**6. Conclusion**

Tracing result of one trial without external disturbance is shown in **Figure 9(a)**. It was obvious that with motion planning by the global visual information, only coarse tracing by the parallel link was realized. With the cooperation between the parallel-link robot's global motion and the add-on module's local compensation, fine tracing with maximum error around 6 pixels (corresponding to 0.288 mm in the

In order to simulate external disturbance to the working target, the stage was manually vibrated in a random manner during the tracing process. The image profile of one trial is shown in **Figure 9(b)**. It demonstrated that the proposed dynamic compensation robot was capable of realizing accurate tracing even under disturbance from external environment, with the maximum tracing error around 9 pixels (corresponding to 0.432 mm) [25]. A video for the contour tracing task can

The two application scenarios addressed above held the same fact that the addon modules were directly utilized in task implementation and the payloads for the add-on module were small. As addressed in the analysis of the proposed framework, an add-on compensation module was assumed to have high-speed motion capability and ideally have a much larger bandwidth than that of the main robot. Therefore, payload for the add-on module should be very limited if the add-on module is directly utilized in task implementation. Then the readers may wonder whether the proposed framework is adoptable for wider applications where payloads for robots could be large. In these cases, we can actually utilize the dynamic compensation framework as a method of realizing autonomous teaching (programming) for an industrial robot. Let us take an example of a certain task with trajectory motion control in two dimensions where a manipulation tool (e.g., a torch for arc welding) would be too heavy for the add-on compensation module. The autonomous teach-

1.**Learning step**. The industrial robot with an add-on compensation module configured at its end effector realizes autonomous contour tracing (as addressed in Section 4) for a specific contour target (e.g., a seam for welding task). Configurations (in configuration space or joint space) of the industrial robot and the add-on module are memorized during the tracing process.

2.**Programming step**. Integrating the data of the add-on module into the

It should be noted that the repeatability accuracy of an industrial robot is assumed to be good in these autonomous teaching applications. However, the proposed framework does have its limitations due to the fact that the on-line accumulated error should be observable by the high-speed sensory feedback in order to

configuration of industrial robot at each time point to generate the synthesized

3.**Manipulation step**. The industrial robot replaces the add-on module with the manipulation tool to its end effector and implements manipulation following

experimental setup) was realized [25].

*Industrial Robotics - New Paradigms*

be found on the website [27].

**5.1 For wider applications**

ing procedure may implement as follows:

trajectory for the industrial robot.

realize dynamic compensation.

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the trajectory programmed in the last step.

**5. Discussion**

In order to improve the autonomy of industrial robots while at the same time keeping good accuracy under many uncertainties, we present a dynamic compensation framework under a hierarchical intelligent architecture based on a coarse-tofine strategy. Traditional industrial robots are designated to conduct coarse global motion by focusing on planning-level intelligence. Concurrently, an add-on robotic module with high-speed actuators and high-speed sensory feedback is controlled to realize fine local motion with the role of implementing action-level intelligence. Main advantages of the proposed framework are summarized as follows:

