机械手英语文献翻译.doc

1 英文文献翻译1.1 Cherry-harvesting robot1.1.1 IntroductionIn Japan, cherries are harvested carefully by human labor. As the harvesting season is short, the harvesting work is concentrated in a short time period and labor shortage tends to limit the farm acreage. Moreover, cherry trees are tall, and so the harvesting work must be conducted using pairs of steps. This makes harvesting dangerous and inefcient. To save on labor, a cherry-harvesting robot was manufactured for trial purposes and initial experiments were conducted. Research on fruit-harvesting robots has already been conducted (Kawamura etal., 1984; Harrell et al., 1990; Fujiura et al., 1990; Hanten et al.,2002). Many of the fruit-harvesting robots previously reported are equipped with a video camera. Fruit images are distinguished from the background by the difference in color or the spectral reectance. The 3-D location of the fruit was calculated using binocular stereo-vision (Kawamura et al., 1985)or by visual feedback control (Kondo and Endo, 1989). Applications of a 3-D vision sensor have also been reported (Subrata etal., 1996; Gao et al., 1997). The 3-D vision sensor has the advantage that each pixel of the image has distance information.Making use of this advantage, the object can be recognized by the 3-D shape. As for the cherry-harvesting work, it is necessary to harvest the fruit while avoiding collisions with obstacles such as leaves and stems. To obtain a successful harvesting motion, detection of obstacles as well as the red ripe fruit is required. To achieve this, a 3-D vision system that has two laser diodes was manufactured. One of them emits a red beam and the other an infrared beam. To prevent the inuence of the sunlight, position sensitive devices (PSDs) were used todetect the reected light. By blinking the laser beams at a high frequency, the signal components of the laser from PSDs were distinguished from that of the sunlight. The 3-D shape of the object was measured by scanning the laser beams and the red fruits were distinguished from other objects by the different cein the spectral-reection characteristics between the red and infrared laser beams. The robot needs to harvest correctly and efciently without damaging the fruits and branches under the environment (temperature, sunshine, etc.) of the orchard. Many cherry trees are cultivated in rain-cover vinyl tents to protect against rain. A robot that works in the tent is not exposed to wind and rain. Cherry fruit, both for the fresh market and for processing, must be harvested with its peduncle.In the case of manual harvesting, therefore, farmers grip the upper part of the peduncle with their ngers, and lift it upward to detach it from the tree. For