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小型零件装配机械手设计【机+电】【三自由度】【9张图纸】【优秀】

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关键词：: 小型零件装配机械手设计三自由度图纸装配机械手

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小型零件装配机械手设计

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小型零件装配机械手设计【三自由度】.gif

摘要I

AbstractII

1 绪论1

1.1 机械手概述1

1.1.1 机械手的组成和分类1

1.1.2 国内外发展状况4

1.2 本课题研究的主要内容5

1.3 小型零件装配机械手的功能要求及现实意义6

1.3.1 小型零件装配机械手的功能要求6

1.3.2 小型零件装配机械手的现实意义6

2 机械手总体设计方案7

2.1 机械手的坐标型式与自由度7

2.1.1 机械手的坐标型式7

2.1.2 机械手的自由度8

2.2 总体设计方案8

2.2.1 传动行走机构8

2.2.2 驱动行走机构9

2.3 机械手的设计参数10

3 机械手机械系统设计12

3.1 X轴结构的设计与选择12

3.1.1X轴滚珠丝杆副尺寸选择计算12

3.1.2滚动轴承型号选择计算16

3.1.3驱动电机的选择计算17

3.1.4X轴键的选择计算18

3.2Y轴结构的设计与选择19

3.2.1滚珠丝杠副尺寸选择计算19

3.2.2滚动轴承型号选择计算23

3.2.3驱动电动机的选择计算25

3.2.4Y轴键的校核26

3.2.5二维工作台底板的选择及校核27

4 机械手控制系统设计29

4.1 控制系统的结构分类29

4.2 控制方式29

4.3机械手的控制电路30

4.3.1 增量式光电编码器辨向电路30

4.3.2增量式光电编码器计数电路31

4.3.3二相混合式步进电机的控制31

4.3.4二相混合式步进电机双极型驱动电路的设计31

4.3.5双极型驱动方式脉冲分配器的设计32

结论35

致谢36

参考文献37

机械手的设计参数

　　　1.主要参数

　　　机械手的最大抓取重量是其规格的主参数，该机械手主参数定为5kg。

　　　设计技术参数

（1)抓重：5kg

　　　(2)自由度：3个

　　　(3)坐标型式：直角坐标型

　　　(4)运动参数：X轴：最大行程1000mm，最大移动速度5m/min

　　　 Y轴：最大行程720mm，最大移动速度5m/min

　　　定位精度：0.1mm

　　　重复定位精度：0.05mm

　　　驱动方式：步进电机驱动

　　　希望寿命：15000h

1 绪论

1.1 机械手概述

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，它可以说是机器人的一个重要分支。它模仿人手的部分动作，按给定程序、轨迹和要求实现自动抓取、搬运或操作，涉及到力学、机械学、电器液压技术、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。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。

生产中应用机械手可以提高生产的自动化水平和劳动生产率；可以减轻劳动强度、保证产品质量、实现安全生产；尤其在高温、高压、低温、低压、粉尘、易爆、有毒气体和放射性等恶劣的环境中，它代替人进行正常的工作，意义更为重大。机械手作业的准确性和各种环境中完成作业的能力，在国民经济各个领域有着广阔的发展前景。

用机械手可以代替人从事单调、重复或繁重的体力劳动，实现生产的机械化和自动化，代替人在有害环境下的手工操作，改善劳动条件，保证人身安全。因此，在现有的机械手技术基础上设计一台小型零件机械装配机械手有着深远的意义。

1.1.1 机械手的组成和分类

1.1.1.1 机械手的组成

机械手主要由执行机构、驱动机构和控制系统位置检测装置等组成。

执行机构

（1）手部

手部安装在手臂的前端。手臂的内孔装有转动轴，可把动作传给手腕，以转动、伸屈手腕，开闭手指。

机械手手部的机构系模仿人的手指，分为无关节，固定关节和自由关节三种。手指的数量又可以分为二指、三指和四指等，其中以二指用的最多。可以根据夹持对象的形状和大小配备多种形状和尺寸的夹头，以适应操作需要。

本设计所做的机械手采用电磁铁直接吸引。

（2）手臂

手臂有无关节和有关节手臂之分。

本设计所做的机械手的手臂采用无关节臂。

手臂的作用是引导手指准确的抓住工件，并运送到所需要的位置上。为了使机械手能够正确的工作，手臂的三个自由度都需要精确的定位。

本设计所做的机械手在手臂的X轴、Y轴移动的定位均采用光电开关控制，以保证定位的精度。

机械手的运动离不开直线移动和转动二种，因此，它采用的执行机构主要是直线油缸、摆动油缸、电液脉冲马达、伺服油马达、直流伺服电机和步进电机等。

躯干是安装手臂、动力源和执行机构的支架。

2、驱动机构

驱动机构主要有四种：液压驱动、气压驱动、电气驱动和机械驱动。其中以液压气动用的最多，占90%以上，电动、机械驱动用的较少。

液压驱动主要是通过油缸、阀、油泵和油箱等实现传动。它利用油缸、马达加上齿轮、齿条实现直线运动；利用摆动油缸、马达与减速器、油缸与齿条、齿轮或链条、链轮等实现回转运动。液压驱动的优点是压力高、体积小、出力大、运动平缓，可无级变速，自锁方便，并能在中间位置停止。缺点是需要配备压力源，系统复杂成本较高。

