... xi Preface xiii 1 Introduction 1 1 .1 Introduction 1 1.2 An Overview of the Book 10 2 Locomotion 13 2 .1 Introduction 13 2 .1. 1 Key issues for locomotion 16 2.2 Legged Mobile Robots 17 2.2 .1 Leg configurations ... 314 Interesting Internet Links to Mobile Robots 314 Index 317 Autonomous Mobile Robots Introduction to Roland Illah R. SIEGWART N...
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... 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 19 9 explicit reasoning about the effect that trajectories ... approach is [14 1], in which the robot plans a path from point to point that takes it near a series of landmarks in order to mitigate localization difficulties. This type of...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 12 doc
... the topological representations. Interestingly, the continuous map representation has Figure 5 .18 A topological representation of an indoor office area. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 18 ... belief state is expressed as a sum over previous possible positions and the motion model: (5.26) 15 15 1 5 ×× 15 3 15 cm 15 cm 1 ×× t Pl t o t () Pl t l' t 1 o t ,()pl...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots part 2 pptx
... gravity or by imparting corrective forces. Thus, the successful single-legged robot must be dynamically stable. N 11 ! 39 916 80 0 == 14 Chapter 2 Owing to these limitations, mobile robots generally ... terrain capability. The most popular research robots are those of ActivMedia Robotics, K-Team SA, and I- Robot (figures 1. 11, 1. 12, 1. 13) and also very small robots like the...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 3 pdf
... each paw. 11 Tail light: Lights up blue or orange to show AIBO’s emotional state. 12 Back sensor: Senses when a person touches AIBO on the back. ERS- 210 © 2000 Sony Corporation ERS -11 0 © 19 99 Sony ... person touches AIBO on the chin. 8 Pause button: Press to activate AIBO or to pause AIBO. 9 Chest light: Gives information about the status of the robot. 10 Paw sensors: Locate...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 4 potx
... with the local reference 10 l 10 l– 010 R θ()ξ I · J 2 ϕ 0 = ξ I · R θ() 1 10 l 10 l– 01 0 1 J 2 ϕ 0 R θ() 1 1 2 1 2 0 0 01 1 2l 1 2l –0 J 2 ϕ 0 == Figure 3 .10 A three-wheel omnidrive ... using equation (3 .12 ) yields Figure 3 .11 The local reference frame plus detailed parameters for wheel 1. Y R X R 1 2 r ϕ⋅ 1 ω 1 3 v y1 ICR v x1 X R P l r ξ I · J 1 β s...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 7 pot
... 0. 2 = f 0. 5 = R δ d 1= e 1= δ d 2= δ R 0.02= R 0.08= d 1 0 = e 0.52 6 = d 11 = e 0.52 4 = R 0 .11 7 = R 0 .12 9 = R f Perception 12 1 approach: the pixel-specific circuitry next to every pixel measures ... robust vision-based sen- sors for mobile robots be available. Figure 4 .18 A commercially available, low-cost CMOS camera with lens attached. Perception 11 1 In figure 4...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 8 pot
... to identify each zero crossing of the LoG as such a feature. Figure 4.23 Step function example of second derivative shape and the impact of noise. 1 16 2 16 1 16 2 16 4 16 2 16 1 16 ... pairs . x P P r ' l x' l y' l z ' l ,,()= P P r ' r x' r y' r z ' r ,,()= R r 0 r ' r r ' l r ' r Rr ' l r 0 +⋅= R r 0 x&a...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 9 ppsx
... algorithms. I ˆ GI⊗= G 1 16 12 1 242 12 1 = 16 0 Chapter 4 feature represented in the measurement set. This more realistic scenario is shown in figure 4.38. The process of dividing up a set of measurements into subsets ... Space β 0 =α [rad] x 1 α 1 r 1 ,[]= x 2 α 2 r 2 ,[]= x 1 x 2 –() T x 1 x 2 –()α 1 α 2 –() 2 r 1 r 2 –() 2 += Perception 15 7 . (4.72) It can be shown t...
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MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 10 pptx
... angle with respect to the column axis. Figure 4.46 shows application of the Sobel filter to a visual scene. θ p 1 p 2 atan≅ p 1 1 1 1 000 11 1 = p 2 1 01 1– 01 1– 01 = s 1 s 2 G θ Figure ... that pass through . Gs 1 2 s 2 2 +≅ θ s 1 s 2 atan≅ s 1 1–2 1 000 12 1 = s 2 1 01 2–02 1 01 = n n n n x p y p ,() I x p y p ,() y p m 1 x p b 1 += m 1 b...
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