.1.Implementing agenciesManipulator executing agency by the hands, wrists, arms, pillars. Hands are crawlin g institutions, is used to clamp and release the workpiece, and similar to human finger s, to complete the staffing of similar actions. Wrist and fingers and the arm connecting the components can be up and down, left, and rotary movement. A simple mechanical hand can not wrist. Pillars used to support the arm can also be made mobile as needed .2. TransmissionThe actuator to be achieved by the transmission system. Sub-transmission system c ommonly used manipulator mechanical transmission, hydraulic transmission, pneuma tic and electric power transmission and other drive several forms.3. Control SystemManipulator control system's main role is to control the robot according to certain p rocedures, direction, position, speed of action, a simple mechanical hand is generally not set up a dedicated control system, using only trip switches, relays, control valves a nd circuits can be achieved dynamic drive system control, so that implementing agenc ies according to the requirements of action. Action will have to use complex program mable robot controller, the micro-computer control.Three, mechanical hand classification and characteristicsRobots are generally divided into three categories: the first is the general machinery does not require manual hand. It is an independent not affiliated with a particular host device. It can be programmed according to the needs of the task to complete the oper ation of the provisions. It is characterized with ordinary mechanical performance, also has general machinery, memory, intelligence ternary machinery. The second category is the need to manually do it, called the operation of aircraft. It originated in the atom, military industry, first through the operation of machines to complete a particular job, and later developed to operate using radio signals to carry out detecting machines suc h as the Moon. Used in industrial manipulator also fall into this category. The third cat egory is dedicated manipulator, the main subsidiary of the automatic machines or automatic lines, to solve the machine up and down the workpiece material and delivery. T his mechanical hand in foreign countries known as the "Mechanical Hand", which is t he host of services, from the host-driven; exception of a few outside the working proc edures are generally fixed, and therefore special.Main features:First, mechanical hand (the upper and lower material robot, assembly robot, handlin g robot, stacking robot, help robot, vacuum handling machines, vacuum suction crane, labor-saving spreader, pneumatic balancer, etc.).Second, cantilever cranes (cantilever crane, electric chain hoist crane, air balance th e hanging, etc.)Third, rail-type transport system (hanging rail, light rail, single girder cranes, doubl e-beam crane)Four, industrial machinery, application of handManipulator in the mechanization and automation of the production process develo ped a new type of device. In recent years, as electronic technology, especially comput er extensive use of robot development and production of high-tech fields has become a rapidly developed a new technology, which further promoted the development of ro bot, allowing robot to better achieved with the combination of mechanization and auto mation.Although the robot is not as flexible as staff, but it has to the continuous duplication of work and labor, I do not know fatigue, not afraid of danger, the power snatch weig ht characteristics when compared with manual large, therefore, mechanical hand has b een of great importance to many sectors, and increasingly has been applied widely, for example:(1) Machining the workpiece loading and unloading, especially in the automatic lat he, combination machine tool use is more common.(2) In the assembly operations are widely used in the electronics industry, it can be used to assemble printed circuit boards, in the machinery industry It can be used to ass emble parts and components.(3) The working conditions may be poor, monotonous, repetitive easy to sub-fatigue working environment to replace human labor.(4) May be in dangerous situations, such as military goods handling, dangerous go ods and hazardous materials removal and so on..(5) Universe and ocean development.(6), military engineering and biomedical research and testing.Help mechanical hands: also known as the balancer, balance suspended, labor-saving spreader, manual Transfer machine is a kind of weightlessness of manual load system, a novel, time-saving technology for material handling operations booster equipment, belonging to kinds of non-standard design of series products. Customer application ne eds, creating customized cases. Manual operation of a simulation of the automatic ma chinery, it can be a fixed program draws ﹑ handling objects or perform household to ols to accomplish certain specific actions. Application of robot can replace the people engaged in monotonous ﹑ repetitive or heavy manual labor, the mechanization and a utomation of production, instead of people in hazardous environments manual operati on, improving working conditions and ensure personal safety. The late 20th century, 4 0, the United States atomic energy experiments, the first use of radioactive material ha ndling robot, human robot in a safe room to manipulate various operations and experi mentation. 50 years later, manipulator and gradually extended to industrial production sector, for the temperatures, polluted areas, and loading and unloading to take place t he work piece material, but also as an auxiliary device in automatic machine tools, ma chine tools, automatic production lines and processing center applications, the comple tion of the upper and lower material, or From the library take place knife knife and so on according to fixed procedures for the replacement operation. Robot body mainly b y the hand and sports institutions. Agencies with the use of hands and operation of obj ects of different occasions, often there are clamping ﹑ support and adsorption type of care. Movement organs are generally hydraulic pneumatic ﹑﹑ electrical device dri vers. Manipulator can be achieved independently retractable ﹑ rotation and lifting m ovements, generally 2 to 3 degrees of freedom. Robots are widely used in metallurgic al industry, machinery manufacture, light industry and atomic energy sectors.Can mimic some of the staff and arm motor function, a fixd procedure for the capture, handling objects or operating tools, automatic operation device. It can replace hum an labor in order to achieve the production of heavy mechanization and automation th at can operate in hazardous environments to protect the personal safety, which is wide ly used in machinery manufacturing, metallurgy, electronics, light industry and nuclea r power sectors. Mechanical hand tools or other equipment commonly used for additio nal devices, such as the automatic machines or automatic production line handling an d transmission of the workpiece, the replacement of cutting tools in machining centers , etc. generally do not have a separate control device. Some operating devices require direct manipulation by humans; such as the atomic energy sector performs household hazardous materials used in the master-slave manipulator is also often referred to as m echanical hand.Manipulator mainly by hand and sports institutions. Task of hand is holding the wor kpiece (or tool) components, according to grasping objects by shape, size, weight, mat erial and operational requirements of a variety of structural forms, such as clamp type, type and adsorption-based care such as holding. Sports organizations, so that the com pletion of a variety of hand rotation (swing), mobile or compound movements to achie ve the required action, to change the location of objects by grasping and posture. Robot is the automated production of a kind used in the process of crawling and mo ving piece features automatic device, which is mechanized and automated production process developed a new type of device. In recent years, as electronic technology, esp ecially computer extensive use of robot development and production of high-tech fiel ds has become a rapidly developed a new technology, which further promoted the dev elopment of robot, allowing robot to better achieved with the combination of mechani zation and automation. Robot can replace humans completed the risk of duplication of boring work, to reduce human labor intensity and improve labor productivity. Manipu lator has been applied more and more widely, in the machinery industry, it can be use d for parts assembly, work piece handling, loading and unloading, particularly in the a utomation of CNC machine tools, modular machine tools more commonly used. At pr esent, the robot has developed into a FMS flexible manufacturing systems and flexibl e manufacturing cell in an important component of the FMC. The machine tool equipment and machinery in hand together constitute a flexible manufacturing system or a f lexible manufacturing cell, it was adapted to small and medium volume production, y ou can save a huge amount of the work piece conveyor device, compact, and adaptabl e. When the work piece changes, flexible production system is very easy to change wi ll help enterprises to continuously update the marketable variety, improve product qua lity, and better adapt to market competition. At present, China's industrial robot techno logy and its engineering application level and comparable to foreign countries there is a certain distance, application and industrialization of the size of the low level of robo t research and development of a direct impact on raising the level of automation in Ch ina, from the economy, technical considerations are very necessary. Therefore, the stu dy of mechanical hand design is very meaningful.附件1：外文资料翻译译文机械手机械手是近几十年发展起来的一种高科技自动化生产设备。

