Master Thesis - Hochschule Bonn-Rhein-Sieg

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4. Overall evading concept Master Thesis Björn Ostermann page 40 of 126 data remains consistent between two program calls, which is necessary during the testing and evaluating process of the program. The loading of the background data from a file, which is stored on the computer alongside the program, is realized in the second subpart (button 5). This process is the standard procedure of loading the background data into the program. Thus this subpart is automatically activated once, alongside the thread’s activation. The user only needs to take action in acquiring new background data, if the workplace was changed. If a change in the workplace occurred, new background data has to be acquired to guaranty a safe operation of the robot. The newly acquired background data has to be stored to the respective file by use of the third subpart (button 6). In this case, the currently stored background data in the file is overwritten with the background data in the program’s variable. Since other threads may work on the background data simultaneously, which can cause problems, all file operations of the program are handled by the data exchange object that takes special measures to avoid those problems (see chapter 4.6). 4.3.3 Adjusting offset and intrusion filter from part 2 Set Offset Store offset value 8 7 Show intrusion Acquire distance data Apply offset value Compare distance data with known background Filter spikes from comparison Depict intrusion and comparison image Figure 23: Camera control thread – part 3 Setting the offset value and showing the detected intrusions part 4
4. Overall evading concept Master Thesis Björn Ostermann page 41 of 126 This part depicts an intrusion image, shown in Figure 24, which is to be used, to adjust filter parameters to the workplace and its environmental conditions. As described in chapter 4.3.2, the distance measurement of the camera contains fluctuation noise that has to be compensated. Since the algorithms working on life data can not acquire a large amount of images in order to calculate a reliable median like with the background data, this inaccuracy has to be dealt with by other algorithms. Most of those algorithms that work on single images take a lot of computational effort. A simple, and computationally cheap, way of noise compensation is to add an offset to the background, causing intrusions only to be recognized, if a certain threshold is exceeded. Since a percental offset can not be used, due to the necessary safety analysis (see chapter 6.3) the offset distance has to have a specific distance value. Using this offset value allows the following compensatory algorithms (see Figure 24) to work with data that is much less noisy and thus easier and faster to filter further. With the use of button 8 of the GUI (Figure 19), contained in the third part of the flowchart in Figure 23, the user transmits this offset value to the camera control thread. The offset then is continuously added to the background, whenever the background is accessed for comparison. This offset-filter (see chapter 5.2.2.1) needs to be adjustable in the demonstrator, since it influences the false positive and false negative detections. - If the chosen value is too low, static noise is seen as intrusions. (false positive) - If the chosen value is too high intrusions close to the background are lost. (false negative) In industrial applications a value of more than 120 mm can not be allowed (see chapter 6.2.1). The second subpart in Figure 23 (button 7) displays the intrusions, detected in the workplace. This subpart uses the algorithms described in chapters 5.2.2.1, 5.2.2.2 and 5.2.2.3. It starts by comparing the currently measured distance values of all pixels, acquired from the 3D camera, with those stored in the background values (see Figure 22). As described above, an offset is added to all currently measured distance values during this comparison. The next step of this subpart is a filtering of the remaining noise. This can be done either by the spike filter (see chapter 5.2.2.2) or the plausibility filter (see chapter 5.2.2.3). Both filters are contained in the source code of the program. As explained in the algorithms’ chapters, the plausibility filter yields better results and is thus activated in the program. The spike filter was kept for evaluation and comparison. In the end of the subpart, the results of the algorithms are displayed. Sample images of the remaining possible intrusions, using different settings are depicted in Figure 24.
Page 1 and 2: Master Thesis ”Industrial jointed
Page 3 and 4: Statutory Declaration Master Thesis
Page 5 and 6: Contents Master Thesis Björn Oster
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Page 9 and 10: 1. Introduction Master Thesis Björ
Page 11 and 12: 2. Overview on human-robot Master T
Page 19 and 20: 3. Hard- and software Master Thesis
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7. Conclusion and Outlook Master Th
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8. Bibliography Master Thesis Björ
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9. List of Figures Master Thesis Bj
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9. List of Figures Master Thesis Bj
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