【正文】 徐州工程學(xué)院畢業(yè)設(shè)計(jì) 外文翻譯 學(xué)生姓名 學(xué)院名稱 專業(yè)名稱 指導(dǎo)教師 20**年 5 月 27 日 COMBINATION OF ROBOT CONTROL AND ASSEMBLY PLANNING FOR A PRECISION MANIPULATOOR Abstract This paper researches how to realize the automatic assembly operation on a twofinger precision manipulator. A multilayer assembly support system is proposed. At the taskplanning layer, based on the puteraided design (CAD) model, the assembly sequence is first generated, and the information necessary for skill deposition is also derived. Then, the assembly sequence is deposed into robot skills at the skilldeposition 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 deposition Automated assembly 1 Introduction Owing to the microelectromechanical systems (MEMS) techniques, many products are being very small and plex, such as microphones, microoptical ponents, and microfluidic biomedical devices, which creates increasing needs for technologies and systems for the automated precision assembly of miniature parts. Many efforts aiming at semiautomated 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 twofinger micromanipulator. A multilayer assembly support system is proposed. Automatic assembly is a plex 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 assemblyplanning 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 assemblyplanning 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 skillbased micromanipulation system has been developed in the authors’ lab, and it can realize many micromanipulation operations. In the system, the assembly task is manually disposed into skill sequences and piled into a file. After importing the file into the system, the system can automatically execute the assembly task. This paper attempts to explore a userfriendly, and at the same time easy, sequencegeneration method, to relieve the burden of manually programming the skill sequence. It is an effective method to determine the assembly sequence from geometric puteraided 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 lowlevel data structure of the CAD model, and can be realized with the application programming interface (API) functions that many mercial CAD software packages provide. In the proposed approach, a relations graph among different ponents 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 depose the assembly sequence into robot skill sequences, some works have been reported. In Nnaji et al.’s work, the assembly task mands are expanded to more detailed mands, which can be seen as robot skills, according to a predefined format. The deposition 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 deposition. 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 assemblytask 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 skilldeposition layer, tasks are deposed 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 assemblyplanning 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 robot’s endeffector 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,..