Submitted version of the thesis - Airlab, the Artificial Intelligence ...

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58 Chapter 5. Vision The whole algorithm and script is found in Object’s Position section in Appendix B.2 5.2 Color Definition The camera we are currently using has a UXGA resolution CMOS and it is set to 160x120 resolution for the blob search algorithm. One major problem is the blob search algorithm. The algorithm used inside is unknown and this forced us to define color information very precisely, since we cannot eliminate the non-object blobs easily. We can eliminate the problem only by using the area, perimeter and the circularity information which, in case the target is a ball, is not enough for most cases to track the blob. So this makes the color definition important. The color is defined in a RGB Cube, using RGB444 format, and it is not very clear to understand the correctness of the defined color from the color codes. In order to ease this process, the color selection is made by theST software by selecting the desired color from the image, or directly programming the rules if the color values are known. Since the resolution is very low, the color consists of shadows, or existence of blob not belonging to the searched object made us to improve the color selection. The current ST software is sufficient to visualize the selected color and test the blob search, but color definition is difficult since, in order to define the color, the pixels should be selected one by one, is not possible to select an area. Moreover, the rules (which is the additions and subtractions of the color codes to RGB Cube) found by the ST software are not in the compact form (the same rule for a color can be added or subtracted more than one). To improve the color selection, we decided to use an offlinecalculator, that calculates thehistogram for the selected object’s colors. As a first step, we started by disabling automatic white balance and automatic exposurecontrol. Before getting into the white balance, the concept of color temperature needs to be introduced. Color temperature is just a way of quantifying the color of light. It is measured in degrees Kelvin (K). Normal daylight has a color temperature of around 6,500K. Warmer light has a lower color temperature. The warm light that occurs late in the afternoon might have a color temperature of around 4,000K. Cooler light has a higher color temperature. Thebluish light that sometimes occurs in twilight periods of the day might have a color temperature of about 7,500K. So, our concept of warm and cool light is tied directly to the color temperature. The
5.2. Color Definition 59 warmer (yellow) the light, the lower the color temperature; the cooler the light (blue), the higher the color temperature. Color Temperature Light Source 1000-2000 K Candle light 2500-3500 K Tungsten Bulb (household variety) 3000-4000 K Sunrise/Sunset (clear sky) 4000-5000 K Fluorescent Lamps 5000-5500 K Electronic Flash 5000-6500 K Daylight with Clear Sky (sun overhead) 6500-8000 K Moderately Overcast Sky 9000-10000 K Shade or Heavily Overcast Sky Color temperature is also important for cameras. The camera manufacturers knew that the color of the light would affect the colors delivered by the camera. Therefore, they decided to deal with the problem by designing the cameras to automatically measure the light temperature and to make adjustments as the light changes color. That is why we can shoot in the relatively neutral light with camera in the afternoon and then shoot the next day in the cool light of early morning and still, probably, get reasonable colors in both situations even though the color of the light was different. Cameras correct for the change in light temperature, using white balance. With auto white balance, the camera attempts to determine the color temperature of the light, and automatically adjusts for that color temperature. Auto white balance works reasonably well under the following conditions: • The application does not require absolute maximum color accuracy • There is not a preponderance of one color in the scene being photographed • Adjustments for the color temperature of the light Asmentionedinthepreviousparagraph, inautowhitebalancemode,the cameradoesits besttodeterminethecolor ofthelight andmakeappropriate adjustments. However, the methodology that is used to do this requires that certain assumptions be made. These assumptions do not always match perfectly the scene being photographed. As a consequence, the auto white balance option does not always yield perfect results. Accordingly, problems
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POLITECNICO DI MILANO Corso di Laur
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To my family...
