Details

Mobile Robotics


Mobile Robotics


2. Aufl.

von: Luc Jaulin

126,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 20.09.2019
ISBN/EAN: 9781119663492
Sprache: englisch
Anzahl Seiten: 384

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Beschreibungen

<p>Mobile Robotics presents the different tools and methods that enable the design of mobile robots; a discipline booming with the emergence of flying drones, underwater mine-detector robots, robot sailboats and vacuum cleaners.<br /> <br /> Illustrated with simulations, exercises and examples, this book describes the fundamentals of modeling robots, developing the concepts of actuators, sensors, control and guidance. Three-dimensional simulation tools are also explored, as well as the theoretical basis for the reliable localization of robots within their environment.<br /> <br /> This revised and updated edition contains additional exercises and a completely new chapter on the Bayes filter, an observer that enhances our understanding of the Kalman filter and facilitates certain proofs.</p>
<p>Introduction ix</p> <p><b>Chapter 1. Three-dimensional Modeling</b><b> 1</b></p> <p>1.1. Rotation matrices 1</p> <p>1.1.1. Definition 2</p> <p>1.1.2. Lie group 3</p> <p>1.1.3. Lie algebra 4</p> <p>1.1.4. Rotation vector 5</p> <p>1.1.5. Adjoint 6</p> <p>1.1.6. Rodrigues rotation formulas 7</p> <p>1.1.7. Coordinate system change 8</p> <p>1.2. Euler angles 11</p> <p>1.2.1. Definition 11</p> <p>1.2.2. Rotation vector of a moving Euler matrix 13</p> <p>1.3. Inertial unit 14</p> <p>1.4. Dynamic modeling 17</p> <p>1.4.1. Principle 17</p> <p>1.4.2. Modeling a quadrotor 18</p> <p>1.5. Exercises 20</p> <p>1.6. Corrections 37</p> <p><b>Chapter 2. Feedback Linearization</b><b> 65</b></p> <p>2.1. Controlling an integrator chain 65</p> <p>2.1.1. Proportional-derivative controller 66</p> <p>2.1.2. Proportional-integral-derivative controller 67</p> <p>2.2. Introductory example 68</p> <p>2.3. Principle of the method 69</p> <p>2.3.1. Principle 69</p> <p>2.3.2. Relative degree 71</p> <p>2.3.3. Differential delay matrix 72</p> <p>2.3.4. Singularities 73</p> <p>2.4. Cart 75</p> <p>2.4.1. First model 75</p> <p>2.4.2. Second model 76</p> <p>2.5. Controlling a tricycle 78</p> <p>2.5.1. Speed and heading control 78</p> <p>2.5.2. Position control 80</p> <p>2.5.3. Choosing another output 81</p> <p>2.6. Sailboat 82</p> <p>2.6.1. Polar curve 83</p> <p>2.6.2. Differential delay 83</p> <p>2.6.3. The method of feedback linearization 84</p> <p>2.6.4. Polar curve control 87</p> <p>2.7. Sliding mode 87</p> <p>2.8. Kinematic model and dynamic model 90</p> <p>2.8.1. Principle 90</p> <p>2.8.2. Example of the inverted rod pendulum 91</p> <p>2.8.3. Servo-motors 94</p> <p>2.9. Exercises 95</p> <p>2.10. Corrections 107</p> <p><b>Chapter 3. Model-free Control</b><b> 133</b></p> <p>3.1. Model-free control of a robot cart 134</p> <p>3.1.1. Proportional heading and speed controller 134</p> <p>3.1.2. Proportional-derivative heading controller 136</p> <p>3.2. Skate car 137</p> <p>3.2.1. Model 138</p> <p>3.2.2. Sinusoidal control 140</p> <p>3.2.3. Maximum thrust control 140</p> <p>3.2.4. Simplification of the fast dynamics 142</p> <p>3.3. Sailboat 145</p> <p>3.3.1. Problem 145</p> <p>3.3.2. Controller 146</p> <p>3.3.3. Navigation 152</p> <p>3.3.4. Experiment 153</p> <p>3.4. Exercises 155</p> <p>3.5. Corrections 168</p> <p><b>Chapter 4. Guidance</b><b> 183</b></p> <p>4.1. Guidance on a sphere 183</p> <p>4.2. Path planning 187</p> <p>4.2.1. Simple example 187</p> <p>4.2.2. Bézier polynomials 188</p> <p>4.3. Voronoi diagram 189</p> <p>4.4. Artificial potential field method 191</p> <p>4.5. Exercises 192</p> <p>4.6. Corrections 201</p> <p><b>Chapter 5. Instantaneous Localization</b><b> 221</b></p> <p>5.1. Sensors 221</p> <p>5.2. Goniometric localization 225</p> <p>5.2.1. Formulation of the problem 225</p> <p>5.2.2. Inscribed angles 226</p> <p>5.2.3. Static triangulation of a plane robot 228</p> <p>5.2.4. Dynamic triangulation 229</p> <p>5.3. Multilateration 230</p> <p>5.4. Exercises 231</p> <p>5.5. Corrections 236</p> <p><b>Chapter 6. Identification</b><b> 243</b></p> <p>6.1. Quadratic functions 243</p> <p>6.1.1. Definition 243</p> <p>6.1.2. Derivative of a quadratic form 244</p> <p>6.1.3. Eigenvalues of a quadratic function 245</p> <p>6.1.4. Minimizing a quadratic function 245</p> <p>6.2. The least squares method 246</p> <p>6.2.1. Linear case 246</p> <p>6.2.2. Nonlinear case 248</p> <p>6.3. Exercises 250</p> <p>6.4. Corrections 253</p> <p><b>Chapter 7. Kalman Filter</b><b> 263</b></p> <p>7.1. Covariance matrices 263</p> <p>7.1.1. Definitions and interpretations 263</p> <p>7.1.2. Properties 266</p> <p>7.1.3. Confidence ellipse 267</p> <p>7.1.4. Generating Gaussian random vectors 268</p> <p>7.2. Unbiased orthogonal estimator 269</p> <p>7.3. Application to linear estimation 274</p> <p>7.4. Kalman filter 275</p> <p>7.5. Kalman–Bucy 279</p> <p>7.6. Extended Kalman filter 282</p> <p>7.7. Exercises 283</p> <p>7.8. Corrections 298</p> <p><b>Chapter 8. Bayes Filter</b> <b>329</b></p> <p>8.1. Introduction 329</p> <p>8.2. Basic notions of probabilities 329</p> <p>8.3. Bayes filter 332</p> <p>8.4. Bayes smoother 334</p> <p>8.5. Kalman smoother 335</p> <p>8.5.1. Equations of the Kalman smoother 335</p> <p>8.5.2. Implementation 336</p> <p>8.6. Exercises 337</p> <p>8.7. Corrections 345</p> <p>References 359</p> <p>Index 361</p>
<p>Luc Jaulin is Professor in robotics at ENSTA-Bretagne in France. He conducts research at the Lab-STICC in the field of submarine robotics and sailing robots using set methods. </p>

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