Autonomous Marine Vehicles Planning and Control E-Book

Yong Bai

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 895

Veröffentlichungsjahr: 2025

Beschreibung

Master the future of marine exploration and technology with Autonomous Marine Vehicles Planning and Control, which provides a comprehensive, interdisciplinary guide to the principles, control, and real-world applications of autonomous marine vehicles.

Autonomous Marine Vehicles Planning and Control explores the intricate and rapidly evolving field of autonomous marine vehicles, focusing on unmanned surface vehicles (USVs) and autonomous underwater vehicles (AUVs). This book is designed to provide a comprehensive overview of the fundamental principles, advanced control methodologies, and practical applications of these autonomous systems in various marine environments. Through a series of detailed chapters, the book delves into the technical aspects, innovative algorithms, and real-world challenges associated with the deployment and operation of USVs and AUVs. Through a highly technical and research-oriented approach, each chapter combines theoretical analysis with practical case studies and simulation results to illustrate the effectiveness of the proposed methods. The book also addresses the interdisciplinary nature of the field, integrating concepts from robotics, artificial intelligence, and marine engineering to provide a holistic view of autonomous marine vehicle technology.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

1 Introduction

1.1 Overview

1.2 System Structure

1.3 Mathematical Model of a USV

1.4 Maritime Applications

1.5 Motivation of this Book

References

2 Automatic Control Module

2.1 Origin and Development

2.2 Common Control System Development

2.3 Advanced Control System Development

References

3 Perception and Sensing Module

3.1 Low-Pass and Notch Filtering

3.2 Fixed Gain Observer Design

3.3 Kalman Filter Design

3.4 Nonlinear Passive Observer Designs

3.5 Integration Filters for IMU and Global Navigation Satellite Systems

References

4 Model Predictive Control for Autonomous Marine Vehicles: A Review

4.1 Introduction

4.2 Fundamental Models and a General Picture

4.3 Methodology

4.4 Discussion

4.5 Conclusion

Acknowledgement

References

5 Controller-Consistent Path Planning for Unmanned Surface Vehicles

5.1 Introduction

5.2 Problem Formulation

5.3 Methodology

5.4 Simulation

5.5 Conclusion

References

6 Nonlinear Model Predictive Control and Routing for USV-Assisted Water Monitoring

6.1 Introduction

6.2 Problem Formulation

6.3 Methodology

6.4 Results and Discussion

6.5 Conclusion

References

7 Global-Local Hierarchical Framework for USV Trajectory Planning

7.1 Introduction

7.2 Problem Formulation

7.3 Methodology

7.4 Simulation Study

7.5 Conclusion

Appendix

List of Abbreviations

Acknowledgements

References

8 Reinforcement Learning for USV-Assisted Wireless Data Harvesting

8.1 Introduction

8.2 Fundamental Models

8.3 Methodology

8.4 Results and Discussion

8.5 Conclusion

Appendix

References

9 Achieving Optimal Dynamic Path Planning for Unmanned Surface Vehicles: A Rational Multi-Objective Approach and a Sensory-Vector Re-Planner

9.1 Introduction

9.2 Problem Formulation

9.3 Methodology

9.4 Results and Discussion

9.5 Conclusion

Acknowledgements

References

10 Coordinated Trajectory Planning for Multiple AUVs

10.1 Introduction

10.2 Problem Model

10.3 Solver Design

10.4 Results and Discussion

10.5 Conclusion

Acknowledgements

References

11 Coverage Strategy for USV-Assisted Coastal Bathymetric Mapping

11.1 Introduction

11.2 Fundamental Models

11.3 Methodology

11.4 Results and Discussion

11.5 Conclusion

References

12 Energy-Efficient Coverage for USV-Assisted Bathymetric Survey Under Currents

12.1 Introduction

12.2 Methodology

12.3 Results and Discussion

12.4 Conclusion

References

13 Modeling and Solving Time-Sensitive Task Allocation for USVs with Mixed Capabilities

13.1 Introduction

13.2 Problem Formulation

13.3 Methodology

13.4 Results and Discussion

13.5 Conclusion

References

14 Joint Optimized Coverage Planning Framework for USV-Assisted Offshore Bathymetric Mapping: From Theory to Practice

14.1 Introduction

14.2 Problem Formulation

14.3 Methods for Problem Solving

14.4 Results and Discussion

14.5 Conclusion

Acknowledgements

Appendix

References

15 Pipe Segmentation and Geometric Reconstruction from Poorly Scanned Point Clouds Based on Deep Learning and BIM-Generated Data Alignment Strategies

15.1 Introduction

15.2 Related Studies

15.3 Methodology

15.4 Experiment

15.5 Conclusion

Acknowledgment

References

16 The Arc Routing Path Planning Problem in the Maritime Domain

16.1 Introduction

16.2 The Arc Routing Path Planning Problem

16.3 One Solution for Arc Problem: The Chinese Postman Problem

16.4 Case Study

16.5 Concluding Remarks

References

17 Atmospheric Scattering Model-Based Dataset for Maritime Object Detection with YOLOv11

17.1 Introduction

17.2 Methodology

17.3 Experiment

17.4 Result and Discussion

17.5 Conclusion

References

18 Multisensor Perception and Data Fusion Technologies

18.1 Camera-Based Detection Approaches

18.2 LiDAR-Based Detection Approaches

18.3 Data Fusion Methods

References

19 Route Planning for Low-Altitude UAV Using Multi-Objective Optimization

19.1 Introduction

19.2 Problem Model

19.3 Multi-Objective Particle Swarm Optimization

19.4 Results and Discussion

References

20 Autonomous System Design of Marine Vehicles

20.1 Introduction

20.2 Planning Module Design

20.3 Control Module Design: USV Dynamics Modeling

20.4 Combined Navigation Module Design

References

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 4

Table 4.1 Some existing surveys/reviews involved MPC of AMVs.

Table 4.2 Summary of existing studies on MPC for AMVs.

Table 4.3 The notation of AMVs.

Table 4.4 Highly cited papers in each application.

Chapter 5

Table 5.1 Parameters of the USV model.

Table 5.2 Maneuvering derivatives of the USV model.

Table 5.3 Results of Monte Carlo simulation.

Table 5.4 Comparison of the path quality.

Chapter 6

Table 6.1 Setting of test instances.

Table 6.2 Comparison of time cost.

Table 6.3 Comparison of the solution quality.

Table 6.4 Design of EMTSPs.

Table 6.5 Parameters of the Otter.

Table 6.6 Maneuvering derivatives of the USV model.

Table 6.7 Evaluation indexes of control performance of NMPC.

Table 6.8 Evaluation indexes of control performance.

Chapter 7

Table 7.1 Fuzzy rules for probability selection.

Table 7.2 Probability set.

Table 7.3 Determination of

I

k

.

Table 7.4 Judgment of the encounter scenario.

Table 7.5 Statistical results.

Table 7.6 Maneuvering derivatives of the USV model.

Table 7.7 Quantitative results (Case 1).

Table 7.8 Quantitative results (Case 2).

Table 7.9 Setting of dynamic obstacles.

Table 7.10 Quantitative results of obstacle avoidance.

Table 7.11 Time spends on replanning and transition path.

Table 7.12 Setting of dynamic obstacles.

Table 7.13 Quantitative results of obstacle avoidance.

Table 7.14 Time spends on replanning and transition path.

Chapter 8

Table 8.1 Notations.

Table 8.2 Parameter analysis.

Table 8.3 Correlation analysis.

Table 8.4 Statistical measurements of Case 1.

Table 8.5 Statistical measurements of Case 2.

Chapter 9

Table 9.1 Summary of recent literature.

Table 9.2 Fuzzy rules.

Table 9.3 Judgment of the encounter scenario.

Table 9.4 HV and time scores on 12 low-dimensional MOPs. The symbols “+”, “_” ...

Table 9.5 Quantitative results of HV on 12 low-dimensional MOPs over 20 indepe...

Table 9.6 Quantitative results of HV on 14 high-dimensional MOPs over 20 indep...

Table 9.7 Calculation results.

Table 9.8 Statistic measurements of the solutions obtained by AENSGA-II.

Table 9.9 Statistic measurements of the solutions obtained by EPSO.

Table 9.10 Statistic measurements of the solutions obtained by NSGA-II.

Table 9.11 Calculation results.

Table 9.12 Statistic measurements of the solutions obtained by AENSGA-II.

Table 9.13 Statistic measurements of the solutions obtained by EPSO.

Table 9.14 Statistic measurements of the solutions obtained by NSGA-II.

Table 9.15 Maneuvering derivatives of the USV model.

Table 9.16 Setting of dynamic obstacles.

Table 9.17 Quantitative results of obstacle avoidance.

Table 9.18 Time spent on replanning and transition path.

Chapter 10

Table 10.1 Statistical results.

Table 10.2 Statistical measurements for each AUV.

Table 10.3 Statistical results.

Table 10.4 Statistical measurements for each AUV.

Chapter 11

Table 11.1 Fuzzy rules.

Table 11.2 Statistical results.

Table 11.3 Statistical measurements.

Table 11.4 Statistical measurements.

Chapter 12

Table 12.1 Comparison of different algorithms.

Table 12.2 Vertices of sub-regions.

Table 12.3 Optimal sweep direction for each region.

Table 12.4 Energy cost calculated from real ship model.

Chapter 13

Table 13.1 Summary of models.

Table 13.2 Linguistic satisfactory degree determined by fuzzy rules.

Table 13.3 Computational test conditions of 7 standard MTSPs.

Table 13.4 Design of ER-MTSPs.

Table 13.5 Workload for each USV in the fuzzy-selected solutions (nonemergency...

Table 13.6 Price achieved of the optimal solution given by FENSGA-II (emergenc...

Table 13.7 Workload for each USV in the optimal solution of FENSGA-II (emergen...

Table 13.8 Price achieved of the optimal solution given by θ-NSGA-III...

Table 13.9 Workload for each USV in the optimal solution of θ-NSGA-III...

Table 13.10 Main particulars of the USVs (provided by Hanpute, Co., Ltd).

Table 13.11 Experimental settings.

Table 13.12 Experimental results of nonemergency case.

Table 13.13 Experimental results of emergency case.

Chapter 14

Table 14.1 Comparative studies.

Table 14.2 Computational data to the large-scale survey task*.

Table 14.3 Experimental result comparison.

Chapter 15

Table 15.1 Number of points in datasets.

Table 15.2 Comparison of different segment models.

Table 15.3 Results comparison of different loss functions.

Table 15.4 Results comparison of different serialization conditions.

Table 15.5 Results comparison of different sampling points.

Table 15.6 Results comparison of different label conditions.

Table 15.7 The error of reconstructed pipes.

Chapter 17

Table 17.1 Distribution of different objects in the dataset.

Table 17.2 Evaluation metrics for model trained on clear images.

Table 17.3 Composition of the dataset.

Table 17.4 Evaluation metrics for model trained on mixed concentration fog ima...

Table 17.5 Parameters and inference time of each model.

Table 17.6 Evaluation metrics for YOLOv10n trained on mixed concentration fog ...

Table 17.7 Evaluation metrics for YOLOv9t trained on mixed concentration fog i...

List of Illustrations

Chapter 1

Figure 1.1 (a) UGV (Defense turkey), (b) UAV (Raima), (c) USV (Marit...

Figure 1.2 Fundamental questions for all kinds of unmanned vehicles.

Figure 1.3 Minesweeper vessels.

Figure 1.4 Typical modules for a USV (Fossen, 2011).

Figure 1.5 Reference frame (Fossen, 2011).

Figure 1.6 Typical applications.

Chapter 2

Figure 2.1 Common dynamic positioning system.

Figure 2.2 Common position mooring system.

Figure 2.3 The conventional path planning autopilot integrated with ...

Figure 2.4 Cascaded kinematic and yaw rate controller for path-follo...

Figure 2.5 Block diagram showing the linear quadratic regulator (LQR...

Figure 2.6 Nonlinear decoupling in the BODY frame.

Figure 2.7 Nonlinear decoupling in the NED frame with transformation...

Figure 2.8 Stabilization of the x1 system by means of the stabilizin...

Chapter 3

Figure 3.1 LP and notch filters in series with the control system.

Figure 3.2 Block diagram showing the nonlinear passive DP observer.

Figure 3.3 Nonlinear attitude observer-based directional measurement...

Chapter 4

Figure 4.1 6-DOF model for AMVs in body-fixed frame , Globe coordin...

