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Lens
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Ruler
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Focusing Stage (Nonflammable!)
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FIGURE 32-9 Use a bright light source, such as an incandescent lamp, and a tape to measure the focal point of a lens.
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ROBOT VISION
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which are secured to a circuit board in two groups of four. The smaller the photocell is, the easier it will be to mount with the lens (although soldering them may be more difficult). Each of the eight cells is connected to a separate input of an eight-input analog-to-digital converter (ADC) chip. By using an eight-input ADC, the values of all eight cells can be readily sensed without the need for separate ADC chips and extra wiring.
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32.4.2 USING FILTERS
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Filters accept light at certain wavelengths and block all others. A common filter used in robot design is intended to pass infrared radiation and block visible light. Such filters are commonly used in front of phototransistors and photodiodes to block out unwanted ambient (room) light. Only infrared light from a laser diode, for instance is allowed to pass through and strike the sensor. Optical filters come in three general forms: colored gel, interference, and dichroic.
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Colored gel filters are made by mixing dyes into a Mylar or plastic base. Good gel filters use dyes that are precisely controlled during manufacture to make filters that pass only certain colors. Depending on the dye used, the filter is capable of passing only a certain band of wavelengths. A good gel filter may have a bandpass region (the spectrum of light passed) of 40 to 60 nanometers (nm), which equates to nearly one full color of the basic six-color rainbow. Interference filters consist of several dielectric and sometimes metallic layers that each block a certain range of wavelengths. One layer may block light under 500 nm, and another layer may block light above 550 nm. The band between 500 and 550 nm is passed by the filter. Interference filters can be made to pass only a very small range of wavelengths. Dichroic filters use organic dyes or chemicals to absorb light at certain wavelengths. Some filters are made from crystals that exhibit two or more different colors when viewed at different axes. Color control is maintained by cutting the crystal at a specific axis.
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32.5 Introduction to Video Vision Systems
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Single- and multicell-vision systems are useful for detecting the absence or presence of light, but they cannot make out the shapes of objects. This greatly limits the environment into which such a robot can be placed. By detecting the shape of an object, a robot might be able to make intelligent assumptions about its surroundings and perhaps be able to navigate those surroundings, recognize its master, and more. A few years ago, video vision was an expensive proposition for any robot experimenter. But the advent of inexpensive and small pinhole and lipstick video cameras that can output video data in a variety of different formats makes the hardware for machine vision affordable (while still not a trivial exercise). A video system for robot vision need not be overly sophisticated. The resolution of the image can be as low as about 100 by 100 pixels (10,000 pixels total), though a resolution
32.5 INTRODUCTION TO VIDEO VISION SYSTEMS
of no less than 300 by 200 pixels (60,000 pixels total) is preferred. The higher the resolution is, the better the image and therefore the greater the robot s ability to discern shapes. A color camera is not mandatory and, in some cases, makes it harder to write suitable video interpolating software. Video systems that provide a digital output are generally easier to work with than those that provide only an analog video output. You can connect digital video systems directly to a PC, such as through a serial, parallel, or USB port. Analog video systems require that a video capture card, a fast analog-to-digital converter, or some other similar device be attached to the robot s computer. While the hardware for video vision is now affordable to most any robot builder, the job of translating a visual image a robot can use requires high-speed processing and complicated computer programming. Giving robots the ability to recognize shapes has proved to be a difficult task. Consider the static image of a doorway. Our brains easily comprehend the image, adapting to the angle at which we are viewing the doorway; the amount, direction, and contrast of the light falling on it; the size and kind of frame used in the doorway; whether the door is open or closed; and hundreds or even thousands of other variations. Robot vision requires that each of these variables be analyzed, a job that requires computer power and programming complexity beyond the means of most robot experimenters.
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