planning in the paper is to generate a sequence of robot actions and to assign values to attributes of these 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 that it is fixed with the Base_Part as the assembly lifecycle of the Assembly_Part. In its assembly lifecycle, the Assembly_Part may be at different assembly states.Here shows a shaft’s states shown as blocks and associated workflows of an insertion task. A workflow consisting of a 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 used, 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, if the shaft has been placed into the workspace with accurate position, 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 these 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, while the target state is calculated based on the CAD model and sensory attributes. According to the start state and the target state, the generator generates a series of skills. Here, we use the Move workflow in as an example to show how skills are generated.
After the assembly task (assembly lifecycle) is initiated, the template is read into the Coordinator. For the workflow Move, its start state is Grasped, which implies that the Assembly_Part is grasped by the robot’s end-effector and, obviously, the position of the Assembly_Part is also obtained. Its target state is Adjusted, which is the state immediately before it is to be fixed with the Base_Part. At the Adjusted state, the orientation of the Assembly_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 decomposition
phase—the generation of a collision-free path. Here, we use a straight-line 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. 3.3 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 completed 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 completed, 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 part's position information is obtained, skills for the move workflow can be generated parallel with the execution of skills generated for the Grasp workflow.
The assembly process is not closed to users. With the proposed skills management list structure, users can monitor and control the assembly process easily. For example, for the adjustment or the error recovery, users can suspend the ongoing skill to input commands directly or move the robot in a manual mode.
4 Experiment
4.1 Experimental platform
The experimental platform used in the paper. For microassembly operations, the precision and workspace are tradeoffs. In order to acquire both a large workspace and high precision, the two-stage control approach is usually used. These systems usually consist of two different sets of actuators; 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 large-range 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 0.1°/step.
In the manipulator, two separate probes, rather than a single probe or parallel jaw grippers, are used to manipulate the miniature parts. The two probes are fixed onto two stepper motors with a maximum resolution of 0.05°/step. The two motors are then fixed onto the parallel motion mechanism respectively. It is a serial connection of a parallel-hexahedron link and a parallelogram link. When the ?1,?2, and ?3 are small enough, the motion of the end-effector can be considered as linear motion.
The magnetic actuator to drive the parallel mechanism consists of an air-core coil and a permanent magnet. The permanent magnet is attached to the parallel link, while the coil is fixed onto the base frame. The magnetic levitation is inherently unstable, because it is weak to external disturbances due to its non-contact operation in nature. To minimize the effect of external disturbances, a disturbance-observer-based 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. Nano-force sensors produced by the BL AUTOTEC company are used to measure the forces. The position resolution of the micromanipulator is 1 um. The maximal resolution of the force is 0.8 gf, and the maximal resolution of the torque is 0.5 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.
4.2 Experiment
An assembly with three components is assembled with the proposed manipulator. It is a wheel of a micromobile robot developed in the authors'lab. The following geometric constraints are defined in the CAD model: collinear between CL_cup and CL_axis, collinear between CL_gear and CL_axis, coplanar between Plane_cup and Plane_gear_1, coplanar between Plane_gear_1 and Plane_axis. According to the above geometric constraints, the three parts construct a loop in the relation graph.
The CAD model is created with the commercial software Solidworks 2005, and its API functions are used to develop the assembly planning model. The assembly Information database is developed with Oracle 9.2. Models involved with skill generation are developed with Visual Basic 6.0. The skill-generation models are run withWindows 2000 on an HP workstation with a
CPU of 2.0 G Hz and memory of 1.0 GB. Assuming that the positions of parts are available beforehand, it took about 7 min to generate the skill sequence. The generated assembly sequence is to assemble the gear onto the axis, and then assemble the cup onto the axis and the gear.
In the assembly operation, the parts are placed on the worktable with special fixtures and then transported into the workspace, so that their initial position and orientation can be assured. Therefore, in the experiment, all of the skill sequences for the different parts can be generated and then transferred to the Skill Management unit. The skill istransmitted to the micromanipulator through TCP/IP communication. Because the controller of the micromanipulator is run on DOS, the WTTCP tools kit are adopted to develop the TCP/IP communication protocol.
Because, currently, the automated control of the fixtures is not realized yet, the parts have to be fixed manually onto the worktable. The promotion between different tasks(assembly lifecycle of different parts) is conducted manually. Here shows some screenshots of the assembly process. In a, the axis is fixed in the workspace; in b, the gear is fixed in the workspace; from c to e, the gear is grasped, moved, and fixed onto the axis by the probes; in f, the cup is fixed in the workspace; from g to i, the cup is fixed with the gear and the axis. It can be found that the proposed system can perform the assembly successfully.
5 Conclusion
This paper has introduced a skill-based manipulation system. The skill sequences are generated based on a computer-aided design (CAD) model. By searching the assembly tree and mate trees, an assembly graph is constructed. The paper proposes the approach to calculate the mating directions and grasping position based on the geometric constraints that define relations between different parts. Because the workspace of the micromanipulator is very small, the assembly sequence brings much influence on the assembly sequence. In the present paper, the number of required times of mounting parts in the workspace is selected as the criterion to select the optimal skill sequence.
This paper presents a method to guide the skill decomposition. The assembly process is divided into different phases. In one phase, the part is at an assembly state. A specific workflow pushes the part forwards to its target state, which is the next desired state of the part in the assembly lifecycle and is calculated based on CAD model information and sensory information. A special skill generator is associated with the workflow to generate skills that promote the part to the target state. After the skill sequence is generated, the system dispatches them to the controller of the manipulator to drive the manipulator.
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