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1、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-ai
2、ded 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
3、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
4、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 technolog
5、ies. 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 co
6、mplex 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 oper
7、ations, 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
8、 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 assemb
9、ly 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,
10、 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
11、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 m
12、any 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 obt
13、ained. 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 a
14、nd 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 workfl
15、ows 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 syste
16、m 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 m