Figure 4.2 The schematic diagram of model predictive controller.

Figure 4.3 The flow chart of MPC.

Figure 4.4 The schematic diagram of MPC.

Figure 4.5 Summary of articles searched. As studies using MPC were p...

Figure 4.6 Publications of research groups about MPC around the worl...

Figure 4.7 Controller architecture of nonlinear MPC controller under...

Figure 4.8 Block diagram of the proposed path following controller (...

Figure 4.9 Diagram of the double closed-loop controller (Yan

et al.

,...

Figure 4.10 Schematic diagram of the AUV leader-follower formation ...

Figure 4.11 The structure of FCAS for AMV (Sun

et al.

, 2019).

Figure 4.12 The block diagram of the control strategy in CAS.

Figure 4.13 Schematic diagram of the AUV leader-follower formation ...

Chapter 5

Figure 5.1 Coordinate system.

Figure 5.2 (a) Path generated by AFSA with unnecessary turnings; (b)...

Figure 5.3 (a) Prey behavior; (b) Swarm behavior; (c) Follow behavio...

Figure 5.4 Node cutting (Zhao

et al.

, 2022).

Figure 5.5 System structure of the USV model.

Figure 5.6 Scheme of the proposed method.

Figure 5.7 (a) Satellite map of Qizhen Lake in Zhejiang University; ...

Figure 5.8 Visualized simulation results.

Figure 5.9 Definition of the path segments.

Figure 5.10 (a) USV track the trajectory presented by proposed meth...

Figure 5.11 Tracking results for proposed method: (a) Course angle ...

Figure 5.12 Tracking results for Zhao

et al.

(2022): (a) Course ang...

Figure 5.13 Tracking results for Yao

et al.

(2021): (a) Course angl...

Chapter 6

Figure 6.1 Illustration of a typical monitoring mission.

Figure 6.2 Geometry of the coordinate system.

Figure 6.3 Framework of the proposed method.

Figure 6.4 Chromosome representation.

Figure 6.5 Example of local exploration.

Figure 6.6 Mutation operators.

Figure 6.7 Flowchart of GPGA.

Figure 6.8 Box-whisker plot of time cost.

Figure 6.9 Box-whisker plot of total distance.

Figure 6.10 Task distribution (a) Case 1; (b) Case 2; (c) Case 3; (...

Figure 6.11 Convergence history: the time costs for Cases 1–6 are 8...

Figure 6.12 Planning results.

Figure 6.13 USV Otter.

Figure 6.14 Environmental disturbances.

Figure 6.15 Path tracking under different model uncertainties.

Figure 6.16 Angle and velocity profile (a) Nominal model; (b) 10% m...

Figure 6.17 Tracking results.

Figure 6.18 Angle and velocity profile (a) NMPC; (b) ALOS; (c) ILOS...

Figure 6.19 Environment set.

Figure 6.20 Results of the global planning: (a) Path generation; (b...

Figure 6.21 Results of the waypoint following: (a) USV trajectories...

Figure 6.22 Results of the tracking process: (a) Course angle and s...

Chapter 7

Figure 7.1 (a) Coordinate system; (b) definition of a path curve.

Figure 7.2 Chromosome representation.

Figure 7.3 Mutation operators: (a) Arithmetic mutation, (b) heuristi...

Figure 7.4 Membership function.

Figure 7.5 Flowchart of AEGAfi.

Figure 7.6 Sensory structure.

Figure 7.7 (a) Example of CPA position; (b) angle definition.

Figure 7.8 Encounter scenario.

Figure 7.9 (a) Illustration of transition path and replanning path; ...

Figure 7.10 The hierarchical framework.

Figure 7.11 Comparison of the time consumption for each case.

Figure 7.12 Comparison of the solution quality for each case, (a) C...

Figure 7.13 Visualized results.

Figure 7.14 Box-whisker plot of the convergence time for (a) Case 1...

Figure 7.15 Simulation results in static environment (Case 1): (a) ...

Figure 7.16 Path comparison (Case 1).

Figure 7.17 Simulation results in static environment (Case 2): (a) ...

Figure 7.18 Path comparison (Case 2).

Figure 7.19 Relative distance of (a) Case 3, (b) Case 4.

Figure 7.20 Visualized trajectory of Case 3.

Figure 7.21 Visualized trajectory of Case 4.

Figure 7.22 Course angle and speed during (a) Case 3, (b) Case 4.

Figure 7.23 Simulation site: an artificial lake in Zhejiang Univers...

Figure 7.24 Visualized USV’s trajectory (a) avoiding static obstacl...

Figure 7.25 Simulation results (a) course angle and speed; (b) rela...

Chapter 8

Figure 8.1 Data collection process.

Figure 8.2 USVs perform ocean data collection.

Figure 8.3 (a) Example environment setting (blue contour is the obst...

Figure 8.4 Obstacle detected in the initial iterations (a) USV1; (b)...

Figure 8.5 Illustrative example of convergence of matrix.

Figure 8.6 (a) Path geometry, (b) USV actions at each iteration.

Figure 8.7 Illustration of the collision risk.

Figure 8.8 Environment set, (a) Case 1, (b) Case 2.

Figure 8.9 Path planning process in each episode for Case 1.

Figure 8.10 Path planning process in each episode for Case 2.

Figure 8.11 Visualized results of Case 1 (a) Proposed, (b) IAFSA; (...

Figure 8.12 Visualized results of Case 2 (a) Proposed, (b) IAFSA; (...

Chapter 9

Figure 9.1 (a) Coordinate system; (b) definition of a path curve.

Figure 9.2 Chromosome representation.

Figure 9.3 CSART initialization (a) step 1; (b) step 2; (c) step 3; ...

Figure 9.4 (a) Traditional CD; (b) DCD.

Figure 9.5 Flowchart of AENSGA-II.

Figure 9.6 Membership function: (a) path length (normalized); (b) sm...

Figure 9.7 Formulation of

V

s

: (a) initialize

V

s

; (b) calculate TCPA&...

Figure 9.8 (a) Initialization; (b) encounter judgment; (c) formulate...

Figure 9.9 Hierarchical framework of the proposed method.

Figure 9.10 Box-whisker plot of time cost on 12 low-dimensional MOP...

Figure 9.11 Solutions (a) AENSGA-II (Solutions number is not given ...

Figure 9.12 (a) Scaled measures of 11 solutions obtained by AENSGA-...