the same reason, the robot manufactured for the experiment also gripped the upper part of the peduncle just like farmers and lifted it upward to detach the peduncle from the tree.1.1.2 Materials and methodsThe robot consists of a manipulator 4 degrees of freedom (DOF), a 3-D vision sensor, an end effector, a computer, and a traveling device (Fig. 2). It is about 1.2m high, 2.3m wide, and 1.2m long. The 3-D vision sensor is attached to the manipulator to scan from different viewpoints by the motion of the manipulator. A vacuum is used to suck the fruit into the sucking pipe of the end effector. Cherry trees cultivated by the method of single trunk training distribute their fruits around the main trunk. In order to harvest a fruit while avoiding obstacles, such as leaves and trunks, the end effector needs to approach the fruit from the outside of the trunk. For this reason, in this study, we manufactured an articulated manipulator with an axis of up-down traverse and three axes of right-left turning, so that the fruits could be harvested in any direction (Fig. 2). The up-down traverse requires comparatively large force caused by the gravity. Therefore, it is driven by an AC servomotor (Yaskawa Electric, SGMAH-01BAA2C, rated power 100W, rated torque 0.318Nm, rated speed 3000min1) and a screw mechanism (lead 10mm). Three axes of the rightleft turning do not require large torque. Axes of the rst and second rightleft turning are driven by small AC servomotors (Yaskawa Electric, SGMAH-A5BAA21, rated power 50W, rated torque 0.159N m,rated speed 3000min1) and harmonic reduction gears (reduction gear ratio 100). The remaining axis of rightleft turning is driven by a small DC motor with reduction gears. The manipulator is designed to be able to move round the circumference of the tree trunk so that not only fruits on the front side of the trunk but also the fruits on the other side of the trunk could be harvested.Since the fruits are located around the tree trunk, if the vision sensor scans from one viewpoint, fruits beyond the trunk are hidden. To scan from different viewpoints, the 3-D vision sensor was attached to the second arm. The movement of the manipulator changed the location and directionof the 3-D vision sensor so that the dead angle becomes small.The 3-D vision sensor is equipped with a light projector, a photo detector, and a scanning device (Fig. 3). The light projector consists of an infrared laser module, a red laser module, cold mirrors, a half mirror, and two full-reecting mirrors. The photo detector consists of two PSDs, a lens, and a red optical lter that weakens the inuence of sunlight. The scanning device consists of a galvanometer scanner and a stepping motor. The galvanometer