喜歡這套資料就充值下載吧。資源目錄里展示的都可在線預覽哦。下載后都有，請放心下載，文件全都包含在內(nèi)，圖紙為CAD格式可編輯，有疑問咨詢QQ:414951605 或 1304139763p 存檔編碼：無無錫錫太太湖湖學學院院 2013 屆屆畢畢業(yè)業(yè)作作業(yè)業(yè)周周次次進進度度計計劃劃、檢檢查查落落實實表表 系別：信機系 班級：機械93 學生姓名：戴鵬飛 課題（設計）名稱：液壓機械手設計 開始日期：2012年11月12日周次起止日期工作計劃、進度每周主要完成內(nèi)容存在問題、改進方法指導教師意見并簽字備 注1-32012年11月12日-2012年12月2日查閱相關資料，完成開題報告按照任務書要求查閱論文相關參考資料，填寫畢業(yè)設計開題報告書參考資料不太詳細，論文中相關的設計結(jié)構比較分散，要將各論文中設計的部件整合起來一起研究分析4-102012年12月3日-2013年1月20日指導專業(yè)實訓，要求了解企業(yè)結(jié)構進入工廠實習，了解企業(yè)生產(chǎn)流程初次進入工廠實習，對生產(chǎn)結(jié)構不太熟悉，要多動手做，加強熟悉工作流程112013年2月11日-2月16日查找一篇與論文相關的學術性英文資料，要求是期刊論文查找一篇關于液壓機械手的學術性英文資料如何查找英文資料還不太清楚，要多加學習122013年2月18日-2月23日嚴格按照要求翻譯英文資料，其中公式必須用公式編輯器輸入翻譯英文資料翻譯中遇到很多專業(yè)術語，翻譯語句不通順，加強英語練習，通順語句132013年3月4日-3月8日指導完成液壓機械手的總體結(jié)構方案設計確定液壓機械手的總體設計方案，整理設計思路設計思路不夠明確，對于所要完成的任務還不是十分清楚，要明確目標，設定計劃142013年3月4日-3月9日指導繪制裝配圖開始繪制液壓機械手的裝配圖運動機理不清楚,部件圖紙不全,多查找相關資料152013年3月11號-3月16日完成裝配圖的繪制完成裝配圖的繪制，并檢查修改對于各部件的裝配關系不太清楚，要理清各部件間的裝配162013年3月18日-3月23日指導完成部件圖的繪制完成繪制機頭部件的圖紙部分圖紙不全，缺少零件,多查找相關資料172013年3月25日-3月30日指導完成部件圖的繪制對已畫出的部件圖紙進行完善修改缺少相應的尺寸,明細表不完善182013年4月1日-4月6日完成零件圖的繪制完成主軸運動的設計，并畫出零件圖紙不知道使用哪種結(jié)構最佳，要建立模型，受力分析，確定最佳結(jié)構192013年4月8日-4月13日完成零件圖的繪制完成底座升降臺設計，并畫出圖紙部分圖紙尺寸不全，多查找相關資料，估算尺寸202013年4月15日-4月20日完成零件圖的繪制完成手臂設計計算，畫出零件圖對絲杠的設計計算比較生疏，查閱書籍，按照書上步驟一步一步設計計算212013年4月22日-4月27日指導說明書初稿寫作根據(jù)所下載的資料，構思并開始寫說明書對各章應寫哪些內(nèi)容比較模糊，可根據(jù)設計的圖紙，著重介紹個部件222013年4月29日-5月3日指導說明書二稿寫作、修改將整篇文章的脈絡、細節(jié)加以完善，用詞準確，不出現(xiàn)病句等。有很多細節(jié)錯誤，將文章再復查一邊，避免出現(xiàn)語句上的和細節(jié)方面的錯誤232013年5月6日-5月10指導修改說明書并定稿說明書按照模板進行修改格式經(jīng)常出錯，耐心修改，要熟練使用word各項功能242013年5月13日-5月18日說明書及相關資料、圖紙打印裝訂打印說明書和圖紙及相關資料論文格式嚴格按照學校的規(guī)范進行修改,完善252013年5月20日-5月25日指導學生整理資料，準備答辯整理資料，并熟讀資料，做好充分準備存在很多細節(jié)方面的錯誤，應認真檢查并及時改正 說明：1、“工作計劃、進度”、“指導教師意見并簽字”由指導教師填寫，“每周主要完成內(nèi)容”，“存在問題、改進方法”由學生填寫。2、本表由各系妥善歸檔，保存?zhèn)洳椤Ｖ艽纹鹬谷掌诠ぷ饔媱?、進度每周主要完成內(nèi)容存在問題、改進方法指導教師意見并簽字備 注 COMBINATION OF ROBOT CONTROL AND ASSEMBLY PLANNING FOR A PRECISION MANIPULATOOR Abstract 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 resulte show the feasibility and efficiency of the proposed system. Keywords ：Manipulator Assembly planning Skill decomposition Automated assembly 1Introduction Owing 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 assembly have been focused on microassembly technologies. However, microassembly techniques of high flexibility, efficiency, and reliability skill open to further research. This paper researches to how to realize the automatic assembly operation on a two-finger micromanipulator. A muli-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 operation, 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 control to 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 complied 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 skill sequence. 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 graph among different components is first constructed by analyzing the assembly model, and then, possible sequences are searched, based on the 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.’work, the assembly task commands are expanded to more detailed commands, which can be 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 start atate and target 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. Te 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. Base on the CAD model, the assembly task squences 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 planning Skills are not used directly at the assembly-planning phase, 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) Based-part and Assembly-Part are two parts that are assembled together. Base-part is fixed on the worktable, while Assembly-Part is handled by robot’s end- effector and assembled onto the Base-Part. Operation describes how the Assembly-Part is assembled with the Base-Part; Operation={Intertion-T,serew-T,align-T,…}. The structure of microparts is usually uncomplicated, and they can be modeled by the constructive solid geometry (CAG) 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 operation-we 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 assembly’s 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 direction In 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 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): For a part in the relation graph, calculate its remaining degrees of freedom, also called degrees of separation, of each geometric constraint. For the conplanar constraint, the remaining degrees of freedom are R1= {x,y,Rotz }. For the collinear constraint, the remaining degrees of freedom are R2= {z,Rotz}. R1 and R2 can also be represented as R1= {1,1,0,0,0,1} and R2{0,0,1,0,0,1}. Here, 1 means that there is a degree of separation between the two parts. R1R2= {0,0,0,0,1},and so, the degree of freedom around the z axis will be ignored in the following steps. In the ease that there is loop in the relation graph, such as parts Part5,Part6, 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 is in Nin=Ri1Ri2...Rin; where R is the remaining degrees of freedom of constraint k by part i. For example, in Fig. 2, given that the number of 1s in U is larger than U, then it can be regarded that the position of part 7 is determined by constraints between part 5 and part 6,while Part5 and Part6 can be fully constrained by constraints between Part 5 and Part 6. we can unite Part 5 and Part 6 as one node will be regarded as a single, but it is obvious that the composite node implies an assembly sequence. 