Figure 9.13 Solution (a) AENSGA-II (Solution number is not given in...

Figure 9.14 (a) Scaled measures of 10 solutions obtained by AENSGA-...

Figure 9.15 Relative distance of (a) case 1, (b) case 2.

Figure 9.16 Visualized trajectory of case 1.

Figure 9.17 Visualized trajectory of case 2.

Figure 9.18 Profile for (a) course angle and speed in case 1; (b) t...

Chapter 10

Figure 10.1 Top view of the planning space.

Figure 10.2 Illustration of path crossing the threat area.

Figure 10.3 Hierarchy of grey wolf (dominance decreases from top).

Figure 10.4 Flowchart of the algorithm.

Figure 10.5 Top view of the task type.

Figure 10.6 (a) Time cost for each method; (b) Convergence history.

Figure 10.7 (a) 3D view of P-GWO; (b) 2D view of P-GWO; (c) 3D view...

Figure 10.8 (a) Time cost for each method; (b) Convergence history.

Figure 10.9 (a) 3D view of P-GWO; (b) 2D view of P-GWO; (c) 3D view...

Chapter 11

Figure 11.1 Core elements (a) ROI and cells; (b) Sonar coverage.

Figure 11.2 (a) Douglas-Peucker algorithm; (b) Proposed CPDP algori...

Figure 11.3 (a) Definitions of elements; (b) Coverage strategy.

Figure 11.5 Breeding.

Figure 11.6 (a) Mapping definition; (b) Population distribution.

Figure 11.7 Membership function.

Figure 11.8 Flowchart.

Figure 11.9 Boxplot of the convergence time (a) eil51; (b) berlin52...

Figure 11.10 Coverage paths for Case 1 and Case 2.

Figure 11.11 Coverage paths for Case 3 and Case 4.

Figure 11.12 Coverage paths with different detection range or safe...

Figure 11.13 Hardware set.

Figure 11.14 USV is surveying (a) Lake trial No. 1; (b) Lake trial...

Figure 11.15 Lake trial No. 1 (a) Planned waypoints; (b) Experimen...

Figure 11.16 Lake trial No. 2 (a) Planned waypoints; (b) Experimen...

Figure 11.17 Lake trial No. 1 (a) Position; (b) Roll angle; (c) Ya...

Figure 11.18 Lake trial No. 2 (a) Position; (b) Roll angle; (c) Ya...

Chapter 12

Figure 12.1 Definition of sweep direction

β.

Figure 12.2 (a) Definitions of elements; (b) splitting the ROI; (c)...

Figure 12.3 Results on a triangle region (a) optimal sweep directio...

Figure 12.4 Results on a square region (a) optimal sweep direction ...

Figure 12.5 Results on a parallelogram region (a) optimal sweep dir...

Figure 12.6 Results on a pentagon region (a) optimal sweep directio...

Figure 12.7 Results on a hexagon region (a) optimal sweep direction...

Figure 12.8

E-β

curves for (a) triangle; (b) square; (c) par...

Figure 12.9

E-β

curves for (a) pentagon; (b) hexagon.

Figure 12.10

E-β

curves for different algorithms.

Figure 12.11 Region of interest in Qizhen Lake.

Figure 12.12 Convex sub-regions after decomposition.

Figure 12.13 Coverage paths generated by (a) proposed model; (b) t...

Figure 12.14 USV prototype: Otter.

Figure 12.15 Trajectory of the USV (a) sweep pattern using propose...

Figure 12.16 Profile of the USV (a) angle profile of proposed patt...

Figure 12.17 (a) and (b) forces generated by port and starboard th...

Chapter 13

Figure 13.1 Chromosome representation.

Figure 13.2 Hierarchical crossover procedure.

Figure 13.3 Phase I.

Figure 13.4 Phase II.

Figure 13.5 Flowchart of the proposed combinatorial multi-objective...

Figure 13.6 Membership functions of the objectives and satisfactory...

Figure 13.7 Pareto front of (a) berlin52, N

U

=5; (b) berlin52, N

U

=7;...

Figure 13.8 Pareto front of (a) eil76, N

U

=5; (b) eil76, N

U

=7; (c) p...

Figure 13.9 Case 2 (a) pareto front; (b) solution with least cost; ...

Figure 13.10 Workload distribution of cases 1, 3, and 5.

Figure 13.11 Workload distribution of case 2, case 4, and case 6.

Figure 13.12 Task accessed for each algorithm.

Figure 13.13 Price collection for each algorithm.

Figure 13.14 Environment set (a) Qizhen Lake; (b) satellite map of...

Figure 13.15 USVs (a) structure components; (b) system structure.

Figure 13.16 Task assignment results of nonemergency case.

Figure 13.17 Experimental results of nonemergency case (a) traject...

Figure 13.18 Experimental results of emergency case (a) trajectori...

Figure 13.19 Comparison of time cost.

Chapter 14

Figure 14.1 Proposed framework.

Figure 14.2 Back-and-forth paths for convex region.

Figure 14.3 Four coverage patterns for one pair of LOS, (a–d) examp...

Figure 14.4 (a–d) Four LOS pairs for a trapezoid, (e–g) For a hexag...

Figure 14.5 (a) Optimal connection between A and B, (b) non-optimal...

Figure 14.6 Testing cases (a) Case 1, (b) Case 2, (c) Case 3.

Figure 14.7 Simulation platform.

Figure 14.8 Simulation results, (a) Case 1 (proposed), (b) Case 1 (...

Figure 14.9 USV system structure.

Figure 14.10 (a) ROI definition, (b) Routes designed by proposed m...

Figure 14.11 Experiment recording (a) Ground station setting, (b) ...

Figure 14.12 Experimental tracking logs of routes by proposed mode...

Figure 14.13 Experimental tracking logs of routes by ZHAO-TORRES, ...

Chapter 15

Figure 15.1 The overall research framework.

Figure 15.2 The process of data generating from BIM.

Figure 15.3 Decomposition and new label.

Figure 15.4 The examples of triangle meshes, sampled point clouds a...

Figure 15.5 The overall architecture of the segmentation process.

Figure 15.6 The architecture of PipeSegNet.

Figure 15.7 Point cloud serialization. (a) Hilbert (b) Z-order (c) ...

Figure 15.8 Block illustration.