scanner scans vertically and the stepping motor scans horizontally. Red and infrared laser beams are united in the same optical axis by a cold mirror that transmits infrared light and reects visible right. The beam is further split into two beams (each still including both wavelengths) by a half mirror. These two beams scan the object simultaneously. Light of the two beams reected from the object is focused onto two PSDs. The distance from the 3-D vision sensor to the object is calculated by a triangulation method using the ratio of the currents of both electrodes of the PSDs. The laser beams emit blinking signals in order to eliminate the inuence of sunlight.By this method, reected light is separated from the sunlight, thus resulting in continuous light. Infrared light with wavelengths about 7001000 nm is reected well by all parts of the cherry tree. On the other hand, red light at about 690 nm is not reected well by unripe fruit, leaves, and stalks, but is reected well by red ripe fruit. In this study, an infrared light beam of 830 nm and a red light beam of 690 nm were used. The infrared laser beam (830 nm) measures the distance to each part of the cherry tree from the 3-D vision sensor and the red laser beam(690 nm) detects the red fruit to be harvested. As mentioned above, the laser beam is split into two beams. The 3-D vision sensor scans these two beams simultaneously, and two pixels were measured at once to increase the scanning speed. The number of pixels was 50,000 (250 in the vertical and 200 in the horizontal direction). The scan time was 1.5 s. The eld of vision was 43.8 in vertical direction and 40.6 in horizontal direction. The effective range of the sensor was from170mmto 500mm. If the object was too far from the sensor, the detected light was weakened and the measuring accuracy was not good.The reected light from these laser beams is detected by two PSDs, one for each beam. The signals from the PSDs include red and infrared components. To acquire the red and infrared signals separately, the red and infrared laser lights were emitted at a blinking frequency of 41.6 kHz with a phase shift of 90. Photoelectric currents from PSDs are amplied. Red and infrared signals are detected separately using lock-in ampliers, which also eliminate the inuence of ambient light. The 3-D vision sensor can be used even under sunlight, where the illuminance is 100 klx. A red image and an infraredimage are fed to the computer, and then a range image and segmentation are obtained. The range image is calculated by triangulation using the infrared signals from anode A and B of the PSD. Segmentation is obtained from the ratio between the infrared and red signals. Red fruits were distinguished from other