Preface v 1 Introduction 1 1.1 Overview 1 1.2 System Structure 6 1.3 Mathematical Model of a USV 8 1.4 Maritime Applications 11 1.5 Motivation of this Book 13 References 13 2 Automatic Control Module 15 2.1 Origin and Development 16 2.2 Common Control System Development 17 2.

2.1 Dynamic Positioning and Position Mooring Systems 17 2.2.1.1 Dynamic Positioning Control System 18 2.2.1.2 Position Mooring Control System 22 2.

2.2 Waypoint Tracking and Path-Following Control Systems 24 2.2.2.1 Waypoint Tracking Control System 24 2.2.2.2 Path-Following Control System 26 2.

3 Advanced Control System Development 31 2.3.1 Linear Quadratic Optimal Control 31 2.3.2 State Feedback Linearization 36 2.3.2.1 Decoupling in the BODY Frame (Velocity Control) 36 2.

3.2.2 Decoupling in the NED Frame (Position and Attitude Control) 38 2.3.3 Integrator Backstepping Control 40 2.3.4 Sliding-Mode Control 45 2.3.

4.1 SISO Sliding-Mode Control 45 2.3.4.2 Sliding-Mode Control Using the Eigenvalue Decomposition 49 References 52 3 Perception and Sensing Module 57 3.1 Low-Pass and Notch Filtering 58 3.1.1 Low-Pass Filtering 58 3.

1.2 Cascaded Low-Pass and Notch Filtering 59 3.2 Fixed Gain Observer Design 60 3.2.1 Observability 60 3.2.2 Luenberger Observer 60 3.2.

3 Case Study: Luenberger Observer for Heading Autopilots Using Only Compass Measurements 61 3.3 Kalman Filter Design 61 3.3.1 Discrete-Time Kalman Filter 61 3.3.2 Continuous-Time Kalman Filter 62 3.3.3 Extended Kalman Filter 63 3.

3.4 Corrector-Predictor Representation for Nonlinear Observers 64 3.3.5 Case Study: Kalman Filter for Heading Autopilots Using Only Compass Measurements 64 3.3.5.1 Heading Sensors Overview 64 3.3.

5.2 System Model for Heading Autopilot Observer Design 65 3.3.6 Case Study: Kalman Filter for Dynamic Positioning Systems Using GNSS and Compass Measurements 66 3.4 Nonlinear Passive Observer Designs 67 3.4.1 Case Study: Passive Observer for Dynamic Positioning Using GNSS and Compass Measurements 67 3.4.

2 Case Study: Passive Observer for Heading Autopilots Using only Compass Measurements 68 3.4.3 Case Study: Passive Observer for Heading Autopilots Using Both Compass and Rate Measurements 71 3.5 Integration Filters for IMU and Global Navigation Satellite Systems 71 3.5.1 Integration Filter for Position and Linear Velocity 72 3.5.2 Accelerometer and Compass Aided Attitude Observer 73 3.

5.3 Attitude Observer Using Gravitational and Magnetic Field Directions 73 References 74 4 Model Predictive Control for Autonomous Marine Vehicles: A Review 75 4.1 Introduction 75 4.1.1 Object Introduction 75 4.1.2 Previous Reviews 77 4.2 Fundamental Models and a General Picture 85 4.

2.1 Model of AMVs 85 4.2.1.1 6-DOF Model 85 4.2.1.2 3-DOF Model 90 4.

2.2 Model Predictive Control 92 4.2.3 Literature Search 96 4.3 Methodology 99 4.3.1 MPC Applications of AMVs 99 4.3.

1.1 Real-Coded Chromosome 99 4.3.1.2 Path Following 101 4.3.1.3 Trajectory Tracking 104 4.

3.1.4 Cooperative Control/Formation Control 106 4.3.1.5 Collision Avoidance 108 4.3.1.

6 Energy Management 111 4.3.1.7 Other Topics 113 4.4 Discussion 114 4.4.1 Limitations of Existing Techniques and Challenges in Developing MPC 114 4.4.

1.1 Uncertainties of AMV Motion Models 114 4.4.1.2 Stability and Security of the New MPC Method 115 4.4.1.3 The Balance Between Effectiveness and Efficiency of the Methods 115 4.

4.1.4 The Practical Application Scenario of the MPC and the Discussion of the Working Conditions 116 4.4.1.5 Challenges Posed by the Marine Environment Affect MPC Development for AMVs 116 4.4.2 Trends in the Technology Development for MPC in AMV 117 4.

4.2.1 More Cooperative Control with MPC 117 4.4.2.2 Rigorous Theoretical Derivation and Experimental Verification 117 4.4.2.

3 Real-Time MPC for AMVs Applications 118 4.4.2.4 The Combination of Machine Learning/Neural Networks and MPC for AMVs Applications 118 4.4.2.5 Address the Challenges Posed by the Marine Environment 119 4.4.

2.6 Potential Interdisciplinary Approaches that Combine MPC with Other Innovative Fields 120 4.5 Conclusion 121 Acknowledgement 121 References 121 5 Controller-Consistent Path Planning for Unmanned Surface Vehicles 129 5.1 Introduction 129 5.2 Problem Formulation 131 5.3 Methodology 132 5.3.1 Improved Artificial Fish Swarm Algorithm 132 5.