气压驱动所采用的元件为气压缸、气压马达、气阀等。一般采用4-6个大气压，个别的达到8-10个大气压。它的优点是气源方便，维护简单，成本低。缺点是出力小，体积大。由于空气的可压缩性大，很难实现中间位置的停止，只能用于点位控制，而且润滑性较差，气压系统容易生锈。

为了减少停机时产生的冲击，气压系统装有速度控制机构或缓冲机构。

电气驱动采用的不多。现在都用三相感应电动机作为动力，用大减速比减速器来驱动执行机构；直线运动则用电动机带动丝杠螺母机构；有的采用直线电动机。通用机械手则考虑用步进电机、直流或交流的伺服电机、变速箱等。

电气驱动的优点是动力源简单，维护，使用方便。驱动机构和控制系统可以采用统一形式的动力，出力比较大；缺点是控制响应速度比较慢。

机械驱动只用于固定的场合。一般用凸轮连杆机构实现规定的动作。它的优点是动作确实可靠，速度高，成本低；缺点是不易调整。

本设计所做的机械手采用电动机带动丝杠螺母机构来实现手臂的X轴、Y轴的移动及X轴的旋转。

3、控制系统

机械手控制系统的要素，包括工作顺序、到达位置、动作时间

内容简介：: 徐州工程学院毕业设计外文翻译学生姓名学院名称机电工程学院专业名称机械设计制造及其自动化指导教师2011年5月27日 COMBINATION OF ROBOT CONTROL AND ASSEMBLY PLANNING FOR A PRECISION MANIPULATOORAbstract This paper researches how to realize the automatic assembly operation on a two-finger precision manipulator. A multi-layer assembly support system is proposed. At the task-planning layer, based on the computer-aided design (CAD) model, the assembly sequence is first generated, and the information necessary for skill decomposition is also derived. Then, the assembly sequence is decomposed into robot skills at the skill-decomposition layer. These generated skills are managed and executed at the robot control layer. Experimental results show the feasibility and efficiency of the proposed system. Keywords Manipulator Assembly planning Skill decomposition Automated assembly1 IntroductionOwing to the micro-electro-mechanical systems (MEMS) techniques, many products are becoming very small and complex, such as microphones, micro-optical components, and microfluidic biomedical devices, which creates increasing needs for technologies and systems for the automated precision assembly of miniature parts. Many efforts aiming at semi-automated or automated assembly have been focused on microassembly technologies. However, microassembly techniques of high flexibility, efficiency, and reliability still open to further research. Thispaper researches how to realize the automatic assembly operation on a two-finger micromanipulator. A multi-layer assembly support system is proposed. Automatic assembly is a complex problem which may involve many different issues, such as task planning, assembly sequences generation, execution, and control, etc. It can be simply divided into two phases; the assembly planning and the robot control. At the assembly-planning phase, the information necessary for assembly operations, such as the assembly sequence, is generated. At the robot control phase, the robot is driven based on the information generated at the assembly-planning phase, and the assembly operations are conducted. Skill primitives can work as the interface of assembly planning to robot control. Several robot systems based on skill primitives have been reported. The basic idea behind these systems is the robot programming. Robot movements are specified as skill primitives, based on which the assembly task is manually coded into programs. With the programs, the robot is controlled to fulfill assembly tasks automatically.A skill-based micromanipulation system has been developed in the authors lab, and it can realize many micromanipulation operations. In the system, the assembly task is manually discomposed into skill sequences and compiled into a file. After importing the file into the system, the system can automatically execute the assembly task. This paper attempts to explore a user-friendly, and at the same time easy, sequence-generation method, to relieve the burden of manually programming the skillsequence.It is an effective method to determine the assembly sequence from geometric computer-aided design (CAD) models. Many approaches have been proposed. This paper applies a simple approach to generate the assembly sequence. It is not involved with the low-level data structure of the CAD model, and can be realized with the application programming interface (API) functions that many commercial CAD software packages provide. In the proposed approach, a relations graph among different components is first constructed by analyzing the assembly model, and then, possible sequences are searched, based onthe graph. According to certain criterion, the optimal sequence is finally obtained. To decompose the assembly sequence into robot skill sequences, some works have been reported. In Nnaji et al.s work, the assembly task commands are expanded to more detailed commands, which can be seen as robot skills, according to a predefined format. The decomposition approach of Mosemann and Wahl is based on the analysis of hyperarcs of AND/OR graphs representing the automatically generated assembly plans. This paper proposes a method to guide the skill decomposition. The assembly processes of parts are grouped into different phases, and parts are at different states. Specific workflows push forward parts from one state to another state. Each workflow is associated with a skill generator. According to the different start state and target state of the workflow, the skill generator creates a series of skills that can promote the part to its target state.The hierarchy of the system proposed here ,the assembly information on how to assemble a product is transferred to the robot