objects such as leaves by the reectivity of the red laser. However, the trunk as well as the fruits reect a red laser beam. Therefore, it was distinguished from fruits using other methods. Fruits reect with specula phenomenon. When they are scanned, the fruit center reects the laser beam well. How- ever, this phenomenon does not occur at the trunk surface. The center of each fruitwas recognized using this specula phenomenon. When the center of a fruit is visible from the 3-D vision sensor, fruits could be recognized by this method. By processing these images, the location of red fruits and obstacles, such as leaves and trunks, could be recognized.Fig. 4 shows examples of the image. The range image was obtained by the method of triangulation using the infrared signals of the PSD. By processing the infrared, red, and the range images, the object was segmented into red fruits and others. The image in the right side shows the result of segmentation. Cherry fruit must be harvested with its peduncle attached. The tensile strength needed to detach the fruit was measured. The strength between the peduncle and the fruit was about 1N. On the other hand, the strength between the peduncle and the branch was about 2.5N. Therefore, if the fruit was pulled it would detach the peduncle and the fruit because the strength in that area is the weakest. To harvest the fruit with its peduncle, a special end effector was used. It consisted of a fruit sucking device, an open-close mechanism, a back-and- forth mechanism, and a pair of ngers. It is about 80 mm high, 30 mm wide, and 145 mm long (Fig. 5). The vacuum pressure from the vacuum cleaner sucks the fruit so that the fruit position is xed at the tip of the pipe. The nger can be opened or closed by the rotation of a servomotor attached on the end effector. After the ngers grasp the peduncle, the end effector is lifted up to remove the peduncle from the tree.Fig. 6 shows the motion of the end effector. First, the ngers are opened and retracted by the servomotors. Then, the end effector approaches a fruit and sucks it. After sucking the fruit, the ngers move halfway forward, and close halfway until the clearance between ngers becomes 5mm. In order to enclose only the target fruit, the ngers are equipped with soft rubber components for obstacle exclusion, so that other fruits may not enter between the ngers. It is necessary to grip the peduncle as near as possible to its root . Therefore, after the ngers are closed halfway, they move further forward. Then, they close completely and grasp the peduncle. Finally, the end effector moves upward to detach the peduncle. The end effector moves to the position above a fruit box, and the ngers open and release the fruit. 