Figure 15.9 The label alignment module.

Figure 15.10 The process of pipe geometric reconstruction.

Figure 15.11 Real and BIM-generated datasets.

Figure 15.12 Number of points in training and testing sets.

Figure 15.13 IoU comparison of different conditions.

Figure 15.14 Example area of geometric reconstruction.

Figure 15.15 Error distribution histograms of reconstructed pipes.

Figure 15.16 Comparison of original pipe point clouds and reconstr...

Chapter 16

Figure 16.1 It is common practice to use drones for inspections bet...

Figure 16.2 Abstracting the marine environment as a topological map...

Figure 16.3 The algorithmic approach to the problem of Chinese post...

Figure 16.4 Software interface display. Users can customize waypoin...

Figure 16.5 Sketch map of Boustrophedon cellular decomposition (BCD...

Chapter 17

Figure 17.1 (a) YOLOV11 network architecture; (b) C32K module; (c) ...

Figure 17.2 Example images in the FoggySea dataset. The first row i...

Figure 17.3 Results of models trained on clear images. (a) Detectio...

Figure 17.4 Results of models trained on mixed concentration fog im...

Figure 17.5 Detection performance comparison of YOLOv11n, YOLOv10n,...

Chapter 18

Figure 18.1 RGB camera qualities,

Figure 18.2 Stereocamera qualities,

Figure 18.3 Thermal camera qualities,

Figure 18.4 LiDAR qualities,

Figure 18.5 Synchronization diagram using GPS-based pulse per secon...

Chapter 19

Figure 19.1 Route planning for UAV in 2D.

Figure 19.2 Case 1 (a) 3D view, (b) 2D view.

Figure 19.3 Case 2 (a) 3D view, (b) 2D view.

Chapter 20

Figure 20.1 Overall process.

Figure 20.2 Process of recursive cell decomposition method.

Figure 20.3 Possible decomposition modes.

Figure 20.4 Decomposition of the real scene.

Figure 20.5 Process of optimal path generation.

Figure 20.6 Pseudo-code of optimal path generation.

Figure 20.7 Diagram of adaptive line-of-sight (ALOS) method.

Figure 20.8 Controller build in simulink.

Figure 20.9 GNSS antenna.

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

Index

Also of Interest

Wiley End User License Agreement

Pages

ii

iii

iv

xix

xx

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

574

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

591

592

593

594

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

633

634

635

636

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

653

654

655

656

657

658

659

660

661

662

663

665

666

667

668

669

670

671

672

673

Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Autonomous Marine Vehicles Planning and Control

Yong Bai

and

Liang Zhao

This edition first published 2026 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2026 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

Wiley Global Headquarters111 River Street, Hoboken, NJ 07030, USA

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

The manufacturer’s authorized representative according to the EU General Product Safety Regulation is Wiley-VCH GmbH, Boschstr. 12, 69469 Weinheim, Germany, e-mail: [email protected].

Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant-ability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.

Library of Congress Cataloging-in-Publication Data

ISBN 9781394355044

Cover image: Generated with AI using Adobe FireflyCover design by Russell Richardson

Preface

The arrival of autonomous vehicles, representing one of the most transformative technological achievements of the 21st century, is now reshaping industries ranging from transportation and logistics to environmental monitoring and defense. This book, Autonomous Marine Vehicles Planning and Control, is the culmination of five years of dedicated research by the authors in the field of robotics, with a primary focus on the challenges and innovations specific to marine domain. Our work is driven by a singular goal: to bridge the gap between theoretical advancements in autonomous systems and their practical implementation in real-world marine environments. It is this unique intersection of theory, engineering, and environmental adaptability that forms the core of our exploration.

The book is organized to provide a comprehensive yet accessible guide to the key components of autonomous marine vehicle design, with an emphasis on path planning and motion control. The first chapters lay the theoretical groundwork, introducing fundamental concepts such as kinematic and dynamic modeling of USVs, sensor fusion, and environmental perception. Subsequent chapters delve into advanced topics, including: Planning and control techniques for navigation, bathymetry, and water monitoring (Chapter 4-14). Techniques such as model predictive control (MPC) for underactuated and over-actuated marine systems (Chapter 2 and Chapter 6). Frameworks for multi-vehicle coordination (Chapter 10). Case studies and experimental results from field trials (Chapter 15 and Chapter 20). Potential applications to areas including UAVs (Chapter 19).

This book is designed to serve as both a scientific resource and a practical handbook. It caters to:

Researchers and Academics

: Graduate students and professors in robotics, control systems, and marine engineering will find the theoretical depth and literature reviews invaluable for advancing their own work.

Industry Professionals

: Engineers and developers in the maritime sector, including those working on offshore energy, environmental monitoring, and defense systems, will benefit from the actionable insights and algorithm implementations.

No scientific work is accomplished in isolation, and this book is no exception. We extend our deepest gratitude to the colleagues and collaborators who contributed to this work:

Mr. Xin Liu, whose expertise in automatic control helped to write

Chapters 2

and

Chapter 4

.

Ms. Zihan Yang contributes her insights into

Chapter 16

and

Chapter 17

.

Ms. Tian Xu, for her groundbreaking work on sensor fusion and perception, detailed in

Chapter 3

and

Chapter 18

.

Mr. Wanchen Yu, whose simulations and software design (

Chapter 20

) provided critical validation of our theoretical models.

The rapid evolution of autonomous systems demands continuous innovation. As we write this preface, new challenges—such as the integration of artificial intelligence for predictive navigation and the ethical implications of marine autonomy—are already shaping the next generation of research. It is our hope that this book not only equips readers with the tools to tackle current problems but also inspires them to ask new questions and push the boundaries of what is possible.

To the reader: Whether you are a student struggling on your first research project, an engineer refining your industrial product or a policymaker advocating for sustainable ocean technologies, we invite you to explore these pages with curiosity. The future of autonomy is vast and uncharted, and together, we have the opportunity to navigate it with precision, responsibility, and vision.