through multiple layers. The top layer is for the assembly-task planning. The information needed for the task planning and skill generation are extracted from the CAD model and are saved in the database. Based on the CAD model, the assembly task sequences are generated. At the skill-decomposition layer, tasks are decomposed into skill sequences. The generated skills are managed and executed at the robot control layer.2 Task planningSkills are not used directly at the assembly-planning phase. Instead, the concept of a task is used. A task can fulfill a series of assembly operations, for example, from locating a part, through moving the part, to fixing it with another part. In other words, one task includes many functions that may be fulfilled by several different skills. A task is defined as: T =（Base Part; Assembly Part; Operation） Base_Part and Assembly_Part are two parts that are assembled together. Base_Part is fixed on the worktable, while Assembly_Part is handled by robots end-effector and assembled onto the Base_Part. Operation describes how the Assembly_Part is assembled with the Base_Part; Operation Insertion_T, screw_T, align_T,.The structure of microparts is usually uncomplicated, and they can be modeled by the constructive solid geometry (CSG) method. Currently, many commercial CAD software packages can support 3D CSG modeling. The assembly model is represented as an object that consists of two parts with certain assembly relations that define how the parts are to be assembled. In the CAD model, the relations are defined by geometric constraints. The geometric information cannot be used directly to guide the assembly operationwe have to derive the information necessary for assembly operations from the CAD model. Through searching the assembly tree and geometric relations (mates relations) defined in the assemblys CAD model, we can generate a relation graph among parts, for example, In the graph, the nodes represent the parts. If nodes are connected, it means that there are assembly relations among these connected nodes (parts).2.1 Mating directionIn CSG, the relations of two parts, geometric constraints, are finally represented as relations between planes and lines, such as collinear, coplanar, tangential, perpendicular, etc. For example, a shaft is assembled in a hole. The assembly relations between the two parts may consist of such two constraints as collinear between the centerline of shaft Lc_shaft and the centerline of hole Lc_hole and coplanar between the plane P_Shaft and the plane P_Hole. The mating direction is a key issue for an assembly operation. This paper applies the following approach to compute the possible mating direction based on the geometric constraints (the shaft-in-hole operation of Fig. 3 is taken as an example): 1. For a part in the relation graph, calculate its remaining degrees of freedom,also called degrees of separation, of each geometric constraint. For the coplanar constraint, the remaining degrees of freedom are . For the collinear constraint, the remaining degrees of freedom are . and can also be represented as and . Here, 1 means that there is a degree of separation between the two parts. , and so, the degree of freedom around the z axis will be ignored in the following steps. In the case that there is a loop in the relation graph, such as parts Part 5, Part 6, and Part 7 in Fig. 2, the loop has to be broken before the mating direction is calculated. Under the assumption that all parts in the CAD model are fully constrained and not over-constrained, the following simple approach is adopted. For the part t in the loop, calculate the number of 1s in ; where is the remaining degrees of freedom of constraint k by part i. For example, in Fig. 2, given that the number of 1s in and is larger than and , respectively, then it can be regarded that the position of Part 7 is determined by constraints with both Part 5 and Part 6, while Part 5 and Part 6 can be fully constrained by constraints between Part 5 and Part 6.We can unite Part 5 and Part 6 as one node in the relation graph, also called a composite node, as shown in Fig. 2b. The composite node will be regarded as a single part, but it is obvious that the composite node implies an assembly sequence.2. Calculate mating directions for all nodes in the relation graph. Again, beginning at the state that the shaft and the hole are assembled, separate the part in one degree of separation by a certain distance (larger than the maximum tolerance), and then check if interference occurs. Separation in both x axis and y axis of R1 causes the interference between the shaft and the hole. Separation in the +z direction raises no interference. Then, select the +z direction as the mating direction, which is represented as a vector M measured in the coordinate system of theassembly. It should be noted that, in some cases, there may be several possible mating directions for a part. The condition for assembly operation in the mating direction to be ended should be given. When contact occurs between parts in the mating direction at the assembled state, which can be