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Offers a theoretical and practical guide to the communication and navigation of autonomous mobile robots and multi-robot systems

This book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups. It addresses methods of positioning in global and local coordinates systems, off-line and on-line path-planning, sensing and sensors fusion, algorithms of obstacle avoidance, swarming techniques and cooperative behavior. The book includes ready-to-use algorithms, numerical examples and simulations, which can be directly implemented in both simple and advanced mobile robots, and is accompanied by a website hosting codes, videos, and PowerPoint slides

Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming consists of four main parts. The first looks at the models and algorithms of navigation and motion planning in global coordinates systems with complete information about the robot's location and velocity. The second part considers the motion of the robots in the potential field, which is defined by the environmental states of the robot´s expectations and knowledge. The robot´s motion in the unknown environments and the corresponding tasks of environment mapping using sensed information is covered in the third part. The fourth part deals with the multi-robot systems and swarm dynamics in two and three dimensions.

Provides a self-contained, theoretical guide to understanding mobile robot control and navigation
Features implementable algorithms, numerical examples, and simulations
Includes coverage of models of motion in global and local coordinates systems with and without direct communication between the robots
Supplemented by a companion website offering codes, videos, and PowerPoint slides
Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming is an excellent tool for researchers, lecturers, senior undergraduate and graduate students, and engineers dealing with mobile robots and related issues.



TABLE OF CONTENTS
List of Contributors xi

Preface xiii

Acknowledgments xv

About the Companion Website xvii

Introduction 1
Eugene Kagan, Nir Shvalb, and Irad Ben-Gal

I.1 Early History of Robots 1

I.2 Autonomous Robots 2

I.3 Robot Arm Manipulators 6

I.4 Mobile Robots 8

I.5 Multi-Robot Systems and Swarms 12

I.6 Goal and Structure of the Book 16

References 17

1 Motion-Planning Schemes in Global Coordinates 21
Oded Medina and Nir Shvalb

1.1 Motivation 21

1.2 Notations 21

1.2.1 The Configuration Space 22

1.2.2 The Workspace 23

1.2.3 The Weight Function 23

1.3 Motion-Planning Schemes: Known Configuration Spaces 25

1.3.1 Potential-Field Algorithms 25

1.3.2 Grid-Based Algorithms 27

1.3.3 Sampling-Based Algorithms 29

1.4 Motion-Planning Schemes: Partially Known Configuration Spaces 30

1.4.1 BUG0 (Reads Bug-Zero) 31

1.4.2 BUG1 32

1.4.3 BUG2 32

1.5 Summary 33

References 33

2 Basic Perception 35
Simon Lineykin

2.1 Basic Scheme of Sensors 35

2.2 Obstacle Sensor (Bumper) 36

2.3 The Odometry Sensor 48

2.4 Distance Sensors 52

2.4.1 The ToF Range Finders 52

2.4.2 The Phase Shift Range Finder 56

2.4.3 Triangulation Range Finder 59

2.4.4 Ultrasonic Rangefinder 60

2.5 Summary 63

References 63

3 Motion in the Global Coordinates 65
Nir Shvalb and Shlomi Hacohen

3.1 Models of Mobile Robots 65

3.1.1 Wheeled Mobile Robots 65

3.1.2 Aerial Mobile Robots 67

3.2 Kinematic and Control of Hilare-Type Mobile Robots 69

3.2.1 Forward Kinematics of Hilare-Type Mobile Robots 69

3.2.2 Velocity Control of Hilare-Type Mobile Robots 71

3.2.3 Trajectory Tracking 72

3.3 Kinematic and Control of Quadrotor Mobile Robots 74

3.3.1 Dynamics of Quadrotor-Type Mobile Robots 74

3.3.2 Forces and Torques Generated by the Propellers 75

3.3.3 Relative End Global Coordinates 76

3.3.4 The Quadrotor Dynamic Model 78

3.3.5 A Simplified Dynamic Model 79

3.3.6 Trajectory Tracking Control of Quadrotors 80

3.3.7 Simulations 84

References 85

4 Motion in Potential Field and Navigation Function 87
Nir Shvalb and Shlomi Hacohen

4.1 Problem Statement 87

4.2 Gradient Descent Method of Optimization 89

4.2.1 Gradient Descent Without Constraints 89

4.2.2 Gradient Descent with Constraints 92

4.3 Minkowski Sum 94

4.4 Potential Field 95

4.5 Navigation Function 99

4.5.1 Navigation Function in Static Deterministic Environment 99

4.5.2 Navigation Function in Static Uncertain Environment 102

4.5.3 Navigation Function and Potential Fields in Dynamic Environment 104

4.5.3.1 Estimation 105

4.5.3.2 Prediction 105

4.5.3.3 Optimization 106

4.6 Summary 106

References 107

5 GNSS and Robot Localization 109
Roi Yozevitch and Boaz Ben-Moshe

5.1 Introduction to Satellite Navigation 109

5.1.1 Trilateration 109

5.2 Position Calculation 111

5.2.1 Multipath Signals 111

5.2.2 GNSS Accuracy Analysis 112

5.2.3 DoP 112

5.3 Coordinate Systems 113

5.3.1 Latitude, Longitude, and Altitude 113

5.3.2 UTM Projection 113

5.3.3 Local Cartesian Coordinates 114

5.4 Velocity Calculation 115

5.4.1 Calculation Outlines 115

5.4.2 Implantation Remarks 116

5.5 Urban Navigation 116

5.5.1 Urban Canyon Navigation 117

5.5.2 Map Matching 117

5.5.3 Dead Reckoning - Inertial Sensors 118

5.6 Incorporating GNSS Data with INS 118

5.6.1 Modified Particle Filter 118

5.6.2 Estimating Velocity by Combining GNSS and INS 119

5.7 GNSS Protocols 120

5.8 Other Types of GPS 121

5.8.1 A-GPS 121

5.8.2 DGPS Systems 122

5.8.3 RTK Navigation 122

5.9 GNSS Threats 123

5.9.1 GNSS Jamming 123

5.9.2 GNSS Spoofing 123

References 123

6 Motion in Local Coordinates 125
Shraga Shoval

6.1 Global Motion Planning and Navigation 125

6.2 Motion Planning with Uncertainties 128

6.2.1 Uncertainties in Vehicle Performance 128

6.2.1.1 Internal Dynamic Uncertainties 128

6.2.1.2 External Dynamic Uncertainties 129

6.2.2 Sensors Uncertainties 129

6.2.3 Motion-Planning Adaptation to Uncertainties 130

6.3 Online Motion Planning 131

6.3.1 Motion Planning with Differential Constraints 132

6.3.2 Reactive Motion Planning 134

6.4 Global Positioning with Local Maps 135

6.5 UAV Motion Planning in 3D Space 137

6.6 Summary 139

References 140

7 Motion in an Unknown Environment 143
Eugene Kagan

7.1 Probabilistic Map-Based Localization 143

7.1.1 Beliefs Distribution and Markov Localization 145

7.1.2 Motion Prediction and Kalman Localization 150

7.2 Mapping the Unknown Environment and Decision-Making 154

7.2.1 Mapping and Localization 155

7.2.2 Decision-Making under Uncertainties 161

7.3 Examples of Probabilistic Motion Planning 169

7.3.1 Motion Planning in Belief Space 169

7.3.2 Mapping of the Environment 176