Reliable Robot Localization

A Constraint-Programming Approach Over Dynamical Systems
1. Auflage 2020
ISBN: 978-1-84821-970-0
Verlag: Wiley

Buch, Englisch, 288 Seiten, Format (B × H): 157 mm x 236 mm, Gewicht: 590 g

Localization for underwater robots remains a challenging issue. Typical sensors, such as Global Navigation Satellite System (GNSS) receivers, cannot be used under the surface and other inertial systems suffer from a strong integration drift. On top of that, the seabed is generally uniform and unstructured, making it difficult to apply Simultaneous Localization and Mapping (SLAM) methods to perform localization. Reliable Robot Localization presents an innovative new method which can be characterized as a raw-data SLAM approach. It differs from extant methods by considering time as a standard variable to be estimated, thus raising new opportunities for state estimation, so far underexploited. However, such temporal resolution is not straightforward and requires a set of theoretical tools in order to achieve the main purpose of localization. This book not only presents original contributions to the field of mobile robotics, it also offers new perspectives on constraint programming and set-membership approaches. It provides a reliable contractor programming framework in order to build solvers for dynamical systems. This set of tools is illustrated throughout this book with realistic robotic applications.

Autoren/Hrsg.

Rohou, Simon

Jaulin, Luc

Mihaylova, Lyudmila

Le Bars, Fabrice

Veres, Sandor M

Inhaltsverzeichnis

Preface xi

Notations xiii

Abbreviations xvii

Introduction xix

Part 1. Interval Tools 1

Introduction to Part 1 3

Chapter 1. Static Set-membership State Estimation 5

1.1. Introduction 5

1.2. Interval analysis 8

1.2.1. Once upon a time 8

1.2.2. Intervals 10

1.2.3. Inclusion functions 14

1.2.4. Pessimism and wrapping effect 16

1.3. Constraint propagation 19

1.3.1. Constraint networks 19

1.3.2. Contractors 21

1.3.3. Application to static range-only robot localization 24

1.4. Set-inversion via interval analysis 25

1.4.1. Subpaving 25

1.4.2. SIVIA algorithm for set-inversion 28

1.4.3. Illustration involving contractions 29

1.4.4. Kernel characterization of an interval function 33

1.5. Discussions 35

1.5.1. From sensors to reliable results 36

1.5.2. Numerical libraries 37

1.5.3. Reliable tool for proof purposes 38

1.6. Conclusion 38

Chapter 2. Constraints Over Sets of Trajectories 41

2.1. Towards dynamic state estimation 41

2.1.1. Overall motivations 41

2.1.2. The approach presented in this book 43

2.2. Tubes 44

2.2.1. Definitions 44

2.2.2. Tube analysis 45

2.2.3. Contractors 48

2.3. Implementation 50

2.3.1. Data structure 52

2.3.2. Build a tube from real datasets 54

2.3.3. Tubex, dedicated tube library 57

2.4. Application: dead-reckoning of a mobile robot 57

2.4.1. Test case 58

2.4.2. Constraint network 58

2.4.3. Resolution 59

2.5. Discussions 60

2.5.1. Limits 60

2.5.2. Extract the most probable trajectory from a tube 61

2.5.3. Application to path planning 62

2.6. Conclusion 63

Part 2. Constraints-related Contributions 65

Introduction to Part 2 67

Chapter 3. Trajectories under Dif

Über Autor(innen)

Simon Rohou is an Associate Professor at ENSTA Bretagne -Lab-STICC (Brest, France). Luc Jaulin is Full Professor of Robotics at ENSTA Bretagne-Lab-STICC. Lyudmila Mihaylova is Professor of Signal Processing and Control with the Department of Automatic Control and Systems Engineering at the University of Sheffield (UK). Fabrice Le Bars is an Associate Professor at ENSTA Bretagne-Lab-STICC. Sandor M. Veres holds a chair in Autonomous Control Systems, and leads the Robotics and Autonomous Systems Research Group at the Department of Automatic Control and Systems Engineering at the University of Sheffield.