Yong Bai and Liang Zhao

Zhejiang University, Hangzhou, China

1Introduction

Abstract

Unmanned surface vehicles (USVs) have emerged as transformative tools in modern maritime applications, offering autonomous operation, enhanced efficiency, and expanded mission capabilities. This book provides a comprehensive overview of the planning and control mechanisms that underpin USV development. It begins with a historical review of unmanned vehicle evolution, highlighting key milestones from early self-propelled devices to modern AI-integrated systems. USVs, in particular, are equipped with advanced propulsion, navigation, communication, and control systems, enabling operations in complex and hazardous environments. Their core functionality is governed by a guidance-navigation-control (GNC) architecture that integrates nonlinear maneuvering models, sensor fusion, and motion control algorithms. Practical applications include bathymetric surveys, data harvesting, shipping logistics, and search and rescue missions, where USVs offer safer, more cost-effective alternatives to manned operations. The book also presents a simplified 3-DOF mathematical model for horizontal-plane maneuvering under environmental disturbances such as waves and currents. Emphasis is placed on the need for intelligent planning algorithms to meet the growing demands of modern engineering tasks. Through detailed system analysis and application-driven research, this work aims to advance the intelligence and operational versatility of USVs, supporting their continued integration into scientific, commercial, and defense domains.

Keywords: Unmanned surface vehicles (USVs), autonomous navigation, guidance-navigation-control (GNC), marine applications, path planning and control

1.1 Overview

Unmanned vehicles (Gage, 1995), encompassing aerial, ground, surface, and underwater platforms (see Figure 1.1), have emerged as pivotal technologies in the 21st century, revolutionizing numerous sectors (Finn and Scheding, 2010; Verfuss et al., 2019). Their significance lies in their ability to perform tasks that are dangerous, repetitive, or otherwise infeasible for humans, thus enhancing efficiency, safety, and capabilities across various domains. In the realm of aerial vehicles, unmanned aerial systems (UAS) or drones have transformed industries such as agriculture, logistics, surveillance, and environmental monitoring, offering unprecedented access to remote or hazardous areas (Deng et al., 2022; Huang et al., 2023; Shao et al., 2022). Ground unmanned vehicles (UGVs) are pivotal in applications ranging from military operations and search-and-rescue missions to automated farming and mining, providing robust solutions in challenging terrains and conditions (Asadi et al., 2020; Tokekar et al., 2016). Unmanned surface vehicles (USVs) and underwater vehicles (UUVs) extend human reach and capability beneath and on the water surface (Liu et al., 2024). These vehicles are instrumental in marine research, environmental monitoring, offshore oil and gas exploration, and naval operations (Abdullah et al., 2023). By enabling detailed and continuous monitoring of oceanographic parameters, underwater vehicles contribute significantly to our understanding of marine ecosystems and climate change impacts. Unmanned surface vehicles enhance maritime security, survey and mapping efforts, and disaster response capabilities (Ai et al., 2021; Baum-Talmor and Kitada, 2022; Yao et al., 2021).

Figure 1.1 (a) UGV (Defense turkey), (b) UAV (Raima), (c) USV (Maritime Robotics Ltd.), (d) AUV (REMUS).

The integration of advanced technologies such as artificial intelligence, machine learning, and sophisticated sensor arrays further augments the autonomy and functionality of unmanned vehicles (see the fundamental questions (Durrant-Whyte, 1994) for autonomy in Figure 1.2), positioning them at the forefront of modern innovation. As these systems continue to evolve, their role in shaping a safer, more efficient, and data-driven world becomes increasingly profound, marking a significant shift in how we approach complex challenges and harness technological advancements for societal benefit.

Uninhabited (or unmanned) surface vehicles are finding ever increasing applications in today’s world, and are being developed by numerous organizations (Bingham et al., 2010; Sharma and Sutton, 2012). USVs enable the ocean monitoring to go far beneath the ocean surface, collect diverse first-hand data, and see how the oceans behave (Karimi and Lu, 2021). Clearly, the manned marine technology was firstly focused. Since 1962 when the first submarine was constructed (Motwani, 2012), dramatic progress has been made in the design and manufacturing of manned marine vehicles. However, the intrinsic weakness of reliance on human pilots limits its applications. In contrast, advances in navigation, control, computer, sensor, and communication technologies have turned the idea of USV into reality.

The concept of unmanned vehicles is far from modern: as far back as 425 BC, the Greek mathematician Archytas of Tarentum is believed to have constructed a wooden dove he called “pigeon”. Propelled by a jet of steam or compressed air, it was capable of flying up to 200 m. His invention is often regarded as the first self-propelled machine or flying robot bird. The earliest self-propelled vehicle with an onboard control system was probably a torpedo developed by the British engineer Robert Whitehead in the 1860s. A self-regulating mechanism kept the torpedo at a constant preset depth: a hydrostatic valve and pendulum balance were connected to a horizontal rudder to control the running depth of the torpedo. Later on, Whitehead introduced Ludwig Obry’s newly invented gyroscope mechanism for azimuth control, thus incorporating gyroscopic stabilization to fix the torpedo’s direction.

Figure 1.2 Fundamental questions for all kinds of unmanned vehicles.

However, the first remotely-controlled vehicle seems to have been invented by Nikola Tesla, the inventor of AC power and responsible for many of the first breakthroughs in radio, radar, and energy fields. In 1898, he designed and built a pair of radio-controlled boats, constructed of iron and powered by a battery of his own design. Tesla ingeniously constructed a radio-control mechanism. It worked as follows: to register the arrival of a radio signal pulse, Tesla had invented a new kind of coherer, or radio-activated switch. It essentially consisted of a canister with some metal-oxide powder which oriented itself in the presence of an electromagnetic field, like that produced by radio waves, becoming conductive and completing an electrical circuit. When this happened, that is, when the coherer conducted, a geared mechanism near the stem advanced a disk bearing several sets of meticulously organized contacts by one step, which in turn would activate, with the aid of levers, gears, springs, and motors, a particular circuit combination on the boat. This would have the effect of advancing the “state” of the system by one, so for example, if the previously connected combination’s state assigned the rudder to be turned to the left, the propeller to be turning at full speed, and the lights to be switched off, the next step might specify a centered rudder, stopped propeller, and lights-on combination. The coherer canister would then be flipped over, and the metal oxide powder restored to a random, nonconductive state, awaiting the next radio signal.

Though radio-control was further developed during the First and Second World Wars (Soviet teletanks, the British QueenBee target-drone radio-controlled aircraft, Gennan radio-controlled missiles and, later on, FL-Boote radio-controlled motor boats filled with explosives to attack enemy shipping), radio-control technology mostly remained stagnant. That is, up until the latter half of the twentieth century, which saw the advent of solid-state electronics, the start of the Space Age (with the launch of Sputnik in 1957), and the frenzied race that followed to deliver satellites (all of which are radio-controlled) into orbit.