checked simply with geometric constraints, the end condition is measured by force sensory information, whereas position information is used as an end condition.3. Calculate the grasping position. In this paper, parts are handled and manipulated with two separate probes, which will be discussed in the Sect. 4, and planes or edges are considered for grasping. In the case that there are several mating directions, the grasping planes are selected as G1G2.Gi, where Gi is possible grasping plane/edge set for the ith mating direction when the part is at its free state. For example, in Fig. 4, the pair planes P1/P1, P2/P2, and P3/P3 can serve as possible grasping planes, and then the grasping planes are The approaching direction of the end-effector is selected as the normal vector of the grasping planes. It is obvious that not all points on the grasping plane can be grasped. The following method is used to determine the grasping area. The end-effector, which is modeled as a cuboid, is first added in the CAD model, with the constraint of coplanar or tangential with the grasping plane. Beginning at the edge that is far away from the Base_Part in the mating direction, move the end-effector in the mating direction along the grasping plane until the end-effector is fully in contact with the part, the grasping plane is fully in contact with the end-effector, or a collision occurs. Record the edge and the distance, both of which are measured in the parts coordinate system.4. Separate gradually the two parts along the mating direction, while checking interference in the other degrees of separation, until no interference occurs in all of the other degrees of separation. There is obviously a separation distance that assures interference not to occur in every degree of separation. It is called the safe length in that direction. This length is used for the collision-free path calculation, which will be discussed in the following section.2.2 Assembly sequenceSome criteria can be used to search the optimal assembly sequence, such as the mechanical stability of subassemblies, the degree of parallel execution, types of fixtures, etc. But for microassembly, we should pay more attention to one of its most important features, the limited workspace, when selecting the assembly sequence. Microassembly operations are usually conducted and monitored under microscopy, and the workspace for microassembly is very small. The assembly sequence brings much influence on the assembly efficiency. For example, a simple assembly with three parts. In sequence a, part A is first fixed onto part B. In the case that part C cannot be mounted in the workspace at the same time with component AB because of the small workspace, in order to assemble part C with AB, component AB has to be unmounted from the workspace. Then, component C is transported and fixed into the workspace. After that, component AB is transported back into the workspace again. In sequence b, there is no need to unmount any part. Sequence a is obviously inefficient and may cause much uncertainty. In other words, the greater the number of times of unmounting components required by an assembly sequence, the more inefficient the assembly sequence. In this paper, due to the small -workspace feature of microassembly, the number of times necessary for the mounting of parts is selected as the search criteria to find the assembly sequence that has a few a number of times for the mounting of parts as possible.This paper proposes the following approach to search the assembly sequence. The relation graph of the assembly is used to search the optimal assembly sequence. Heuristic approaches are adopted in order to reduce the search times:1. Check nodes connected with more than two nodes. If the mating directions of its connected nodes are different, mark them as inactive nodes, whereas mark the same mating directions as active mating direction.2. Select a node that is not an inactive node. Mark the current node as the base node (part). The first base part is fixed on the workspace with the mating direction upside (this is done in the CAD model). Compare the size (e.g., weight or volume) of the base part with its connected parts, which can be done easily by reading the bill of materials (BOM) of the assembly. If the base part is much smaller, then mark it as an inactive node.3. Select a node connected with the base node as an assembly node (part). Check the mating direction if the base node needs to be unmounted from the workspace. If needed, update a variable, say mount+. Reposition the component (note that there may be not only the base part in the workspace; some other parts may have been assembled with the base part) in the workspace so that the mating direction is kept upside. 