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As the world goes online, many of our activities are no longer in the realm of the physical but involve traversing and manipulating our new electronic realities. There will be online tasks that we don t want to do ourselves
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because they are hard, tedious, or otherwise better left to automation. And some of these tasks will take a fair level of intelligence. What is needed is an electronic robot with many of the traits of its physical counterpart. The ability to travel around its environment, in this case the Internet. The ability to sense and manipulate this environment. And the ability to match incomplete or unclear patterns and to make action decisions based on them. Such a virtual robot is known as an agent. Agents tend to operate in swarms, interacting with other agents with perhaps di erent capabilities. Agents lack the physical dimension, so there is none of that pesky, expensive hardware to worry about. They are robots in their most abstract form, pure information. People have been creating simulated robots in simulated environments almost as long as we have been building computer-driven robots. It is easier and faster to perform experiments in the computer before committing the design to solid form. But those are still just simulations of a robot whose ultimate operation is to be in the physical world. An agent s environment is the world of information.
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This chapter gave an overview of some arti cial intelligence concerns, and how AI techniques could be applied to robots. First we stepped back and looked at the role of pattern matching in the decision process. Then we saw how the apparently simple task of mapping a room is complicated by the realities of mechanical construction and inaccurate sensors. The neural systems we explored in 17 were expanded to allow them to be trained and then to train themselves. In closing, we mentioned some new, nontraditional robotic systems such as swarms of robots and online agents.
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1. 2. 3. 4. 5. What are the sensory inputs on industrial robots used for Name and describe a technique used to calculate a robot s position. What is the problem with the dead-reckoning system from question 2 What are the two types of neural network learning Do robots always have to have a physical body
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1. 1.2 meters (4 feet). In the equation d (a t2)/2, a is the acceleration from gravity which is 9.8 m/s2. Time t is 0.5. 9:8 0:52 2 9:8 0:25 d 2 d 1:2 d Bonus question: First, you need to turn the equation inside out, so solve for t: r 2 d t a So, solving for t with d 3 and a 9.8 gives a travel time of 0.78 seconds: r 2 3 t 9:8 p t 0:612 t 0:78 2. This is a tricky question, since the details are spread around. The force is the mass times the acceleration, sure. But what is the acceleration of a ball being stopped in a tenth of a second Acceleration is the change in velocity over time. We know the ball was moving at 10 meters in 1 second, and once it hits the wall it is
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ANSWERS
moving at 0 m/s. So the change in velocity is 10 m/s. We can ignore the negative sign, it is just telling us that this is a deceleration instead of an acceleration. Since this change in velocity accurs across 0.10 seconds, we see 10 m/s divided by 0.1 seconds, for an acceleration of 100 m/s2. So the applied force is 50 newtons: 10 1 0:1 F 0:5 100 F 0:5 F 50 A soft wall changes the velocity to zero across 0.25 seconds, for an acceleration of 40 m/s. The soft wall only needs to apply 20 newtons of force, but it does it across a longer time span: 10 0:25 F 0:5 40 F 0:5 F 20 3 0.5 kg at 10 m/s has a kinetic energy of 25 joules. This is a straigtforward solution of the kinetic energy formula: 0:5 102 2 KE 0:25 100 KE KE 25 At 20 m/s, the kinetic energy is quite a bit larger: 0:5 202 2 KE 0:25 400 KE KE 100 4. Another easy solution, using the potential energy equation: PE 0:5 9:8 4 PE 19:6 Now, you know that the ball dropped from 4 meters, so the time it would take to hit the ground is 0.9 seconds. Since it crossed these 4 meters in 0.9 seconds, you might think that its velocity is 0.9 m/s,
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