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The robot we will build in this chapter will be capable of looking at and seeing an object (or target) and following that object If the object gets too close to the robot, the robot will back away from it Choose a bright target that will have good contrast with the background environment Colored objects are fine; a bright red or yellow ball works fine For my tests I used a 25 in square of orange construction paper taped to a stiff wire One of the most difficult areas in robotics today is the creation of an artifi cial vision system for a robot to see Teaching a robot to see is not simply a case of connecting a video camera to a computer The electronic representation of an image created by a video camera must be presented in a way a computer can look at and interpret ( see ) the image To gain an understanding of the processes involved, let s examine how a computer might look at a simple black and white image We must first define the resolution of the picture image in pixels For our discussion let s assume a low resolution picture of 80 143 pixels At that resolution our computer must look at 11,440 pixels (80 143 11,440) Each pixel of the picture can be any tonality of gray between pure black and pure white We now have to determine how many different shades our computer can differentiate between pure black and pure white If we wanted each pixel to be represented by a single byte (8 bit number), there would be 256 shades of gray, including pure white (0) and black (255) The computer would look at each pixel and assign a number between 0 and 255 depending upon its tonality (grayness) After the computer assigned num bers to each 11,440 pixels, it transformed the basic image into a numbered representation it needs to look at the image The software that looks at an image and interprets visual features is appro priately called image processing Robotists over the years have gleaned some techniques for helping computers to see One technique is called edge detec tion Here the computer looks through the image You program the computer
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Figure 141 Field of view from CMU camera
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so that any pixel dark enough to be a 200 number or larger is a possible edge, and you change that pixel to pure black, number 256 Any number less than 200 probably isn t an edge, and the computer changes those pixels to pure white, number 1 What is left is a simplified representation of the picture that can be more easily analyzed The same process used for edge detection may also be used to detect partic ular colors in an image or the contrast between colors Once an object has been detected, through contrast, color, or edge detection, the processing software can assign location parameters to the object within the image and field of view (FOV) of the camera In Fig 141 we have a representation of the FOV from the CMU camera we will be using and a few of the image processing parameters available Once an object is detected by the camera, we can read these image processing parame ters in real time from the serial communication port of the camera We use these parameters to track an object in the camera s image space and to move our robot accordingly CMU Camera The CMU camera (see Fig 142) was developed at Carnegie Mellon University (CMU) The CMU camera uses an SX28 microcontroller interfaced to an Omnivision OV6620 CMOS camera chip The SX28 microcontroller does much of the image processing for us We communicate with the camera via a stan dard RS 232 or TTL serial port A few of the CMU camera features are as follows: Tracks user defined color objects at 17 frames per second Finds the center of the object
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