3.1.1 Prey Behavior 133 5.3.1.2 Follow Behavior 135 5.3.1.

3 Swarm Behavior 135 5.3.1.4 Random Behavior 136 5.3.1.5 Adaptive Visual and Step 136 5.3.

2 Expanding Technique 138 5.3.3 Node Cutting and Path Smoother 139 5.3.4 Establishment of USV Model 141 5.4 Simulation 144 5.4.1 Monte Carlo Simulation 145 5.

4.2 Path Quality Test 146 5.4.3 Simulation Using USV Control Model in Practical Environment 149 5.5 Conclusion 151 References 152 6 Nonlinear Model Predictive Control and Routing for USV-Assisted Water Monitoring 155 6.1 Introduction 156 6.2 Problem Formulation 161 6.2.

1 Heterogeneous Global Path Planning Problem 161 6.2.1.1 USV Model 161 6.2.1.2 Task Model 162 6.2.

1.3 Problem Statement 162 6.2.2 Problem Analysis 164 6.2.3 Path Following Problem 164 6.2.3.

1 Basic Assumptions 165 6.2.3.2 Vessel Model 165 6.2.3.3 Problem Description 168 6.3 Methodology 169 6.

3.1 Greedy Partheno Genetic Algorithm 169 6.3.1.1 Dual-Coded Chromosome 170 6.3.1.2 Fitness Function 170 6.

3.1.3 Greedy Randomized Initialization 171 6.3.1.4 Local Exploration 172 6.3.1.

5 Mutation Operators 174 6.3.1.6 Algorithm Flow 175 6.3.2 Nonlinear Model Predictive Control 177 6.3.2.

1 State Space Model 177 6.3.2.2 NMPC Design 178 6.3.2.3 Solver 180 6.3.

2.4 Stability 181 6.4 Results and Discussion 181 6.4.1 Simulation: Global Task Planning 181 6.4.1.1 Convergence Test 181 6.

4.1.2 Heterogeneous Task Planning 185 6.4.2 Simulation: NMPC Control Performance 188 6.4.2.1 Test 1: Simulation Under Different Model Uncertainties 190 6.

4.2.2 Test 2: Comparative Study with Other Methods 192 6.4.3 Simulation Verification of the Framework 196 6.5 Conclusion 200 References 201 7 Global-Local Hierarchical Framework for USV Trajectory Planning 207 7.1 Introduction 207 7.2 Problem Formulation 212 7.

2.1 Marine Environment 212 7.2.2 Dynamic Obstacles 213 7.2.3 Effects of Currents 213 7.2.4 USV Model and Constraints 213 7.

2.5 Protocol Constraints 216 7.2.6 Objective Functions 217 7.2.6.1 The Minimum Cruising Time 217 7.2.

6.2 The Minimum Variation of Heading Angle 217 7.2.6.3 The Safest Path 218 7.2.7 Problem Statement 219 7.3 Methodology 221 7.

3.1 Adaptive-Elite GA with Fuzzy Inference (AEGAfi) 221 7.3.1.1 Real-Coded Chromosome 221 7.3.1.2 Initialization Based on Adaptive Random Testing (ART) 222 7.

3.1.3 Adaptive Elite Selection 223 7.3.1.4 Double-Functioned Crossover 224 7.3.1.

5 Mutation Operators 225 7.3.1.6 Fuzzy-Based Probability Choice 226 7.3.1.7 Fitness Function Design 227 7.3.

2 Replanning Strategy Based on Sensory Vector 229 7.3.2.1 Sensory Vector Structure 229 7.3.2.2 Formulation of V s 230 7.3.

2.3 Formulation of Gap Vector V g Based on COLREGs 232 7.3.2.4 Formulation of Transition Path 234 7.4 Simulation Study 236 7.4.1 Convergence Benchmark Analysis 236 7.

4.2 Simulation Under Static Environment 238 7.4.3 Simulation Under Time-Varying Environment 246 7.4.4 Simulation on Real-World Geography 251 7.5 Conclusion 254 Appendix 255 List of Abbreviations 255 Acknowledgements 256 References 256 8 Reinforcement Learning for USV-Assisted Wireless Data Harvesting 263 8.1 Introduction 263 8.

2 Fundamental Models 269 8.2.1 Environment Model 272 8.2.2 Sensor Node and Communication Model 273 8.2.3 USV Model 275 8.2.

3.1 Kinematic Model 275 8.2.3.2 Sensing Module 277 8.3 Methodology 278 8.3.1 Brief States on Q-Learning 278 8.

3.2 Interactive Learning 279 8.3.2.1 Heuristic Reward Design 279 8.3.2.2 Design of Value-Iterated Global Cost Matrix 279 8.

3.2.3 Local Cost Matrix and Path Generation 282 8.3.2.4 USV Actions with Discrete Precise Clothoid Path 283 8.3.3 Summary of the Path Planning Algorithm 286 8.

3.4 Time Complexity 287 8.4 Results and Discussion 288 8.4.1 Performance Indicators 288 8.4.2 Hyper-Parameter Analysis 290 8.4.

3 Comparative Study with State of the Art 294 8.5 Conclusion 298 Appendix 299 References 300 9 Achieving Optimal Dynamic Path Planning for Unmanned Surface Vehicles: A Rational Multi-Objective Approach and a Sensory-Vector Re-Planner 307 9.1 Introduction 308 9.2 Problem Formulation 314 9.2.1 Environment Modeling 315 9.2.1.

1 Motion Area 315 9.2.1.2 Effects of Currents 315 9.2.2 Dynamic Obstacles 316 9.2.3 Motion Constraints 317 9.

2.4 Objective Functions 317.