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1. De ne intelligence. Is intelligence good for a punch press machine 2. What does a PID control used for What does it do 3. For sequential behaviors, what is the tricky part How about for layered behaviors 4. Describe a pattern-matching system inspired from nature.
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The subject of robotic control is huge. You could ll a small library with the research and control techniques used to manage both industrial robots and the more ambitious research robots, many of which are trying to ll the role of a human. Some of these humanoid robots are caretakers, helping out the disabled. Others are guides either mechanical or virtual (software only) tour guides for museums, hospitals, or online help systems. Then there is NASA s humanoid robot designed to face the rigors of space. The nonhuman explorer robots, wheeled rovers or legged explorers, need the exibility of intelligent control as they wander harsh or distant landscapes. This chapter is a continuation of 17. In the previous chapter we focused on control systems and pattern matching to enable the robot to switch between behaviors as needed. This chapter looks at decision-making a little bit more and then spends the rest of its time exploring new topics.
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Copyright 2005 by The McGraw-Hill Companies, Inc. Click here for terms of use.
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One of the key aspects of intelligence as we recognize it is the ability to learn. We look at some apparently simple learning tasks and then take a deeper look at techniques for robotic education. You may have noticed a distinct shortage of hands-on projects in these control chapters. While it is easy to snap together a mechanism to illustrate a mechanical concept, or wire together a few components to show o a circuit, control programs are harder to create. While the RCX owchart language provides for some easy examples, it is also limited in what it can do. The other text-based languages mentioned in 16 have the power we need, even on a simple controller like the RCX, but involve a lot of infrastructure to make them work. With programming, you enter into the world of complex grammars and the supporting network of tools needed to make the languages work: compilers, downloaders, debuggers. It takes several chapters just to get started with programming. So most of our explorations of control concepts are in the abstract. I encourage you to look up more books on the subject of Arti cial Intelligence, the foundation of most nonindustrial robotic systems. For example, Hands-On AI with Java by Edwin Wise (McGraw-Hill, 2004) provides a better introduction to the subject.
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We have already peeked into the world of decision-making in 17, with serial and layered behaviors supported by pattern matching to trigger changes. The gray area of decision-making lls the gap between machine control and raw sensory data. You have inputs, you have outputs how do you tie the two together For industrial machines, the senses are used to ne-tune the control. There is no sensing and reacting to the environment in a big sense, no strategic decisions based on external input. The sensor is used to make the industrial robot more accurate, to keep the process within de ned limits. All of the important decisions were made by the human who wrote the program or script that is driving the robot. As we approach the intelligent robot, the subject of how it chooses its behavior grows larger. Many of the mobile robots don t make decisions, but operate on simple re ex. Bump into something, avoid it. Receive an instruction and follow it. Sense a low battery and return to home. Instinct, like a bug or worm, reacting in a basic way to its environment. Surprisingly,
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