4. In the CAD model, move the assembly part to the base part in the possible mating direction, while checking if interference (collision) occurs. If interference occurs, mark the base node as an inactive node and go to step 2, whereas select the Operation type according to parts geometric features. In this step, an Obstacle Box is also computed. The box, which is modeled as a cuboid, includes all parts in the workspace. It is used to calculate the collision-free path to move the assembly part, which will be introduced in the following section. The Obstacle Box is described by a position vector and its width, height, and length.5. Record the assembly sequence with the Operation type, the mating direction, and the grasping position.6. If all nodes have been searched, then mark the first base node as an inactive node and go to step 2. If not, select a node connected with the assembly node. Mark it as an assembly node, and the assembly node is updated as a base node. Check if there is one of the mating directions of the assembly node that is same as the mating direction of the former assembly node. If there is, use the former mating direction in the following steps. Go to step 3. After searching the entire graph, we may have several assembly sequences. Comparing the values of mount, the more efficient one can be selected. If not even one sequence is returned, then users may have to select one manually. If there are N nodes in the relation graph of Fig. 2b, all of which are not classed as inactive node, and each node may have M mating directions, then it needs MN computations to find all assembly sequences. But because, usually, one part only has one mating direction, and there are some inactive nodes, the computation should be less than MN. It should be noted that, in the above computation, several coordinate systems are involved, such as the coordinates of the assembly sequence, the coordinates of the base part, and the coordinates of the assembly. The relations among the coordinates are represented by a 44 transformation matrix, which is calculated based on the assembly CAD model when creating the relations graph. These matrixes are stored with all of the related parts in the database. They are also used in skill decomposition.3 Skill decomposition and execution3.1 Definition of skill primitiveSkill primitives are the interface between the assembly planning and robot control. There have been some definitions on skill primitives. The basic difference among these definitions is the skills complexity and functions that one skill can fulfill. From the point of view of assembly planning, it is obviously better that one skill can fulfill more functions. However, the control of a skill with many functions may become complicated. In the paper, two separate probes, rather than a single probe or parallel jaw gripper, are used to manipulate the part. Even for the grasp operation, the control process is not easy. In addition, for example, moving a part may involve not only the manipulator but also the worktable. Therefore, to simplify the control process, skills defined in the paper do not include many functions. More importantly, the skills should be easily applied to various assembly tasks, that is, the set of skills should have generality to express specific tasks. There should not be overlap among skills. In the paper, a skill primitive for robot control is defined as:Attributes_i Information necessary for Si to be executed. They can be classified as required attributes and option attributes, or sensory attributes and CAD-model-driven attributes. The attributes are represented by global variables used in different layers. Action_i Robots actions, which is the basic sensormotion. Many actions are defined in the system, such as Move_Worktable, Move_Probes, Rotation_Worktable, Rotation_Probes, Touch, Insert, Screw, Grasp, etc. For one skill, there is only one Action. Due to the limited space, the details of actions will not be discussed in this paper.Start_i The start state of Action_i, which is measured by sensor values.End_i The end state of Action_i, which is measured by sensor values.Condition_i The condition under which Action_i is executed.From the above definitions, we may find that skill primitives in the paper are robot motions with start state and end state, and that they are executed under specific conditions. Assembly planning in the paper is to generate a sequence of robot actions and to assign values to attributes of these actions.3.2 Skill decompositionSome approaches have been proposed for skill decomposition. This paper presents a novel approach to guide the skill decomposition. As discussed above, in the present paper, a task is to assemble the Assembly_Part with the Base_Part. We define the process from the state that Assembly_Part is at a free state to the state that it is fixed with the Base_Part as the assembly lifecycle of the Assembly_Part. In its assembly lifecycle, the Assembly_Part may be at different assembly states.Here shows a shafts states shown as blocks and associated workflows of an insertion task. A workflow consisting of a group of skills pushes forward the Assembly_Part from one state to another state. A workflow is associated with a specific skill generator that is in charge of generating skills. For different assembly tasks, the same workflows may be used, though specific skills generated for different tasks may be different.The system provides default task templates, in