Cover image for Mobile Robotics, 2nd Edition

Mobile Robotics, 2nd Edition

Author Luc Jaulin
Release Date: 2019/12/01
Topic:
0%
17
Chapters
0-1
Hours read
0k
Total Words
    Start Reading Now    
       Add to Wishlist       
View table of contents

Book Description

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.

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.

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.

Table of Contents

  1. Cover
  2. Introduction
  3. 1 Three-dimensional Modeling
    1. 1.1. Rotation matrices
    2. 1.2. Euler angles
    3. 1.3. Inertial unit
    4. 1.4. Dynamic modeling
    5. 1.5. Exercises
    6. 1.6. Corrections
  4. 2 Feedback Linearization
    1. 2.1. Controlling an integrator chain
    2. 2.2. Introductory example
    3. 2.3. Principle of the method
    4. 2.4. Cart
    5. 2.5. Controlling a tricycle
    6. 2.6. Sailboat
    7. 2.7. Sliding mode
    8. 2.8. Kinematic model and dynamic model
    9. 2.9. Exercises
    10. 2.10. Corrections
  5. 3 Model-free Control
    1. 3.1. Model-free control of a robot cart
    2. 3.2. Skate car
    3. 3.3. Sailboat
    4. 3.4. Exercises
    5. 3.5. Corrections
  6. 4 Guidance
    1. 4.1. Guidance on a sphere
    2. 4.2. Path planning
    3. 4.3. Voronoi diagram
    4. 4.4. Artificial potential field method
    5. 4.5. Exercises
    6. 4.6. Corrections
  7. 5 Instantaneous Localization
    1. 5.1. Sensors
    2. 5.2. Goniometric localization
    3. 5.3. Multilateration
    4. 5.4. Exercises
    5. 5.5. Corrections
  8. 6 Identification
    1. 6.1. Quadratic functions
    2. 6.2. The least squares method
    3. 6.3. Exercises
    4. 6.4. Corrections
  9. 7 Kalman Filter
    1. 7.1. Covariance matrices
    2. 7.2. Unbiased orthogonal estimator
    3. 7.3. Application to linear estimation
    4. 7.4. Kalman filter
    5. 7.5. Kalman–Bucy
    6. 7.6. Extended Kalman filter
    7. 7.7. Exercises
    8. 7.8. Corrections
  10. 8 Bayes Filter
    1. 8.1. Introduction
    2. 8.2. Basic notions of probabilities
    3. 8.3. Bayes filter
    4. 8.4. Bayes smoother
    5. 8.5. Kalman smoother
    6. 8.6. Exercises
    7. 8.7. Corrections
  11. References
  12. Index
  13. End User License Agreement
18.118.1.158