Contents
List of Figures x
Abstract 1
Acknowledgements 5
1 Introduction 7
1.1 The Asymmetric Threat Protection Problem . . . 7
1.2 The Robotic Approach . . . 9
1.3 Thesis Main Contributions . . . 11
1.3.1 Academy and Industry Collaboration . . . 12
1.4 Application Scenarios . . . 14
1.4.1 Military Scenario . . . 15
1.4.2 Civilian Scenario . . . 18
1.5 Collateral Research Topics . . . 21
1.5.1 Team Coordination Protocols for Environment Re-construction . . . 21
1.5.2 Distributed Indoor Navigation System for Multi– rotors Platforms . . . 24
1.5.3 Multi–object Handling for Robotic Manufacturing . 27 1.6 Thesis Organization . . . 28
2 Problem Formulation 31 2.1 Introduction . . . 31
CONTENTS CONTENTS
2.2 Formalization framework . . . 32
3 Distributed Cooperative Control 39 3.1 Introduction . . . 39
3.1.1 The Proposed Approach . . . 41
3.2 Search and Secure: State–of–the–art . . . 44
3.3 Cooperative Control Problem as Potential Game . . . 48
3.3.1 Potential Game . . . 49
3.4 Asymmetric Threats as a Strategic Game . . . 50
3.4.1 Constrained Potential Games . . . 50
3.4.2 Coverage Problem as Constrained Potential Game . 52 4 Game Theoretic Learning Algorithms 55 4.1 State–of–the–art Learning Algorithms . . . 56
4.1.1 The Structure of Learning Algorithm . . . 59
4.1.2 DISL Algorithm . . . 60
4.1.3 PIPIP Algorithm . . . 61
4.2 Homogeneous Algorithms . . . 64
4.2.1 Distributed Learning . . . 64
4.2.2 Applicability to the Intruder Tracking Problem . . . 68
4.3 Trajectory Algorithms . . . 69
4.3.1 Trajectory Algorithm: T–DISL . . . 71
4.3.2 Convergence Results . . . 73
4.3.3 Applicability to the Asymmetric Threat Problem . . 76
5 Serious Game Framework 77 5.1 NoStop Introduction . . . 77
5.2 System Requirements . . . 79
5.3 Software Architecture . . . 80
5.3.1 Coordination Protocol Architecture . . . 83
5.4 Hardware integration . . . 84
5.5 NoStop Evaluation and Validation . . . 85
5.5.1 Actions Selection Set . . . 86
5.5.2 Validation of Homogeneous Algorithm in Static En-vironments . . . 86
5.5.3 Simulations in Dynamic Environments . . . 90 5.5.4 Simulations of T –Algorithms in Static Environments 95
CONTENTS CONTENTS
5.5.5 Learning Algorithms in Dynamic Environments . . . 97
5.5.6 Team Sizing Tool . . . 103
5.6 Algorithms Performance Comparison . . . 103
5.7 Simulation Results Evaluation . . . 111
6 Real Robot Experiments 113 6.1 Distributed Collision Avoidance . . . 118
6.1.1 Introduction . . . 118
6.1.2 Problem Description . . . 120
6.1.3 The Multi–Robot Path Planning Approach . . . 123
6.1.4 Algorithm Properties . . . 131
6.1.5 Experiments and Results . . . 132
6.1.6 Conclusions . . . 134
6.2 Experimental Results . . . 136
6.2.1 Setup . . . 137
6.2.2 Results . . . 139
6.2.3 Conclusions . . . 146
7 Coordination protocol extension 147 7.1 Introduction . . . 147
7.2 Environmental Design . . . 148
7.3 Sensor Modelling . . . 151
7.4 Definition of New Utility Function . . . 156
7.5 Simulation Results . . . 160
7.5.1 Setup . . . 162
8 From Detection to Reaction 167 8.1 Introduction . . . 167
8.2 Problem Formulation . . . 169
8.2.1 Game Theoretic Framework . . . 171
8.3 Intruder Tracking and Herding Problem . . . 173
8.3.1 Virtual Barrier Construction and Orientation . . . . 174
8.3.2 Coordination Protocol . . . 176
8.3.3 Intruder Objectives Identification . . . 177
8.4 Simulation Results . . . 179
8.5 Conclusions . . . 183 v
CONTENTS CONTENTS 9 Conclusions and Future Works 185
9.1 Conclusions . . . 185
9.2 Future Works . . . 187
9.2.1 Applications to Real Scenarios . . . 187
9.2.2 Other Ways to Go . . . 187
Appendix 189
