機(jī)器人控制和裝配結(jié)合的機(jī)密機(jī)械手外文文獻(xiàn)翻譯、中英文翻譯

1、COMBINATION OF ROBOT CONTROL AND ASSEMBLY PLANNING FOR A PRECISION MANIPULATOORAbstractThis 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-aid

2、ed 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 l

3、ayer. Experimental resulte show the feasibility and efficiency of the proposed system.Keywords ：Manipulator Assembly planning Skill decomposition Automated assembly1IntroductionOwing to the micro-electro-mechanical systems (MEMS) techniques, many products are becoming very small and complex, such as

4、 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 skil

5、l 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 seq

6、uences 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

7、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 sy

8、stems 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,

9、 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-frien

10、dly, 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

11、 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

12、 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,

13、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

14、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 r

15、obot 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

16、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, the concept of a task is used. A task can fulfill a series of assembly operations, for example, from

17、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 to

18、gether. 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=Intertion-T,serew-T,align-T,.The structure of microparts is usually uncomplicated

19、, 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 assem

20、bled. 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 relat

21、ions (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 re

22、lations 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 co

23、llinear 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 g

24、eometric 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

25、,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 R20,0,1,0,0,1. 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 wil

26、l 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-const

27、rained, 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 t

28、he 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

29、 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. Separati

30、on 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

31、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 posi

32、tion 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 g

33、rasping plans are selected as G1G2Gi, 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

34、/P3/P1/P1, P3/P3/P1/P1,P2/P2=P1/P1The 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 mode

35、led 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

36、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.Separate gradually the two parts along the mating direction, which checking interference in

37、 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 collisi

38、on-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 sh

39、ould 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

40、 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

41、 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 as

42、sembly 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

43、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

44、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 th

45、e 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

46、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 (c

47、ollision) 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

48、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 pos

49、ition.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

50、 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 in

51、active 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 computat

52、ion, 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

53、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 execution3.1 Definition of skill primitiveSkill primitives are the interface between the assembly planning and robot co

54、ntrol. 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 con

55、trol 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 def

56、ined 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 d

57、efined 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 attri

58、butes. 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 skil

59、l, 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

60、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 t