which default states are defined. These templates are imported into the system and instantiated after they are associated with the corresponding Assembly_Part. In some cases, some states defined by the default template may be not needed. For example, if the shaft has been placed into the workspace with accurate position, for example, determined by the fixture, then the Free and In_WS states can be removed from the shafts assembly lifecycle. The system provides a tool for users to modify these templates or generate their own templates. The tools user interface is displayed in. For a workflow, the start state is measured by sensory values, while the target state is calculated based on the CAD model and sensory attributes. According to the start state and the target state, the generator generates a series of skills. Here, we use the Move workflow in as an example to show how skills are generated.After the assembly task (assembly lifecycle) is initiated, the template is read into the Coordinator. For the workflow Move, its start state is Grasped, which implies that the Assembly_Part is grasped by the robots end-effector and, obviously, the position of the Assembly_Part is also obtained. Its target state is Adjusted, which is the state immediately before it is to be fixed with the Base_Part. At the Adjusted state, the orientation of the Assembly_Part is determined by the mating direction, while the position is determined by the Safe Length. These values have been calculated in the task planning layer and are stored in a database. When the task template is imported, these values are read into the memory at Coordinate and transformed into the coordinates of the workspace.There is an important and necessary step that has to be performed in the skill decomposition phasethe generation of a collision-free path. Here, we use a straight-line path, which is simple and easy calculated. Assume that P3 is the position of the Assembly_Part at the Adjusted state and P0 is the position at the Grasped state. The following approach is applied to generate the path:1. Based on the orientation of the Assembly_Part and mating direction, select skills (Rotate_Table or Rotate_Probes) to adjust the orientation of the part and assign values to the attributes of these skills.2. Based on the Obstacle Box, mating direction, real position/orientation of the Assembly_Part, the intermediate positions P1 and P2 need to be calculated.3. For each segment path, verify whether the Move_Table skill (for a large range) or the Move_Probe skill (for a small range) should be used.4. Generate skill lists for each segment and assign values to these skills.3.3 Execution of skillsAfter a group of skills which can promote the part to a specific state are generated, these skills are transferred to the Skill Management model. The system promotesone or several skills into the On Work Skill list and simultaneously dispatches them to the micromanipulator. Once the skill has been completed by the robot, the system removes it from the OnWork Task list and places it into the Completed Task list. After all of these skills have been completed, the state of the part is updated. For some states, skill execution and skill generation can be conducted in parallel. For example, for the Insertion lifecycle, if the parts position information is obtained, skills for the move workflow can be generated parallel with the execution of skills generated for the Grasp workflow.The assembly process is not closed to users. With the proposed skills management list structure, users can monitor and control the assembly process easily. For example, for the adjustment or the error recovery, users can suspend the ongoing skill to input commands directly or move the robot in a manual mode.4 Experiment4.1 Experimental platformThe experimental platform used in the paper. For microassembly operations, the precision and workspace are tradeoffs. In order to acquire both a large workspace and high precision, the two-stage control approach is usually used. These systems usually consist of two different sets of actuators; the coarse one, which is of large workspace but lower precision, and the fine one, which is of small workspace but higher precision. In our system, the large-range coarse motion is provided by a planar motion unit, with a repeatability of 2 m in the x and y directions, which is driven by two linear sliders made by NSK Ltd. The worktable can also provide a rotation motion around the z axis, which is driven by a stepper motor with a maximum resolution of 0.1/step.In the manipulator, two separate probes, rather than a single probe or parallel jaw grippers, are used to manipulate the miniature parts. The two probes are fixed onto two stepper motors with a maximum resolution of 0.05/step. The two motors are then fixed onto the parallel motion mechanism respectively. It is a serial connection of a parallel-hexahedron link and a parallelogram link. When the , and are small enough, the motion of the end-effector can be considered as linear motion.The magnetic actuator to drive the parallel mechanism consists of an