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
9.2.4.1 Path Length 317
9.2.4.2 Path Smoothness 318
9.2.4.3 Energy Consumption 318
9.2.4.4 The Safest Path 318
9.2.5 Optimization Problem Statement 319
9.3 Methodology 321
9.3.1 Framework of NSGA-II 321
9.3.2 Aensga-ii 322
9.3.2.1 Real-Coded Representation 322
9.3.2.2 Initialization Using Candidate Set Adaptive Random Testing (CSART) 323
9.3.2.3 Adaptive Crowding Distance (ACD) Strategy 324
9.3.2.4 Improved Binary Tournament Selection 326
9.3.3 Fuzzy Satisfactory Degree 327
9.3.4 Replanning Strategy Based on Sensory Vector 330
9.3.4.1 Sensory Vector Structure 330
9.3.4.2 Formulation of Gap Vector V g Based on COLREGs 333
9.3.4.3 Formulation of Transition Path 335
9.4 Results and Discussion 336
9.4.1 Convergence and Diversity Analysis 336
9.4.2 Implementation in Static Environment 342
9.4.2.1 Fixed Currents 342
9.4.2.2 Time-Varying Currents 346
9.4.3 Simulation Under Dynamic Environment 351
9.5 Conclusion 356
Acknowledgements 357
References 357
10 Coordinated Trajectory Planning for Multiple AUVs 363
10.1 Introduction 363
10.1.1 Background 363
10.1.2 Related Work 364
10.1.3 Contributions 366
10.2 Problem Model 367
10.2.1 Environment Model 367
10.2.2 AUV Model 369
10.2.3 Space and Time Constraint Model 370
10.2.4 Optimization Terms 371
10.2.5 Problem Statement 374
10.3 Solver Design 374
10.3.1 Brief States on Grey Wolf Optimizer 374
10.3.2 Parallel Grey Wolf Optimizer Design 376
10.4 Results and Discussion 379
10.4.1 Simulation 1: Allocation Task 380
10.4.2 Simulation 2: Rendezvous Task 381
10.5 Conclusion 385
Acknowledgements 385
References 386
11 Coverage Strategy for USV-Assisted Coastal Bathymetric Mapping 389
11.1 Introduction 390
11.2 Fundamental Models 394
11.2.1 Region of Interest 394
11.2.2 USV Model 395
11.3 Methodology 396
11.3.1 Coastal Line Approximation 396
11.3.2 Coverage Strategy 397
11.3.2.1 Trapezoidal Cellular Decomposition 397
11.3.2.2 Optimal Back and Forth Coverage Algorithm 398
11.3.2.3 Theoretical Analysis 402
11.3.3 Fuzzy-Biased Random Key Evolutionary Algorithm (FRKEA) 403
11.3.3.1 Chromosome Mapping 404
11.3.3.2 Evaluation in Real Space 405
11.3.3.3 Elitist Breeding 406
11.3.3.4 Mutating 407
11.3.3.5 Fuzzy Bias 409
11.4 Results and Discussion 411
11.4.1 Convergence Analysis 412
11.4.2 Simulation Study 414
11.4.2.1 Competitive Study 414
11.4.2.2 Parameter Analysis 417
11.4.3 Lake Trials 419
11.5 Conclusion 423
References 424
12 Energy-Efficient Coverage for USV-Assisted Bathymetric Survey Under Currents 429
12.1 Introduction 429
12.2 Methodology 433
12.2.1 Problem Models 433
12.2.1.1 Region of Interest 433
12.2.1.2 Current Model 433
12.2.1.3 USV Kinematics Under Currents 434
12.2.1.4 Energy Estimation 435
12.2.2 Coverage Strategy 436
12.3 Results and Discussion 440
12.3.1 Preparation 440
12.3.2 Analysis on Polygon Shapes 441
12.3.3 Analysis on Attacking Angle 444
12.3.4 Analysis on Different Coverage Strategy 445
12.3.5 Test on a Complex Concave ROI 447
12.4 Conclusion 454
References 455
13 Modeling and Solving Time-Sensitive Task Allocation for USVs with Mixed Capabilities 459
13.1 Introduction 459
13.2 Problem Formulation 463
13.2.1 Fundamental Models 463
13.2.1.1 USV Model 463
13.2.1.2 Target Model 464
13.2.2 Extended-Restriction Multiple Traveling Salesman Problem (ER-MTSP) 465
13.2.3 Problem Analysis 467
13.3 Methodology 468
13.3.1 Dual-Coded Chromosome Representation 468
13.3.2 Adaptive Random Testing Initialization 469
13.3.3 Hierarchical Crossover 469
13.3.4 Customized Mutation Strategy 472
13.3.5 Two-Phase Refinement Strategy 473
13.3.6 Linguistic Satisfactory Degree 475
13.4 Results and Discussion 477
13.4.1 Convergence and Diversity Analysis 477
13.4.2 Case Studies 480
13.4.3 Field Test 487
13.5 Conclusion 492
References 493
14 Joint Optimized Coverage Planning Framework for USV-Assisted Offshore Bathymetric Mapping: From Theory to Practice 497
14.1 Introduction 498
14.2 Problem Formulation 502