1.2 樱桃采摘机器人1.2.1 简介在日本，采摘樱桃是一项细致的人工劳动。因为收获的季节是短暂的，收获工作必须在有限的短时间段内完成，所以劳力短缺成了限制农场发展扩大的主要因素。更甚者，樱桃树都很高，收获时需要大量的梯子之类的装置。这增加了收获工作的危险性，也使得工作和效率低下。为了节约劳动力，一种基于尝试性和理论性试验的机器人得到研制。对于采摘机器人的研究早就有人涉足（河村etal ， 1984 ; 哈瑞尔等人， 1990年;藤浦等人， 1990年; hanten等人， 2002年）。许多水果采摘机器人被研制前都被告知将装备摄像头。这个装置可以在复杂的颜色和其他的特殊环境中识别水果。水果的三维空间位置，经过双目立体视觉（河村建夫等人， 1985年）和视觉反馈控制（近藤和远藤， 1989年）计算出来。三维视觉传感器的应用早就得到研究（ subrata etal ， 1996年;高等人， 1997年） 。 三维视觉传感器有个好处可以获得每个像素的图像距离信息。利用这个优点，对象的立体形状被识别。关于樱桃收获工作，避开各种障碍无如叶子，枝条等来采摘到果实是必要的。为了获得一个好的采摘路线，检测障碍和检测成熟的果实是必须的。为了实现这一点，一个由两个激光二极管组成的三维视觉系统被研制。其中一个发出红色光束，而另外一个发出红外光束。相位灵敏探测系统被用来检测光线的反射以阻止太阳光线的影响。通过高频率的闪烁激光束，激光信号和太阳光线被相位灵敏探测系统分辨开来。通过分析激光输，成熟的果实从不同的对象里面被区分出来，对象的三维形状被测量出来。在不损害果实和树枝的完好性和复杂的果园环境（温度，阳光照射等）条件下，机器人要正确的，有效的采摘果实。许多樱桃树种在防止雨淋的塑料帐篷里面。在帐篷里工作的机器人可以避免遭受雨打风吹。不论是运进市场还是用来加工的樱桃必须连带肉茎采摘下来。在手工采摘时，农民用手指抓住樱桃肉茎的上部，然后向上稍微用力就可以把它摘下来。同样，采摘机器人也需要像农民一样抓住肉茎的上部，向上举起一点然后从树上把樱桃摘下来。1.2.2 材料和方法樱桃采摘机器人是由一个4自由度的机械臂，一个三维视觉传感器，一个末端执行器，一台电脑和一个行走装置组成(Fig. 2)。它大概1.2米高，2.3米宽，1.2米长。三维视觉传感器安装在机械手臂上，随着机械手臂的运动，它可以扫描到不同的方位。一个真空吸附器用来吸取果实到装在末端执行器的吸管里。樱桃树种植成一列列，它们的果实分布在主要树干的周围。机器人收获樱桃时需要避开叶子和树干，为了采摘到果实，末端执行器需要从树干的外部深入到采到果实的位置。因此，在这里，我们制造出一种农用机械手，它有一个可以做上下仰俯运动的轴和三个做左右摆动的轴。这样不论在哪个位置的果实都可以被采摘的到。那个可以做仰俯运动的轴要求相当大的驱动力来克服重力。所以，我们采用了交流伺服电机（yaskawa electric, SGMAH-01BAA2C,功率100w，转矩0.318n.m，转速3000r/min）和丝杠机构传动（导程10mm）。传递三个左右摆动的轴不需要大的转力矩。其中第一个和第二个传递左右摆动的轴由小的交流伺服电机驱动（yaskawa electric, A5BAA21，功率50w，转矩0.159n.m，转速3000r/min）和调波减速齿轮传动（传动比100）。最后一个传递左右摆动的轴是由一个小的直流电机通过减速齿轮传动的。设计的机械手可以绕树的主干周围移动 ，这样，不仅仅是只有在前面的樱桃可以被采摘到，在另一面的樱桃同样也可以被采摘到。由于果实是分布在树干周围的，如果视觉传感器只从一个视角扫描，那么在其他视角的果实可能是隐藏的。为了能够扫描到不同视角的果实，三维视觉传感器被安装在第二个 手臂上。机械手的运动改变了三维视觉传感器的位置和方向，这样死角的范围就很小了。三维就视觉传感器是由一个探照灯，一个图片检测器和一个扫描仪组成的(Fig. 3)。探照灯是有红外光束模块，红色光束模块，冷光镜，半反镜和两个偏转镜组成。图像检测器是由两个位置敏感器件，一个镜头和一个用来削弱太阳光线影响的红滤光片组成。扫描仪是由检流计扫描仪和卧式自动控制步进电机组成的。红色激光束和红外激光束通过传输红外光的冷光镜集成在相同的光纤上来反射可见的光线。光束通过半反镜进一步分散进两个光束（每个光束都包含两种波长）。这两个光束同时扫描对象。从对象反射回来的两个光束的光线被采集到两个位置敏感器件上。从三维视觉传感器到对象的距离通过三角测量法测量位置敏感器件的两级电流的方法计算出来。为了消除太阳光线的影响，激光束放射闪烁信号。樱桃树所有部位的波长在700-1000nm范围内的红外线被折射。另一方面，不熟的果实，叶子和茎放出的在690nm左右的红色光线折射的很少，相反，红色成熟的果实折射的很多。也就是说，波长在830nm的红外激光束和690nm的红色光束得到利用。通过三维视觉传感器，红外激光束（830nm）测量樱桃树每部分的距离，红色光束（690nm）探测要收获的红色果实。如上所述，激光波束分解成两部分。三维视觉传感器同时扫描被分解成的两部分，两个尺寸同时被测得以增加扫描速度。尺寸的具体数字是50000（垂直方向是250，水平方向是200）。扫描时间是1.5s。视角是垂直方向43.8，水平方向是40.6。传感器的有效范围是170mm500mm。如果对象距离传感器太远的话，探测射线会渐渐变弱，同时测量的精度也变差。从激光光束折射的光线将分别被两个位置敏感器件探测到。位置敏感器件得到的信号包括红色和红外的元素。为了获取红色波和红外波的信号，红色光束和红外激光光束在闪烁频率为41.6KHz波段的发射并且偏移相位为90。位置敏感器件获得的图片信息流得以放大滤波。红光和红外信号由自然放大滤波器分别独立探测，这个滤波器也会剔除周围自然光线的影响。三维视觉传感器可以用于照明度为100klx的阴暗处。红光和红外光的图像反馈到电脑，这样就取得了递变的图像。变化的图像通过三角测量法计算使用位置敏感器件得到的节点A和B处的红外信号。不同时段的信息从红光和红外信号的比率中获取。通过红色激光束，红色果实和叶子等其他的对象被区分开来。但是，树干和果实 反射一种红外线波束。因此，用其他方法可以区分果实。果实反射窥镜现象。当他们被扫描，果实中心反射红外波束良好。但是这种现象不会发生在树干表面。利用这种窥镜现象每个果实的中心被识别。当果实的中心通过三维视觉传感器是可见的，那么通过窥镜现象果实将被识别。处理完这些图像，红果和障碍物（如叶子和树干）的位置将被识别。