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Range sensing is the measurement of distances to objects in a robot s environment in a single dimension. Range plotting is the creation of a graph of the distance (range) to objects, as a function of the direction in two or three dimensions. For one-dimensional (1-D) range sensing, a signal is sent out, and the robot measures the time it takes for the echo to come back. This signal can be sound, in which case the device is sonar. Or it can be a radio wave; this constitutes radar. Laser beams can also be used. Close-in, one-dimensional range sensing is known as proximity sensing. Two-dimensional (2-D) range plotting involves mapping the distance to various objects, as a function of their direction. The echo return time for a sonar signal, for example, might be measured every few degrees around a complete circle, resulting in a set of range points. A better plot would be obtained if the range were plotted every degree, every tenth of a degree, or even every minute of arc (1/60 degree). But no matter how detailed the direction resolution, a 2-D range plot renders only one plane, such as the floor level in a room, or some horizontal plane above the floor. The greater the number of echo samples in a complete circle (that is, the smaller the angle between samples), the more detail can be resolved at a given distance from the robot, and the greater the distance at which a given amount of detail can be resolved. Three-dimensional (3-D) range plotting is done in spherical coordinates: azimuth (compass bearing), elevation (degrees above the horizontal), and range (distance). The distance must be measured for a large number of directions preferably at least several thousand at all orientations. In a furnished room, a 3D sonar range plot would show ceiling fixtures, things on the floor, objects on top of a desk, and other details not visible with a 2-D plot. The greater the number of echo samples in a complete sphere surrounding the robot, the more detail can be resolved at a given distance, and the greater the range at which a given amount of detail can be resolved.
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Epipolar navigation is a means by which a machine can locate objects in three-dimensional space. It can also navigate, and figure out its own position and path. Epipolar navigation works by evaluating the way an image changes as the viewer moves. The human eyes and brain do this without having to think, although they are not very precise. Robot vision systems, along with AI, can do it with extreme precision. Imagine you re piloting an airplane over the ocean. The only land you see is a small island. You have an excellent map that shows the location, size, and shape of this island (Fig. 34-9). For instrumentation, you have only a computer, a video camSighting A Sighting B Sighting C
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34-9 Epipolar navigation is a form of electronic spatial perception requiring only one observation point, but that point must be moving.
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Telepresence 661 era, and AI software. You can figure out your coordinates and altitude, using only these devices, by letting the computer work with the image of the island. As you fly along, you aim the camera at the island and keep it there. The computer sees an image that constantly changes shape. The computer has the map data, so it knows the true size, shape, and location of the island. The computer compares the shape/size of the image it sees, from the vantage point of the aircraft, with the actual shape/size of the island, which it knows from the map data. From this alone, it can determine your altitude, your speed relative to the surface, your exact latitude, and your exact longitude. There is a one-to-one correspondence between all points within sight of the island and the size/shape of the island s image. Epipolar navigation works on any scale, for any speed. It is a method by which robots can find their way without triangulation, direction finding, beacons, sonar, or radar. It is only necessary that the robot have a computer map of its environment and that viewing conditions be satisfactory.
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