The development and operation of navy USVs has been going on since the Second World War, but these have mostly consisted of simple, radio-controlled drone boats used for battle/bomb damage assessment, target practice for manned vessels, and as tools for dangerous mine clearance operations, see Figure 1.3. For instance, during Operation Crossroads in 1946, drone boats were sent to collect samples of radioactive water after the atomic bomb blast tests on Bikini Atoll. Post-war Britain saw the need for fast target craft, and so, many military vessels that were no longer needed for war missions were converted to radio control. For example, the RAF converted four of their 68ft High Speed Launch (HSL) vessels to Remote Controlled Target Launches (LTRCs) in 1949. The HSLs, built by the British Power Boat Company during the war but which closed down at its end, were given to Vosper Ltd for their modification. This involved removing the turrets, guns, and other superstructure aft of the bridge and replacing the resulting bare deck section with am1our plating to withstand vertical attacks from 251b break-up bombs dropped from a height of 25,000 feet, and upon which was installed a large radar reflector with a large lit up “T”. The remote control box was a simplistic device developed by inventor P.F. Parfitt, with five push buttons that enabled the operator to start and stop the engines, open and close the throttles, and tum on the launches lights.

Figure 1.3 Minesweeper vessels.

In summary, USVs hold significant promise for the future of marine applications, revolutionizing the way we explore, monitor, and utilize our oceans. These autonomous vessels offer unparalleled efficiency and safety, capable of performing tasks that are dangerous or impractical for manned vessels. In environmental monitoring, USVs can continuously collect data on ocean conditions, pollution levels, and marine life, providing valuable insights for climate research and conservation efforts. Their ability to operate in hazardous conditions, such as during storms or in contaminated waters, makes them indispensable for disaster response and oil spill clean-ups. In the realm of maritime security, USVs can conduct surveillance and patrol missions, enhancing the protection of critical infrastructure and national waters without risking human lives. Furthermore, in commercial sectors, USVs can optimize shipping routes, inspect underwater infrastructure, and assist in offshore oil and gas operations, reducing costs and improving operational efficiency. As technology advances, USVs are expected to become more versatile and autonomous, further expanding their applications and making them a cornerstone of future marine operations.

1.2 System Structure

The system structure of a USV is a sophisticated integration of various subsystems, each designed to perform specific functions that collectively enable autonomous operation and mission execution. At the heart of the USV is the central processing unit (CPU), which acts as the brain, running the software that coordinates all activities. This CPU interfaces with the Guidance, Navigation, and Control (GNC) system, which includes sensors such as GPS, inertial measurement units (IMUs), and other navigational aids for real-time location tracking and motion control (Fossen, 2022). The propulsion subsystem comprises engines or motors, along with steering mechanisms like rudders or thrusters that enable movement and maneuverability. The communication subsystem ensures seamless data exchange between the USV and its remote-control center via satellite, radio, or cellular networks, facilitating remote monitoring and control. Additionally, the safety and fail-safe mechanisms are embedded within the system structure to handle unexpected situations and ensure the USV can return to a safe state if any component fails. The overall architecture is modular, allowing for easy upgrades and maintenance, ensuring the USV can be adapted to various missions and technological advancements. This structured approach ensures that all components work in harmony, providing the reliability and flexibility needed for diverse and demanding maritime operations.

USVs are equipped with a variety of hardware components essential for their autonomous operation and mission-specific tasks. At the core of a USV is its propulsion system, which can include traditional internal combustion engines, electric motors, or hybrid systems, each chosen based on the mission requirements and endurance needs. Navigation and control systems are integral, featuring GPS for precise location tracking, gyroscopes, accelerometers, and magnetometers for maintaining stability and orientation. Communication hardware, such as satellite links, radio transmitters, and antennas, ensures reliable data transmission between the USV and its control center. Additionally, USVs are outfitted with various sensors tailored to their specific missions, including sonar for underwater mapping, LIDAR for surface scanning, and cameras for visual inspections. Power management systems, often supported by batteries, solar panels, or fuel cells, ensure sustained operations. These components collectively enable USVs to perform complex tasks autonomously, ranging from environmental monitoring and maritime surveillance to search and rescue operations.

For software architecture, the GNC system is the cornerstone of a USV (see Figure 1.4), ensuring its autonomous and precise operation. It comprises four key modules: the Ship Nonlinear Maneuvering Model, Navigation Module, Guidance Module, and Control Module. The Ship Nonlinear Maneuvering Model simulates the kinematics and dynamics of the ship during motion, accounting for rigid body kinetics, hydrodynamics, propulsion, and environmental disturbances. This module accurately models ship movement on the water surface by considering nonlinear effects such as added mass, Coriolis effects, water damping, drifting, and water current interactions. The Navigation Module estimates the ship’s position, velocity, and course during navigation by integrating measurement data from the Global Navigation Satellite System (GNSS), inertial measurement units (IMUs), and gyros. The data is processed by a state estimator in the navigation computer to filter noise, predict, and reconstruct unmeasured states. The Guidance Module continuously computes the desired position, velocity, and acceleration signals, which are then transferred to the low-level motion control system. Finally, the Control Module receives these desired signals and calculates control actions to steer the ship, adjusting inputs using actuators like rudders and thrusters to ensure precise navigation and adherence to planned waypoints.

Figure 1.4 Typical modules for a USV (Fossen, 2011).

1.3 Mathematical Model of a USV

The mathematical model of a ship moving on the water surface can be formulated as motion equations in six degrees of freedom (6-DOF) under environmental disturbances including winds, currents, and waves (Fossen, 2011).

First, we consider two reference frames to describe the motions of a ship. The first one is the North-East-Down (NED) coordinate system {n} = (xn, yn, zn) with origin on defined relative to the Earth’s reference ellipsoid, see Figure 1.5. The other one is the body-fixed reference frame {b} = (xb,yb,zb) with origin ob that is fixed to the center of gravity of the ship. The position and orientation of the ship are described relative to the inertial reference frame (approximated by {n}) while the linear and angular velocities of the ship should be expressed in {b}.