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Summary Aim of this project is the
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Contents Sommario I Summary III Tha
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List of Figures 2.1 The standard wh
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Chapter 1 Introduction 1.1 Goals Th
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1.4. Thesis Structure 3 tailed desc
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Chapter 2 State of the Art Advances
Page 23 and 24: 2.1. Locomotion 7 provides low resi
Page 25 and 26: 2.1. Locomotion 9 Figure 2.4: Diffe
Page 27 and 28: 2.1. Locomotion 11 Omnidirectional
Page 29 and 30: 2.2. Motion Models 13 2.2 Motion Mo
Page 31 and 32: 2.4. Games and Interaction 15 a 2.5
Page 33 and 34: 2.4. Games and Interaction 17 We in
Page 35 and 36: 2.4. Games and Interaction 19 lot o
Page 37 and 38: Chapter 3 Mechanical Construction W
Page 39 and 40: 3.2. Motors 23 The initial design o
Page 41 and 42: 3.2. Motors 25 Figure 3.3: The calc
Page 43 and 44: 3.3. Wheels 27 target surface, maxi
Page 45 and 46: 3.5. Bumpers 29 Figure 3.8: Three w
Page 47 and 48: 3.6. Batteries 31 itself also works
Page 49 and 50: 3.7. Hardware Architecture 33 In sh
Page 51 and 52: 3.7. Hardware Architecture 35 The s
Page 53 and 54: 3.7. Hardware Architecture 37 With
Page 55 and 56: Chapter 4 Control The motion mechan
Page 57 and 58: 4.1. Wheel configuration 41 Figure
Page 59 and 60: 4.2. Matlab Script 43 This is case
Page 61 and 62: 4.2. Matlab Script 45 Mixed Angular
Page 63 and 64: 4.3. PWM Control 47 acollision, suc
Page 65 and 66: 4.3. PWM Control 49 mentioned previ
Page 67 and 68: Chapter 5 Vision The vision system
Page 69 and 70: 5.1. Camera Calibration 53 the prin
Page 71 and 72: 5.1. Camera Calibration 55 Figure 5
Page 73: 5.1. Camera Calibration 57 Later, i
Page 77 and 78: 5.2. Color Definition 61 Anotheraut
Page 79 and 80: 5.2. Color Definition 63 9000 8000
Page 81 and 82: 5.3. Tracking 65 any color values.
Page 83 and 84: Chapter 6 Game The robot will be us
Page 85 and 86: • Initialize sensors • Initiali
Page 87 and 88: If the camera is set to SERVO MID,
Page 89 and 90: Chapter 7 Conclusions and Future Wo
Page 91 and 92: ior, which will be useful in the re
Page 93 and 94: Bibliography [1] Battle bots-http:/
Page 95 and 96: BIBLIOGRAPHY 79 [25] Han et al. A n
Page 97 and 98: Appendix A Documentation of the pro
Page 99 and 100: Figure A.2: The flow diagram of the
Page 101 and 102: Appendix B Documentation of the pro
Page 103 and 104: B.1. Microprocessor Code 87 /* Conf
Page 105 and 106: B.1. Microprocessor Code 89 #ifdef
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Page 113 and 114: B.1. Microprocessor Code 97 RGBCube
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Page 117 and 118: B.1. Microprocessor Code 101 #endif
Page 119 and 120: B.1. Microprocessor Code 103 } void
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B.1. Microprocessor Code 109 } Inve
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B.1. Microprocessor Code 111 #inclu
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B.1. Microprocessor Code 113 /* * M
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B.1. Microprocessor Code 115 b[1] =
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B.1. Microprocessor Code 117 } * (B
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B.1. Microprocessor Code 119 #defin
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B.2. Color Histogram Calculator 121
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B.3. Object’s Position Calculator
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B.3. Object’s Position Calculator
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B.4. Motion Simulator 127 0 0 0 0 0
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B.4. Motion Simulator 129 if (plot
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B.4. Motion Simulator 131 fill (m a
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B.4. Motion Simulator 133 end text(
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Appendix C User Manual This documen
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C.1. Tool-chain Software 137 Figure
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C.2. Setting up the environment (Qt
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C.3. Main Software - Game software
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Appendix D Datasheet The pin mappin
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Figure D.2: The schematics for the
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