【正文】 mbly_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 deposition phase—the generation of a collisionfree path. Here, we use a straightline 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. Execution of skills After 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 pleted 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 pleted, 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 part39。/step. The two motors are then fixed onto the parallel motion mechanism respectively. It is a serial connection of a parallelhexahedron link and a parallelogram link. When the 1? , 2? , and 3? are small enough, the motion of the endeffector can be considered as linear motion. The magic actuator to drive the parallel mechanism consists of an aircore coil and a permanent mag. The permanent mag is attached to the parallel link, while the coil is fixed onto the base frame. The magic levitation is inherently unstable, because it is weak to external disturbances due to its noncontact operation in nature. To minimize the effect of external disturbances, a disturbanceobserverbased method is used to control our micromanipulator. Laser displacement sensors are used to directly measure the probe’s position. The reflector is attached to the endeffector. Nanoforce sensors produced by the BL AUTOTEC pany are used to measure the forces. The position resolution of the micromanipulator is 1 um. The maximal resolution of the force is gf, and the maximal resolution of the torque is 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 GPB–K PCI frame grabber, which works at 25 MHz. Experiment An assembly with three ponents is assembled with the proposed manipulator. It is a wheel of a micromobile robot developed in the authors39。 in f, the cup is fixed in the workspace。 in b, the gear is fixed in the workspace。 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 largerange coarse motion is provided by a planar motion unit, with a repeatability of 2 μm 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 176。x axis and 177。 Assembly Part。 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 sequenc