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 than 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 on interference. Then, select the +z direction as the mating direction, which is represented as a vector M measured in the coordinate system of the assembly. It should be noted that , in some case, there may be several possible mating directions for a part. The condition for assembly operation 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. 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 plans 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 {P1/P1’, P2/P2’, P3/P3’}/{P1/P1’, P3/P3’}/{P1/P1’,P2/P2’}={P1/P1’} 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 grsped. 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 Bae-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 part’s coordinate system. Separate gradually the two parts along the mating direction, which 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 sequence Some 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 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 pay part. Sequence a is obviously inefficient and may cause much uncertainty 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 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: 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. 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. 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 In the CAD model, move the assembly part to the base part in the possiblemting direction, which 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 collicion-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. Record the assembly sequence with Operation type, the mating direction, and the grasping position. 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 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 search assembly sequence s. Comparing the values of mount , the more efficient one can be selected. 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 M 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 sequences, the coordinates of the base part, and the coordinates, of the assembly. The relations among the coordinates are represented by a 4*4 transformation matrix , which is calculated based on the assembly CAD model when creating the relations graph. These matrixes are stored with all o the related parts in the database. They are also used in skill decomposition. 3 Skill decomposition and execution 3.1 Definition of skill primitive Skill 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 skill’s 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 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, sills 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 skill should have generality to express specific tasks. There should not be overlap among skill. In the paper, a skill primitive for robot control is defined as: Attribute -I, Action -i(Attribute -i), Si= Start -i(Attribute -i), End -i(Attribute -i) Condition -i(Attribute -i). Attribute –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’ action, 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, ect. For one skill, there is only one Action. Due to the limited space, the details of actions will not be discussed in the 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 bobot 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 pf thede actions. 3.2 Skill decomposition Some 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 it is fixed with Bese_Part as the assembly lifestyle of the Assembly_Part. In its assembly lifecycle, the Assembly_Part may be at different assembly states. Here shows a shaft’s sates show as blocks and associated workflows of an insertion task. A workflow consisting of 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 uded, 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, determined by the fixture, then the Free and In_WS states can be removed from the shaft’s assembly lifecycle. The system provides a tool for users to modify thede templates or generate their own templates. The tool’s user interface is displayed in. For a workflow, the start state is measured by sensory values, which the target state is calculated based on the CAD model and sensory attributes. According to the start state and target state, the generator generates a series of skills. Here, we use the Move workflow in as an example to show how skills are gener