14.2.1 Definitions 502
14.2.2 Problem Statement 503
14.2.3 Theoretical Analysis 506
14.3 Methods for Problem Solving 507
14.3.1 Bisection-Based Convex Decomposition 507
14.3.2 Hierarchical Heuristic Optimization Algorithm 510
14.3.2.1 Order Generation 510
14.3.2.2 Candidate Pattern Finding 514
14.3.2.3 Tour Finding 518
14.3.2.4 Final Optimization 519
14.4 Results and Discussion 520
14.4.1 Validation in Simulation 520
14.4.2 Lake Experiments 526
14.5 Conclusion 530
Acknowledgements 530
Appendix 530
References 530
15 Pipe Segmentation and Geometric Reconstruction from Poorly Scanned Point Clouds Based on Deep Learning and BIM-Generated Data Alignment Strategies 535
15.1 Introduction 535
15.2 Related Studies 537
15.2.1 Pipe Segmentation 537
15.2.1.1 Descriptor-Based Methods 537
15.2.1.2 Learning-Based Methods 538
15.2.2 Dataset Preparation 538
15.2.3 Pipe Reconstruction 539
15.3 Methodology 539
15.3.1 BIM-Based Data Generating 540
15.3.2 Network Architecture 542
15.3.2.1 Overall Architecture 542
15.3.2.2 PipeSegNet Architecture 543
15.3.2.3 Feature Alignment Module 545
15.3.2.4. Label Alignment Module 546
15.3.2.5 Loss Function 547
15.3.3 Pipe Geometric Reconstruction 548
15.4 Experiment 552
15.4.1 Experimental Settings 552
15.4.2 Evaluation Metrics 555
15.4.3 Results and Discussion 556
15.5 Conclusion 563
Acknowledgment 564
References 564
16 The Arc Routing Path Planning Problem in the Maritime Domain 571
16.1 Introduction 571
16.2 The Arc Routing Path Planning Problem 575
16.2.1 Introduction to Arc Routing 575
16.2.2 Common Applications of Arc Routing 577
16.3 One Solution for Arc Problem: The Chinese Postman Problem 578
16.3.1 Basic Conception 578
16.3.2 Core Formulation 579
16.3.3 Variants of the Chinese Postman Problem 580
16.3.4 Algorithmic Approaches and Solution Methods 581
16.3.4.1 Polynomial-Time Solutions 581
16.3.4.2 NP-Hard Variants 582
16.4 Case Study 583
16.4.1 Background 583
16.4.2 Platform Design 584
16.4.3 Full Coverage Problem 586
16.4.3.1 Theoretical Formulation: Using the Chinese Postman Problem for Efficient Coverage 586
16.4.3.2 Coverage Path Generation 587
16.4.3.3 Discussion 588
16.5 Concluding Remarks 588
References 589
17 Atmospheric Scattering Model-Based Dataset for Maritime Object Detection with YOLOv 11 591
17.1 Introduction 591
17.2 Methodology 593
17.2.1 Physics-Based Fog Simulation Using Depth Estimation 593
17.2.1.1 MiDaS: Monocular Depth Estimation 593
17.2.1.2 Atmospheric Scattering Model 595
17.2.2 YOLOv 11 596
17.3 Experiment 598
17.3.1 Dataset 598
17.3.2 Foggy Dataset Generation and Model Training 599
17.3.2.1 Foggy Dataset Generation 599
17.3.2.2 Model Training 599
17.4 Result and Discussion 600
17.4.1 Baseline Training and Generalization Analysis 600
17.4.2 Improving Model Robustness with Mixed- Concentration Fog Training 601
17.4.3 Detection Result Comparison 604
17.5 Conclusion 610
References 611
18 Multisensor Perception and Data Fusion Technologies 613
18.1 Camera-Based Detection Approaches 614
18.1.1 RGB and Stereo Camera 614
18.1.2 Infrared and Thermal Camera 617
18.1.3 Object Detection Methodologies 618
18.2 LiDAR-Based Detection Approaches 620
18.2.1 Stages of Object Detection 621
18.2.2 Challenges and Resolutions 623
18.3 Data Fusion Methods 624
18.3.1 Radar 625
18.3.2 Fusion Level 626
18.3.3 Synchronization and Calibration 627
References 629
19 Route Planning for Low-Altitude UAV Using Multi-Objective Optimization 633
19.1 Introduction 634
19.2 Problem Model 636
19.3 Multi-Objective Particle Swarm Optimization 639
19.4 Results and Discussion 643
References 645
20 Autonomous System Design of Marine Vehicles 647
20.1 Introduction 647
20.2 Planning Module Design 649
20.2.1 Recursive Cell Decomposition Method 650
20.2.2 Optimal Path Generation 653
20.2.3 Guidance Planning: Adaptive Line-of-Sight (ALOS) Method 656
20.3 Control Module Design: USV Dynamics Modeling 657
20.4 Combined Navigation Module Design 661
References 663
Index 665
