Planning for Optimal Robot Trajectory for Visual Inspection

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2.3. Experiment Setup

Figure 1. Experimental setup showing the work piece (car door) and the end effector where a camera were installed marked in the small box in the figure

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The robot manipulator was used to realize the automatic visual inspection where the manipulator’s function was to bring the camera to the pre-defined locations within the configuration space. The camera shootings were realized by another separate system. Figure 1 exhibits the experiment setup that consists of the robot itself, a work piece that is a car door, and a camera that is attached onto the end effector. The white markings were plastic straws that functioned as indicators that limit the configuration space. They provided the limit within which the camera should reach but to avoid any obstacles. Prior to the realization of the inspection process, the setup was simulated in Workspace. Figure 2 shows the simulated environment of the setup. The results obtained from the simulation would explain the behavior of the individual actuator and this would reflect to equations (1), (2), and (3).

Figure 2. Simulation for planning of manipulator’s path and trajectories using Workspace

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Figure 3. The examples of inspection points, figures (d) and (e), where the robot is programmed to stop at proximity to the point within the prescribed delay time. Figures (a) and (c) are the front and the rear side views of the car door, respectively. Figure (b) is the side view

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While maneuvering through the programmed path, the robot will stop at the predefined point so that an image could be captured. Figure 3 shows example of the points where image areas are selected such as shown in Figure 3d and Figure 3e. This sub process is conducted to find the optimum parameter for the coefficient correlation (CC) template matching. The template distance selection, the threshold adjustment and the average image processing speed for CC are determined. The sample images are tested while manipulating threshold value and the distance between camera and object . The sample of images captured within the range .

3. Results and Discussion

The realization of the experimental setup that was based on the simulation environment is shown in Figure 2. The image frames are the four of the selected robot motion images during the inspection process. The frames show the transit points upon which the camera captured images of areas that were likely to have defects. There were five points where the tool needed to stop for a moment during which the camera would capture images of the defect areas. The images were then uploaded for image processing to determine the degrees of defects.

Figure 4. Realization of manipulator planned path and trajectory

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Prior to robot initiation, the results obtained from Workspace (see Figure 4) explain the joints' behaviors. The behaviors dictated that in order to complete a cycle of visual inspection; the joints would need to displace on some degrees of angular position. It is observed, in Figure 4, that all joints were active during the inspection process from start to end. If this were to be implemented onto the actual robot program, there would have been unnecessary energy usage. From Figure 4, joint–4 is seen to consume the most energy, whereas joint–6 was the least. The simulated cycle time was less than 80 seconds.

Figure 5. Joints’ angular position behavior on simulation

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Based on the simulation results, it was observed that not all joints needed to be controlled in order to achieve the optimum configuration space following the specifications listed in Table 1. Figure 5 shows the actual joints’ behavior for a cycle time of 25.02 seconds. It is less than that the simulation cycle time because of the improved configuration space. In Figure 5, it is observed that all joints displacement was moderated where each displaced accordingly. All joints were functioning while the tool in motion.

There were rapid speed changes where all joints were functioning throughout the cycle (see Figure 6), whereas in the actual setup, most of the joints are somewhat steady during the mid-range of the cycle (see Figure 7). The mid-range of the cycle was where the inspection was carried out. During simulation, the robot had a rapid start and a fast end. Similarly, in the actual implementation, the trajectories were fast start and rapid end. This is further explained in Figure 8 and Figure 9 where the robot had slight decelerations in simulation environment, but with considerable rapid decelerations in the actual setup.

Figure 6. Joints’ angular position behavior on realization of robot program

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Figure 7. Joints’ angular velocity behavior on simulation

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Figure 8. Joints’ angular velocity behavior on realization of robot program

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Figure 9. Joints’ angular acceleration behavior on simulation

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Figure 10. Joints’ angular acceleration on realization of robot program

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In general, a template of an image at distance L = X is suitable to be match with image taken through a template at the same distance. A template of image at = Y, however, could match a template of image at L = X ^[8]. Therefore, this work implemented the template of image as such where the first parameter of the algorithm that was the template distance was found. Template size of for images at distance range from was tested. Template of L = 35, however, was not taken as a sample template because the distance was too far. In fact, an image captured through the template could result in loss of image features, hence poor accuracy.

The results are listed in Table 2. For L = 15 cm, the image retains most of its features. The value 3/3 dictates that 3 out of 3 points were detected. The template cropped from L = 15 cm was found to be acceptable for short ranges: of 15cm and of 20 cm, respectively. The degree of detection, however, was the lowest—26.67%. Conversely, there were two cases where the degree of detection was the highest: at L = 20 cm and at L = 30 cm, respectively. These templates could detect more points at different distances. Nevertheless, the template with L = 20 cm was selected to reduce the risk of losing image features. Figure 11 depicts the templates that match the points based on the CC method. The points are shown within the boxes in Figure 11b. In addition, Figure 12 explains, by the correlation measurement space, how the points were detected by chromatic scale such that the equivalent value of 0.8 represented the detected region.

Table 2. Range of template matching when template from input at different L

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Figure 11. The template matching based on the correlation coefficient approach

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Figure 12. The correlation measurement space exhibits chromatic degree of detection where the equivalent value of 0.8 represents the detected region

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