**4.4 Experimental result**

Before each experiment, the paper with irregular contour pattern (2.5 mm width) printed on it was placed randomly on the working stage. Key points (shown in **Figure 8(b)**) for coarse tracing motion of the parallel-link robot were extracted. The parallel-link robot was set to be 100% smooth motion with 400 mm/s speed. Along with the coarse tracing by the parallel-link robot, the compensation module was realizing fine local compensation in two dimensions with the visual feedback information from the new high-speed vision system.

### **Figure 9.**

*Result of contour tracing [25]. (a) Image profile of contour tracing without external disturbance. (b) Image profile of contour tracing with random external disturbance.*

3.Point *pi* starting from *pc* along the extraction direction with a small step size *δ*

*Coarse motion of the main robot. (a) Method for extraction of key points. (b) Extracted key points.*

the compensation module in image space, we then continue to examine the next point by increasing one step further. Otherwise, *pi* was elected as the new extraction point *pn*. With insertion of the new point *pn*, points between *pc* and *pn* should be re-examined to see whether distance from *pj* to chord *pcpn*

bigger than *Smax* or not. If it was true, another new extraction point at *pj* should be inserted, and a recursive check for points between *pc* and *pj* should be conducted. Until all points between *pc* and *pn* were secured (the distance from each point to the corresponding chord was smaller than *Smax*), move the probing circle by updating *pc* with *pn*. And then the algorithm will return back to the last step until all the discretization points of the target contour were

> ! � *pcpd* !∣

> > ∣*pcpd*

Usually, a commercial robot controller enables different methods of on-line path

generation with selected key points. As an example, as shown in **Figure 8(a)**, a point-to-point (P2P) method that generated a path strictly passing through all key points with nonconstant velocity was included. On the other hand, a smooth path (100% smoothing factor) method would achieve a constant velocity profile while at the same time the exact generated trajectory would be not known to the user in advance. In many industrial applications, contour tracing with constant speed has significant advantages. It not only achieves good energy efficiency by reducing unnecessary acceleration and deceleration but also obtains better working performance in cases where work timing is critical, such as in welding. In this task, we

were the key points extracted from the target contour.

*<sup>D</sup>* <sup>¼</sup> <sup>∣</sup>*pcpi*

! was within the work range (*Smax*) of

! was represented as *D* and was

!<sup>∣</sup> (12)

! was

was examined. If its distance to chord *pcpd*

visited. The distance from *pi* to chord *pcpd*

calculated by

**Figure 8.**

*Industrial Robotics - New Paradigms*

4.Points *p*0, *p*1, … , *pn*

**74**

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 experimental setup) was realized [25].

**5.2 Extendibility of the add-on module**

*DOI: http://dx.doi.org/10.5772/intechopen.90169*

a specific task.

**6. Conclusion**

for coarse motion

separated

**Author details**

Shouren Huang<sup>1</sup>

Tokyo, Japan

**77**

Under the condition that the add-on module should have a much higher bandwidth than that of a companion main robot, the add-on module can be designed to be more than two DOFs. Specifically, it is suitable to adopt the parallel mechanism for an add-on module with more than three DOFs to assure motion accuracy. The high-speed sensory feedback for detection of on-line accumulated error can be high-speed vision with 2D/3D configuration or other forms of sensors depending on

*Dynamic Compensation Framework to Improve the Autonomy of Industrial Robots*

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:

1.Easy calibration for sensor-motor mapping as the industrial robot is designated

2.Good compatibility for industrial robots due to the fact that the control scheme for an arbitrary industrial robot and the add-on compensation module is

3.Good compatibility for artificial intelligence algorithms as the planning-level

\*, Yuji Yamakawa<sup>2</sup> and Masatoshi Ishikawa<sup>1</sup>

1 Graduate School of Information Science and Technology, University of Tokyo,

2 Interfaculty Initiative in Information Studies, University of Tokyo, Tokyo, Japan

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: huang\_shouren@ipc.i.u-tokyo.ac.jp

provided the original work is properly cited.

intelligence and action-level intelligence are decoupled.

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 be found on the website [27].

### **5. Discussion**

### **5.1 For wider applications**

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 teaching procedure may implement as follows:


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 realize dynamic compensation.

*Dynamic Compensation Framework to Improve the Autonomy of Industrial Robots DOI: http://dx.doi.org/10.5772/intechopen.90169*
