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Robotics and AI Robotics and AI complement each other. Scientists have dreamed for more than a century about building smart androids: robots that look like people, act like people, and can even reason like people. Androids exist, but they aren t very smart. Powerful computers exist, but they lack mobility. If a machine has the ability to move around under its own power, to lift things, and to move things, it seems reasonable that it should do so with some degree of intelligence if it is to accomplish anything worthwhile. Conversely, if a computer is to manipulate anything, it must be able to cause a machine to do physical work according to a precise program. Expert Systems The term expert systems refers to a method of reasoning in AI. Sometimes this scheme is called the rule-based system. Expert systems are used in the control of smart robots. The heart of an expert system is a set of facts and rules. In the case of a robotic system, the facts consist of data about the robot s work environment, such as a factory, an office, or a kitchen. The rules are statements of the logical form If X, then Y, similar to many of the statements in highlevel programming languages. An inference engine decides which logical rules should be applied in various situations and instructs the robot to carry out certain tasks. But the operation of the system can only be as sophisticated as the data supplied by human programmers. Expert systems can be used in computers to help people do research, make decisions, and make forecasts. A good example is a program that assists a physician in making a diagnosis. The

Binocular machine vision is the analog of binocular human vision. It is sometimes called stereo vision. In humans, binocular vision allows perception of depth. With only one eye, that is, with monocular vision, you can infer depth to a limited extent on the basis of perspective. Almost everyone, however, has had the experience of being fooled when looking at a scene with one eye covered or blocked. A nearby pole and a distant tower might seem to be adjacent, when in fact they are a city block apart. Figure 34-6 shows the basic concept of binocular robot vision. Of primary importance are high-resolution video cameras and a sufficiently powerful robot controller. Left eye sees: Right eye sees:

F 8-bits per sample, 24 samples per frame + frame bit = 193 bits. 8,000 frames are generated per second, yielding 1.544 Mbps.

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18. The meter movement in an illumination meter directly measures (a) current. (b) voltage. (c) power. (d) energy. 19. An oscilloscope cannot be used to indicate (a) frequency. (b) wave shape. (c) energy. (d) peak signal voltage. 20. What voltage would be expected to produce the reading on the bar-graph meter shown in Fig. 3-18 (a) 6.0 V (b) 6.5 V (c) 7.0 V (d) There is no way to tell because the meter, as shown, is malfunctioning.

34-6 Binocular machine vision. Two different views of the same object are combined to achieve a sense of depth and perspective.

of frame bits (one frame bit per frame, 8,000 frames per second); only 1.536 Mbps belong to the user.

more acquainted with this type of diagram. You ll also learn more about how current, voltage, resistance, and power are related in dc and low-frequency ac circuits.

Robot vision systems often function only in gray scale, like old-fashioned 1950s television. But color sensing can be added, in a manner similar to the way it is added to television systems. Color sensing can help a robot with AI tell what an object is. Is that horizontal surface a floor inside a building, or is it a grassy yard (If it is green, it s probably a grassy yard or maybe a playing field with artificial turf.) Sometimes, objects have regions of different colors that have identical brightness as seen by a gray-scale system; these objects, obviously, can be seen in more detail with a color-sensing system than with a vision system that sees only shades of gray. In a typical color-sensing vision system, three gray-scale cameras are used. Each camera has a color filter in its lens. One filter is red, another is green, and another is blue.

The earliest T-Carrier equipment was referred to as D1 and was considerably more rudimentary in function than modern systems (see Figure 3-47). In D1, every 8-bit sample carried 7 bits of user information (bits 1 through 7) and 1 bit for signaling (bit 8). The signaling bits were used for exactly that: indications of the status of the line (on-hook, offhook, busy, high and dry, and so on), while the 7 user bits carried encoded voice information. Because only 7 of the 8 bits were available to the user, the result was considered to be less than toll quality (128 possible values, rather than 256). The frame bits, which in modern systems indicate the beginning of the next 192-bit frame of data, toggled back and forth between 0 and 1.

Robotic navigation 657 These are the three primary colors. All possible hues, levels of brightness, and levels of saturation are made up of these three colors in various ratios. The signals from the three cameras are processed by a microcomputer, and the result is fed to the robot controller.

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