MIT Press Introduction to Autonomous Mobile Robots Part 4 potx

MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 4 potx

MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 4 potx

... robot both in order to design appro- priate mobile robots for tasks and to understand how to create control software for an instance of mobile robot hardware. Of course, mobile robots are not the ... θ()ξ · I 0= β Y R α β +()sin 0 = βα –= M ξ I · N N f N s β s t() N s β f N f ϕ f t() ϕ s t() ϕ t() 48 Chapter 3 mobile robot is a self-contained automaton that can wholly move...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 13 potx

MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 13 potx

... (figure 5.27). We can now rewrite equations (5.38) and (5.39) using equations (5 .42 ) and (5 .43 ). (5 .44 ) (5 .45 ) k k 1 + d x dt uw+= k σ k 2 σ w 2 k′ x ˆ k′ x ˆ k ut k 1+ t k –()+= σ k′ 2 σ k 2 σ w 2 t k ... section 5.2 .4] : (5 .47 ) This allows us to predict the robot’s position and its uncertainty after a movement spec- ified by the control input . Note that the belief state i...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots part 1 doc

MIT.Press.Introduction.to.Autonomous.Mobile.Robots part 1 doc

... 317 Autonomous Mobile Robots Introduction to Roland Illah R. SIEGWART NOURBAKHSH Autonomous Mobile Robots SIEGWART and NOURBAKHSH Introduction to Introduction to Autonomous Mobile Robots Roland Siegwart ... localization strategies, autonomous mapping, and navigation competence. Bringing together all aspects of mobile robotics into one volume, Introduction...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots part 2 pptx

MIT.Press.Introduction.to.Autonomous.Mobile.Robots part 2 pptx

... local- ization. The final part of chapter 5 is devoted to a discussion of the challenges and most promising techniques for mobile robots to autonomously map their surroundings. Mobile robotics is so ... 2.2) movement pendulum Oscillatory of a multi-link movement pendulum 8 Chapter 1 For example, AGV (autonomous guided vehicle) robots (figure 1.8) autonomously deliver parts betw...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 3 pdf

MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 3 pdf

... faced by different mobile robots. So you will see great variety in the wheel configurations of mobile robots. In fact, few robots use the Ackerman wheel configuration of the automobile because of ... sufficiently high torque to sup- port the robot, yet back drivable for safety. In addition to developing custom motors and software, Sony incorporated a color vision system that enab...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 7 pot

MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 7 pot

... between transmitter and receiver according to (4. 15) if the transmitter is moving and (4. 16) if the receiver is moving. In the case of a reflected wave (figure 4. 16b) there is a factor of 2 introduced, ... sensitive to light between 40 0 and 1000 nm wavelength. It is important to remember that photodiodes are less sensitive to the ultraviolet end of the spectrum (e.g., blue) and...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 8 pot

MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 8 pot

... In this case the optical v 0 v i v i v 0 140 Chapter 4 (4. 41) from which we can abbreviate ; (4. 42) and ; ; (4. 43) so that we obtain (4. 44) The derivative represents how quickly the intensity ... interactively to achieve the best performance. The resulting problem then amounts to the calculus of variations, and the Euler equa- tions yield (4. 47) (4. 48) where (4. 49) which...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 9 ppsx

MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 9 ppsx

... ρ i [m] 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 0.5197 0 .44 04 0 .48 50 0 .42 22 0 .41 32 0 .43 71 0.3912 0.3 949 0.3919 0 .42 76 0 .40 75 0.3956 0 .40 53 0 .47 52 0.5032 0.5273 0 .48 79 Figure 4. 37 Extracted line from laser range measurements ... Vision Tools in Computer Vision 158 Chapter 4 Table 4. 2 Measured values pointing angle of sensor θ i [deg] ra...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 10 pptx

MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 10 pptx

... such histograms as whole-image features, we need ways to compare to histograms to quantify the likelihood that the histograms map to nearby robot positions. The problem of defining useful histogram ... the image about to be processed. To do this, a histogram of the gradient magnitudes of the processed image is calculated (figure 4. 47). With this simple histogram it is easy to...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 11 doc

MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 11 doc

... are partic- ularly appropriate for study given their significant recent successes in enabling mobile robots to navigate a variety of environments, from academic research buildings, to factory floors, ... 5.16. Figure 5. 14 Example of exact cell decomposition. goal start 1 7 2 3 4 5 6 8 9 10 11 12 13 14 15 17 16 18 Mobile Robot Localization 199 explicit reasoning about the effec...
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