air-core coil and a permanent magnet. The permanent magnet is attached to the parallel link, while the coil is fixed onto the base frame. The magnetic levitation is inherently unstable, because it is weak to external disturbances due to its non-contact operation in nature. To minimize the effect of external disturbances, a disturbance-observer-based method is used to control our micromanipulator.Laser displacement sensors are used to directly measure the probes position. The reflector is attached to the endeffector. Nano-force sensors produced by the BL AUTOTEC company are used to measure the forces. The position resolution of the micromanipulator is 1 um. The maximal resolution of the force is 0.8 gf, and the maximal resolution of the torque is 0.5 gfcm. A more detailed explanation on the mechanism of the manipulator can be found. All assembly operations are conducted under a microscope SZCTV BO61 made by the Olympus Company. The image information is captured by a Sharp GPBK PCI frame grabber, which works at 25 MHz.4.2 ExperimentAn assembly with three components is assembled with the proposed manipulator. It is a wheel of a micromobile robot developed in the authorslab. The following geometric constraints are defined in the CAD model: collinear between CL_cup and CL_axis, collinear between CL_gear and CL_axis, coplanar between Plane_cup and Plane_gear_1, coplanar between Plane_gear_1 and Plane_axis. According to the above geometric constraints, the three parts construct a loop in the relation graph. The CAD model is created with the commercial software Solidworks 2005, and its API functions are used to develop the assembly planning model. The assembly Information database is developed with Oracle 9.2. Models involved with skill generation are developed with Visual Basic 6.0. The skill-generation models are run withWindows 2000 on an HP workstation with a CPU of 2.0 G Hz and memory of 1.0 GB. Assuming that the positions of parts are available beforehand, it took about 7 min to generate the skill sequence. The generated assembly sequence is to assemble the gear onto the axis, and then assemble the cup onto the axis and the gear.In the assembly operation, the parts are placed on the worktable with special fixtures and then transported into the workspace, so that their initial position and orientation can be assured. Therefore, in the experiment, all of the skill sequences for the different parts can be generated and then transferred to the Skill Management unit. The skill istransmitted to the micromanipulator through TCP/IP communication. Because the controller of the micromanipulator is run on DOS, the WTTCP tools kit are adopted to develop the TCP/IP communication protocol.Because, currently, the automated control of the fixtures is not realized yet, the parts have to be fixed manually onto the worktable. The promotion between different tasks(assembly lifecycle of different parts) is conducted manually. Here shows some screenshots of the assembly process. In a, the axis is fixed in the workspace; in b, the gear is fixed in the workspace; from c to e, the gear is grasped, moved, and fixed onto the axis by the probes; in f, the cup is fixed in the workspace; from g to i, the cup is fixed with the gear and the axis. It can be found that the proposed system can perform the assembly successfully.5 ConclusionThis paper has introduced a skill-based manipulation system. The skill sequences are generated based on a computer-aided design (CAD) model. By searching the assembly tree and mate trees, an assembly graph is constructed. The paper proposes the approach to calculate the mating directions and grasping position based on the geometric constraints that define relations between different parts. Because the workspace of the micromanipulator is very small, the assembly sequence brings much influence on the assembly sequence. In the present paper, the number of required times of mounting parts in the workspace is selected as the criterion to select the optimal skill sequence.This paper presents a method to guide the skill decomposition. The assembly process is divided into different phases. In one phase, the part is at an assembly state. A specific workflow pushes the part forwards to its target state, which is the next desired state of the part in the assembly lifecycle and is calculated based on CAD model information and sensory information. A special skill generator is associated with the workflow to generate skills that promote the part to the target state. After the skill sequence is generated, the system dispatches them to the controller of the manipulator to drive the manipulator. An experiment has been performed to verify the proposed approaches. An assembly of three components is assembled. The experimental results prove the feasibility of the proposed system. The system is ready to be used for automated assembly operations. However, there are still many open issues to be solved in order to achieve fully automated assembly. For example, currently, the position of parts is assured by a special fixture on the worktable. Now a CAD-based image processing model is underdevelopment. It is expected that, with the aid of the image-processing model, the system can deal with parts with random positions and orientations.