Ship models are usually reduced-order models for stabilization of the horizontal plane motions including surge, sway, and yaw. When designing the model for navigating a ship, the following assumptions and definitions are considered:

Assumption 1. The inertia, added mass, and damping matrices are diagonal.

The 6 DOF nonlinear equations of motion, in their most general representation, require that a large number of hydrodynamic derivatives. However, the number of parameters can be drastically reduced by using Assumption 1. This is valid for ships with three planes of symmetry, and simplifies the 6-DOF model by neglecting the coupling effects between surge, sway, and yaw with heave, roll, and pitch.

Figure 1.5 Reference frame (Fossen, 2011).

Assumption 2. The dynamics associated with the motion in heave, roll and pitch are neglected.

In practical applications, the motions of surge, sway, and yaw are dominant. However, the motions of heave (vertical motion), roll (sideways tilting), and pitch (forward/backward tilting) have small influence on the overall maneuvering behavior but greatly increase the computational burden. Therefore, neglecting these motions does not significantly affect the accuracy of the model for control design.

Considering Assumption 2, we define the position vector in {n}, the velocity vector in {b}, and the relative velocity vr = v − vc in {b}, where is the current velocity vector in {b}. Then, the following definitions are made.

Definition 1. Heading angleψ. We define the heading angle ψ as the angle between xn axis (true north) to xb axis of the ship, positive rotation about the zn axis by right-hand screw convention.

Definition 2. Crab angleβc. We define the crab angle βc as the angle between xb axis and the velocity vector (movement direction of the ship), positive rotation about the zb axis by the right-hand screw convention.

(1.1)

Definition 3. Course angleχ. We define the course angle χ as the angle between xn axis and the velocity vector (movement direction of the ship), positive rotation about the zn axis by right-hand screw convention. It is calculated by

(1.2)

Then the 3 DOF horizontal plane models for ship maneuvering can be represented by

(1.3)

where is the rotation matrix from {b} to {n}, , denotes the system inertia matrix which contains the rigid body matrix MRB and hydrodynamic added mass MA, , is the Coriolis-centripetal matrix, denotes the damping matrix, and are the propulsion forces provided by the actuators. The matrices have the following structure:

(1.4)

where the parameters m11,m22, and m33 include the ship inertia including added mass effects, d11,d22, and d33 denote the damping-related coefficients, , , and are the hydrodynamic coefficients, and m and Iz denote the mass and rotational inertia of the underactuated marine vehicle, respectively. Moreover, the state-space representation can be formulated as:

(1.5)

1.4 Maritime Applications

USVs are revolutionizing various maritime industries with their versatility, efficiency, and ability to operate autonomously in challenging environments. These innovative vessels are deployed in a wide range of applications, significantly enhancing capabilities in fields such as bathymetric surveys, wireless data harvesting, maritime shipping, and search and rescue operations.

In bathymetric surveys, USVs play a crucial role by mapping underwater topography with high precision. Equipped with advanced sonar systems and GPS, they can efficiently gather detailed data on ocean depths and seabed features (see Figure 1.6), which is essential for navigation, construction, and environmental monitoring. Compared to traditional methods involving manned vessels or divers, USVs offer several advantages. They can operate continuously for extended periods, covering larger areas at lower costs. Moreover, their smaller size and reduced environmental impact make them ideal for surveying shallow or sensitive areas where larger vessels may struggle to maneuver.

Wireless data harvesting is another key application of USVs (Zhao and Bai, 2024). These vehicles can act as mobile data hubs, collecting information from various sensors and transmitting it to shore-based stations or satellites. This capability is invaluable for scientific research, environmental monitoring, and maintaining communication networks in remote areas. Unlike stationary data collection platforms or manned vessels, USVs provide greater flexibility and accessibility, allowing them to navigate narrow channels, shallow waters, and other challenging environments. Their autonomous operation also reduces the need for human intervention, minimizing risks and costs associated with manned missions.

Figure 1.6 Typical applications.

In the realm of maritime shipping (Baum-Talmor and Kitada, 2022; Li et al., 2022), USVs offer significant benefits by enhancing route optimization, reducing operational costs, and improving safety. They can be used for inspecting hulls, monitoring ship traffic, and even performing autonomous cargo delivery, thereby streamlining logistics and reducing the risk of human error. Compared to traditional manned vessels, USVs are more energy-efficient, emitting fewer pollutants and reducing fuel consumption. Additionally, their smaller size and maneuverability enable them to access ports and navigate congested waterways with ease, improving overall efficiency and reducing delays in shipping operations.

Maritime search and rescue operations are also greatly enhanced by USVs (Ai et al., 2021). These vehicles can quickly cover large areas, operate in hazardous conditions, and provide real-time data to rescue teams. Equipped with cameras, thermal imaging, and other sensors, USVs can locate and assist distressed vessels or individuals at sea, increasing the chances of successful rescue missions. Unlike manned vessels, USVs can operate autonomously in adverse weather conditions, allowing them to continue search and rescue efforts when manned vessels are forced to retreat. Their ability to remain on station for extended periods also improves response times and enhances coordination with other rescue assets.

Also, USVs are revolutionizing water monitoring and sampling efforts (Zhao et al., 2025), providing a highly efficient and cost-effective alternative to traditional methods. Compared to manual sampling techniques conducted by human operators, USVs offer several advantages in water monitoring and sampling. USVs can operate in hazardous or hard-to-reach areas, such as industrial zones, contaminated sites, or remote water bodies, where human access may be limited or unsafe. This capability allows for more comprehensive monitoring of water bodies and early detection of pollution events or environmental hazards.

1.5 Motivation of this Book

The motivation for this book is very straightforward. The intelligence level of USVs is currently still in its early stages. Modern complex engineering applications have diverse requirements for USVs. For instance, seabed mapping requires USVs to cover all mapping areas, maritime wireless data collection needs USVs to complete data transmission while avoiding obstacles, and maritime search and rescue operations require searching based on the probable areas of individuals. Traditional methods are often inadequate for efficiently completing these complex tasks, posing a series of new challenges for the control and planning of USVs. We urgently need to design a range of sophisticated models and algorithms to enable USVs to meet modern engineering applications. Consequently, the authors have conducted a series of studies in recent years addressing these challenges. We have compiled these works into different chapters in this book, hoping that our work will provide new insights into the intelligence of USVs.

References

Abdullah, M., Ahmad, S., Kanwal, A., Farhan, M., