## Mobile RoboticsMobile Robotics: A Practical Introduction (2nd edition) is an excellent introduction to the foundations and methods used for designing completely autonomous mobile robots. A fascinating, cutting-edge, research topic, autonomous mobile robotics is now taught in more and more universities. In this book you are introduced to the fundamental concepts of this complex field via twelve detailed case studies that show how to build and program real working robots. Topics covered in clued learning, autonomous navigation in unmodified, noisy and unpredictable environments, and high fidelity robot simulation. This new edition has been updated to include a new chapter on novelty detection, and provides a very practical introduction to mobile robotics for a general scientific audience. It is essential reading for 2nd and 3rd year undergraduate students and postgraduate students studying robotics, artificial intelligence, cognitive science and robot engineering. The update and overview of core concepts in mobile robotics will assist and encourage practitioners of the field and set challenges to explore new avenues of research in this exiting field. The author is Senior Lecturer at the Department of Computer Science at the University of Essex. "A very fine overview over the relevant problems to be solved in the attempt to bring intelligence to a moving vehicle." Professor Dr. Ewald von Puttkamer, University of Kaiserslautern "Case studies show ways of achieving an impressive repertoire of kinds of learned behaviour, navigation and map-building. The book is an admirable introduction to this modern approach to mobile robotics and certainly gives a great deal of food for thought. This is an important and though-provoking book." Alex M. Andrew in Kybernetes Vol 29 No 4 and Robotica Vol 18 |

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### Contents

Introduction | 1 |

Foundations | 7 |

Robot Hardware | 25 |

Making Sense of Raw Sensor Data | 47 |

Navigation | 95 |

Novelty Detection | 167 |

Modelling RobotEnvironment Interaction | 183 |

Analysis of Robot Behaviour | 199 |

The Fundamental Theorem and Pappuss Theorem | 33 |

Onedimensional Projectivities | 41 |

Twodimensional Projectivities | 49 |

Polarities | 60 |

The Conic | 71 |

The Conic Continued | 81 |

A Finite Projective Plane | 91 |

Parallelism | 102 |

Outlook | 249 |

Answers to Exercises | 255 |

List of Exercises and Case Studies | 263 |

273 | |

Coordinates | 111 |

Answers to Exercises 133 | 133 |

157 | |

### Common terms and phrases

achieved algorithm artificial neural network autonomous mobile robots Axiom axis behaviour collinear points compass Computer conic coordinates correlation corresponding dead reckoning determine diagonal points distance distinct points environment episodic mapping equation example Exercise experiments FortyTwo function given harmonic conjugate hyperbolic input vector intelligent invariant point involution landmarks layer line at infinity localisation machine learning map-building mechanism motor action navigation system neural network neuron novelty detection obstacle avoidance obtained odometry output pairs parallel patterns pencil Perceptron perceptual aliasing performance perspective polarity position problem projective collineation projective geometry Q-learning quadrilateral range real robot Reference reinforcement learning robot learning robot navigation route Section self-organising feature map self-polar sensor signals shown in figure sides simulation SOFM sonar sonar sensors space supervised learning symbol tangents task theorem tion transforms triangle truth table Ulrich Nehmzow values vertices wall following