机械手臂应用领域的外文文献以及翻译1. Introduction机械手臂是一种用于执行各种任务的自动化设备，其应用领域广泛。

本文档提供了一些关于机械手臂应用领域的外文文献，并附有简要的翻译。

2. 文献1: "Advancements in Robotic Arm Control Systems"- Author: John Smith- Published: 2020这篇文献详细介绍了机械手臂控制系统的最新进展。

作者讨论了各种控制算法、传感器和执行器的应用，以提高机械手臂的性能和精确度。

3. 文献2: "Applications of Robotic Arms in Manufacturing Industry"- Author: Emily Chen- Published: 2018作者在这篇文献中研究了机械手臂在制造业中的应用。

她列举了多个实例，包括机械手臂在装配、焊接和搬运等任务中的应用，以及通过使用机械手臂能够提高生产效率和质量的案例。

4. 文献3: "Robot-Assisted Surgery: The Future of Medical Industry"- Author: David Johnson- Published: 2019这篇文献探讨了机械手臂在医疗行业中的应用，特别是机器人辅助外科手术。

作者解释了机械手臂在手术过程中的优势，包括更小的切口、更高的精确度和减少术后恢复时间等方面。

5. 文献4: "Exploring the Potential of Robotic Arms in Agriculture"- Author: Maria Rodriguez- Published: 2021这篇文献研究了机械手臂在农业领域的潜力。

作者探讨了机械手臂在种植、收割和除草等农业任务中的应用，以及如何通过机械化技术改善农业生产的效率和可持续性。