机器人控制和装配计划相结合的精密机械手摘要本文研究了如何实现对两个手指精密机械手进行自动装配操作。提出了一个多层次装配支持系统。在任务规划层的基础上，计算机辅助设计（CAD）模型、装配序列最先产生，必要的技能信息也从此分解而得。装配顺序为在技能分解层的机器人技能。这些技能，在机器人控制层进行管理和执行。实验结果表明了该系统的可行性和有效性。、关键词机械手；装配计划；程序分解；自动装配1简介由于微电子机械系统（MEMS）技术，许多产品都变得非常小而复杂的，如麦克风，微光学元件，微流体生物医学设备，创造增加了对微型零件的自动化精密装配技术和系统的需求。在半自动化或自动化装配中，许多努力都是以微组装技术为重点。然而，高灵活性，效率高和可靠性好的微组装技术，还是有待深入研究。这篇文章研究了如何实现对两个手指微操作机械手自动装配操作。提出了一个多层次装配支持系统。自动装配是一个复杂的问题，可能涉及许多不同的问题，如任务计划，装配顺序的产生，执行和控制等等。它可以简单地分为两个阶段：装配计划和机器人控制。在装配计划阶段，生成装配所需的信息，如装配顺序。在机器人控制阶段，由装配计划阶段产生的信息驱动机器人，并控制装配作业。程序可以作为装配计划到机器人控制的接口。基于程序基元的几个机器人系统已有报道。这些系统背后的基本理论是机器人编程。机器人的动作被指定为技术单元，在此基础上，装配的任务是手动编写程序。通过这些程序，机器人被控制去自动完成组装任务。一个以程序为基础的微操作系统已经在作者的实验室完成，它可以实现许多显微操作。在该系统中，装配任务被手动分成技能顺序，并且编译成一个文件。再导入文件到系统中，系统可自动执行装配任务。本文试图探讨一个好用，并且简单的顺序代法，以减轻手工编程的负担。它是一种有效的方法，从几何计算机辅助设计（CAD）模型确定装配顺序。许多方法已经被提出。本文采用一种简单的方法来生成装配序列。它与低层次的CAD数据模型结构无关，并且可以与许多商业CAD软件包提供应用程序编程接口（API）相连。在提出的方法中，不同的组件之间的关系图是首先通过分析装配模型而建立，然后，在图的基础上搜索可能的顺序。根据某些标准，最终得到最优顺序。把装配顺序分解成机器人技术顺序，有些作品被报道。在Nnaji et al.的作品中，按照事先确定的格式，装配任务命令被扩展到更详细的命令，它可以作为机器人技术出现。Mosemann和Wahl的分解方法是基于自动生成装配计划的图表AND/OR的分析。本文提出了一种方法来指导程序分解。零件的装配过程分成不同的阶段，并且部件处于不同的状态。具体的工作流程使部件从一个状态到另一个状态。每个工作流程与一个程序发生器相连。根据工作流程不同有开始状态和目标状态，程序生成器产生一系列的命令，使部件进一步到达它的最终状态。这里提出了系统的层次结构，如图1所示。关于如何通过多个层次利用机器人组装产品的转配信息。最上层是装配任务计划。任务计划和命令生成所需要的信息从CAD模型中提取，并保存在数据库中。在CAD模型的基础上，装配任务顺序生成。在命令分解层，任务分解为程序序列。生成的程序在机器人控制层管理和执行。 2任务计划程序是不能直接使用在装配计划阶段，而是使用任务。一个任务可以完成了装配的一系列操作，例如，从通过移动部件定位一个部件，到和另一个部件进行组装。换句话说，一个任务包括许多功能，它们可能通过几个不同程序来完成。一个任务是定义为： T =（基础部件，组装部件，操作）Base_Part和Assembly_Part是两个部件组装在一起。 Base_Part固定在工作台上，而Assembly_Part是通过机器人的抓取，组装到Base_Part上。操作描述了Assembly_Part与Base_Part如何组装。操作Insertion_T，screw_T，align_T ,. 对微细结构通常是简单的，他们可以通过实体几何（CSG）来建立模型。目前，许多商业CAD软件包支持3D CSG建模。装配模型包含两个部件，具有一定的装配关系定义如何将这两个部件进行组装。在CAD模型中，这种关系被几何约束条件限定。几何信息不能直接用于指导装配操作，我们能从CAD模型装配操作所需的必要信息。通过搜索装配CAD模型中定义的装配树和几何关系（配偶关系），我们可以生成一个零件之间的关系图。在图中，节点表示零件。如果节点连接，这意味着在连接的节点（零件）有装配关系。2.1装配方向在CSG中，两个零件之间的关系，几何约束，最终表示为平面与线条之间的关系，如共线、共面、相切、垂直等。轴与孔的装配，两个零件之间的装配关系包含两个约束：轴Lc_shaft的中心线与孔Lc_hole的中心线共线，平面P_Hole 与平面P_Shaft共面。装配方向是一个装配操作的关键问题。本文采用以下方法在几何约束的基础上计算可能的装配方向（以图3所示的轴孔操作为例子）： 1.对关系图中的一个零件，计算其每一个几何约束的剩余自由度，也称分离度。对于共面，剩余的自由度为。对于共线，剩余自由度为。R1 R2 也可以表示为。在这里，1表示是有两部分之间有一个的分离度。，因此，绕Z轴旋转的自由度度在下面的步骤将被忽略。还会出现一种循环关系在关系图中，例如图中的part5,part6和part7 ,在计算装配方向之前，循环关系必须打破。假设在CAD模型的各个部分都完全约束，而不是过度约束，采用下面简单的方法。循环中的partt，利用公式计算个数，这儿的是指在零件parti约束下的剩余自由度，举个例子，在图2中，和中1s的个数分别大于和，那么可以认为零件part7的位置是由part5和part6两部分约束确定的，5，并且零件part5和零件part6可以通过它们之间的约束而完全约束。在图中我们可以把零件part5和part6结合到一点，也叫复合节点。复合节点将被视为一个单独的部分，但很明显，复合节点意味着装配顺序。 2.在关系图中为所有节点的计算装配方向。再次，举个例子，轴孔装配的起始状态，在一个分离度中以一定的距离分离零件（大于最大公差），然后检查是否发生干扰。在R1的x轴与y轴的分离造成轴与孔的干扰。Z轴正方向的分离不会造成干扰。因此，选择z轴正方向作为装配方向，就是矢量M在装配坐标系统的测量。应该指出的是，在某些情况下，对于一个零件可能有几个可能的装配方向。对于装配方向即将结束的装配操作应该放在首位。装配状态时，在装配方向上，零件之间发生接触，可以通过几何约束很简单的检查出来，最后位置通过压力传感器测量，而位置信息作为最终条件。 3.计算抓的位置。在本文中，是通过两个独立探针来抓取操作零件，这将在第四部分进行讨论，而且抓取考虑零件的面或边缘。在这种情况下会有几个装配方向，通过G1G2.Gi寻找抓取平面，这儿的Gi指当零件处于自由状态时第i个装配方向上可能的抓取面或边。例如，在图4中，对面P1/P1，P2/P2和P3/P3都可以作为抓取平面，抓取平面为末端执行器接近的方向作为抓取平面的法向矢量量。很明显，并非所有平面上所有点都可以抓取。下面的方法用来确定抓面积。末端执行，器用长方体作为模型，首先添加到CAD模型中，与抓取平面共面或者相切。一开始在装配方向上抓取的边远离Base_Part，在装配方向上沿着抓取平面移动末端执行器直到末端执行器完全抓取零件，抓取平面与末端执行器完全接触，或发生碰撞。记下这条边及移动的距离，两者都是在零件的坐标系统中测量而得。 4.沿着装配方向逐步分离两个零件，同时检查在其他自由度的干扰，直到在其他自由度上没有干扰发生。显然一定的分离距离能保证在每一个自由度都不发生干扰。这就是所谓的装配方向上的安全长度。这个长度是用于无碰撞路径计算，这将在下一节讨论。2.2装配顺序有些依据可用于寻找最佳的装配顺序，如组件的机械稳定性，并联执行的准确度，固定装置等等。但对于微装配，在选择装配顺序时，我们应该注意它的一个重要特征有限的工作空间。微装配通常显微镜下进行控制和监测，并且微装配工作空间非常小。装配顺序给装配效率带来很大影响。例如，一个简单的三个零件的组装。在方案a中，零件A首先装配到B上，在这种情况下，因为空间小零件C不能与组件AB装配。为了将零件C与AB组装，组件AB不得不从工作区卸载。然后，再把C的转移和固定到工作区。之后，组件AB重新转移到工作区。在方案b中，没有卸载任何部件的必要。方案a显然是效率低下并且可能带来许多不确定性。换言之，在一个装配顺序中，组件的卸载次数越多，装配越没效率。在本文中，由于微装配小型工作区，部件的必要卸载次数选择尽可能卸载次数少的。本文提出以下办法来寻求装配顺序。装配关系图是用来寻找最佳的装配顺序。采用试探的方法，减少搜索时间： 1.检查与两个以上节点连接的节点。如果它连接节点的装配方向不同，把它们标记为非活动节点，而标记相同装配方向为活动装配方向。 2.选择一个不是非活动节点的节点。标记当前节点为基本节点（零件）。第一个零件固定在与装配方向相反的工作区域（这是在CAD模型进行）。把基础件的尺寸（例如重量或体积）与它的连接件相比较，这些尺寸可以通过组装材料清单（BOM）很容易得到。如果基础件相对非常小，就标记为非活动节点。 3.选择一个与基本节点相连的节点作为装配节点（零件）。如果基本节点需要从工作区卸载就检查装配方向。如果需要，更新一个变量，比如mount+ +，重新放置元件（注意，不仅有工作区中基础零件；也可能有已经组装的其他零件），以便装配方向保持向上。 4.在CAD模型，在可能的装配方向上，将装配件移动到基础件上，同时检查是否有干扰（碰撞）发生。如果发生干扰，标记这个基本节点为非活动节点，接着进入第2步，根据零件的几何特征选择操作方式。在这一步中，也计算一个Obstacle Box。这个盒子，模拟长一个长方体，包括了工作区中的所有部件。它是用来计算移动装配件时的无碰撞路径，这将在下一节介绍。这个Obstacle Box用一个位置矢量和它的宽度，高度和长度来描述。 5. 记录装配顺序，包括操作类型，装配方向，抓取定位。 6.如果搜寻所有节点之后，没有发现非活动节点，就标记第一基本节点为非活动节点，转到步骤2。如果还没有，选择与装配节点连接的节点。标记为一个装配节点，把这个装配节点作为一个基本节点。检查这个装配节点是否有一个装配方向与之前的装配方向之相同。如果有，在下面的步骤中使用之前的装配方向，然后转到步骤3。整个图形搜索完成后，我们可能有几个装配顺序。比较mount值，选择一个效率更高的方案。如果一个顺序都没反馈，用户可能不得不人为选择一个。如果在图2所示的关系图中有N个节点。所有这些都不是非活动节点，每个节点可能有M个装配方向，则需要MN次计算找出所有装配顺序。但是，因为通常情况下，一个零件只有一个装配方向和一些飞活动节点，计算应少于MN次。应该指出的是，在上述计算，涉及几个坐标系统，如装配顺序坐标，基础零件坐标和装配坐标。坐标之间由一个4 4变换矩阵来表示，这些都是在当我们创建关系图时在装配CAD模型的基础上计算得来。在数据库中矩阵与所有相关零部件存储在一起。他们还被用在程序分解。3程序分解和执行 3.1原始程序的定义原始程序使装配计划和机器人控制之间得以链接。已经有一些对于原始程序的定义。这些定义之间的根本区别是技能的复杂性以及它可以实现实现的功能。从装配计划的角度看，一个程序可以实现更多的功能显然更好。然而，对于能实现许多功能的程序的控制可能就会变得复杂。在论文中，用来操作的部分是两个不同的探针，而不是一个探头或平行颚爪。即使是抓取操作，控制过程也是不容易的。此外，像移动一个零件可能不仅涉及机械手，还有工作台。因此，为了简化控制过程，在论文中定义的程序不包括许多功能。更重要的是，技能应该很容易地应用到各种组装任务，即

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