Fig.4 是实例图像。通过三角测量法测得，利用位置敏感器件的红外信号，变化的图像被获取。通过处理红外线，红色光线和变化的图像，被探测物将被分为红色果实和其他对象两类。在右边的图像显示了分类的结果。樱桃采摘时必须连带肉根茎一起摘下。这个使肉根茎分离果实的拉力已经测量过了。梗和果实间的拉力大约是1N。另一方面，梗和树枝之间的拉力大概是2.5N。因此，如果果实是被拽下的，那么梗会和果实分离，因为在他们之间的拉力是最弱的。为了采摘下带梗的樱桃，一个特殊的末端执行器被使用。它由一个水果吸附装置，一个开合装置，一个伸缩装置和一对手指组成。它大约有80mm高，30mm宽，145mm长（Fig.5）。由真空吸附器产生的真空压力吸附果实，这样果实的位置就固定在了吸管的端部。通过安装在末端执行器上的伺服电机的旋转驱动，手指是可以开合。手指抓到梗部之后，末端执行器会举起一些以便从树上摘下带梗的樱桃。Fig.6演示了末端执行器的运动轨迹。首先，手指在伺服电机的驱动下张开和伸缩。然后，末端执行器靠近一个果实并吸附它。吸到果实之后，手指向前移动他们之间的一半的距离，然后再移动他们之间的一半的距离直到手指之间的间隙为5mm。为了抓到目标果实，手指设计了软摩擦部件以减小障碍，这样其他果实可能就不会在两手指之间了。尽可能近的抓到梗的根部是有必要的。因此，在手指接近的途中，它们一步一步的向前推进。然后，它们完全接近并抓取梗不。最后，末端执行器向上移动以便使梗分离树枝。末端执行器移动到装果实的箱子上方，手指张开，释放果实到箱子里。2 专业阅读书目2.1 仿人机器人发展及其技术探索内容摘要：仿人机器人是研究人类智能的高级平台，它是综合机械、电子、计算机、传感器、控制技术、人工智能、仿生学等多种学科的复杂智能机械，目前已成为机器人领域的研究热点问题之一。对国内外仿人机器人研究现状进行广泛调研，其中日、美等国在研制仿人机器人方面做了大量的工作，中国各高校也积极研究，取得了突破性进展。概括并分析机器人自由度配置、步态规划的分类、基于零力矩点的稳定性判据、传感器的分类和应用以及机器人控制系统等关键技术。于秀丽.机械工程学报C.北京：北京邮电大学,20092.2 机械式导向探测装置内容摘要：拖拉机自动导向能提高行驶轨迹的精度，提高行间作业质量，减轻驾驶员劳动强度。机械式拖拉机自动导向利用田间的作物、秸秆或垄沟等进行接触探测，机构简单、成本低、易维护。设计针对玉米秸秆行间作业的低成本机械式导向探测装置。通过对导向探测器具有的特点进行分析，确定触杆的对称结构和偏心半椭圆形状特征，在对触杆进行受力分析的基础上确定触杆形状的关键参数即偏心矩。经过试验表明这种形式的导向探测装置及角位移传感器能实现拖拉机在秸秆行间的导向探测，并对秸秆无破坏。何卿.机械工程学报C.北京：中国农业大学工学院，20072.3 RGRR-构造混联6R机器人内容摘要：新型并联双自由度转动关节(RGRR-)活动构件少、工作空间大，利用RGRR-构造混联6R机器人可以实现高刚度、大工作空间、机构紧凑等。提出混联机构的求解新方法，即建立少自由度并联机构的运动坐标参数与机构运动输入参数之间的关系，把混联机构的求解转化为各少自由度并联机构的求解和运动坐标参数之间的串联求解，降低了求解难度，可以方便地推导出位置的正解和反解。此方法对其他混联机器人的运动学分析有借鉴作用。姜铭.机械工程学报C. 上海：东南大学机械工程学院，20102.4 大学生机械设计竞赛指导内容摘要：主要介绍了大学生在参加机械设计竞赛中常用的设计、加工方法；着重讲解了原理方案的构思，包括抓取方案、行走方案、搬运方案、越障方案、提升方案、攀爬方案以及创新设计等。在机械本体制作过程中，作者根据多年的指导经验，讲述了常用零件及其设计、机械加工基本知识，常用工具及其使用、竞赛中常用的设计、加工及装配技巧。为了做到对作品的灵活控制，作者用了一定的篇幅，详细讲解了电机的选择，以及如何控制直流电机、舵机和步进电机，并给同学们提供了一种适用于大学生机械设计竞赛的控制平台（包括控制电路板和遥控板）。为了方便大家完成控制部分的设计，本书还列出了控制部分的详细代码。针对同学们在撰写理论方案中出现的种种问题，作者对理论方案的撰写做了一些介绍，并做了举例说明。赵明岩.大学生机械设计竞赛指导C. 江苏：浙江大学出版社，20082.5 机械手图册内容摘要：现代工业机械手起源于20世纪50年代初，是基于示教再现和主从控制方式、能适应产品种类变更，具有多自由度动作功能的柔性自动化产品。机械手首先是从美国开始研制的。1958年美国联合控制公司研制出第一台机械手。他的结构是：机体上安装一回转长臂，端部装有电磁铁的工件抓放机构，控制系统是示教型的。1962年，美国机械铸造公司在上述方案的基础之上又试制成一台数控示教再现型机械手。商名为Unimate(即万能自动)。运动系统仿造坦克炮塔，臂回转、俯仰，用液压驱动；控制系统用磁鼓最存储装置。不少球坐标式通用机械手就是在这个基础上发展起来的。同年该公司和普鲁曼公司合并成立万能自动公司（Unimaton）,专门生产工业机械手。随着工业机器手（机器人）研究制造和应用的扩大，国际性学术交流活动十分活跃，欧美各国和其他国家学术交流活动开展很多。加藤一郎.机械手图册M. 上海：上海科技技术出版社，20042.6 工业机械手内容摘要：1.手部 即直接与工件接触的部分，一般是回转型或平移型(为回转型，因其结构简单）。手爪多为两指（也有多指）；根据需要分为外抓式和内抓式两种；也可用负压式或真空式的空气吸盘（它主要用于吸取冷的，光滑表面的零件或薄板零件）和电磁吸盘。传力机构型式较多，常用的有：滑槽杠杆式、连杆杠杆式、斜楔杠杆式、轮齿条式、丝杠螺母式、弹簧式和重力式。2.腕部 是连接手部和手臂的部件，并可用来调整被抓物体的方位（即姿态）。它可以有上下摆动，左右摆动和绕自身轴线的回转三个运动。如有特殊要求（将轴类零件放在顶尖上，将筒类、盘类零件卡在卡盘上等），手腕还可以有一个小距离的横移。也有的工业机械手没有腕部自由度。3.臂部 手臂是支承被抓物、手部、腕部的重要部件。手部的作用是带动手指去抓取物体，并按预定要求将其搬到预定的位置。手臂有三个自由度，可采用直角坐标（前后、上下、左右都是直线）,圆柱坐标（前后、上下直线往复运动和左右旋转），球坐标（前后伸缩、上下摆动和左右旋转）和多关节（手臂能任意伸屈）四种方式。李允文.工业机械手设计M. 北京：机械工业出版社，19942.7 机器人智能技术内容摘要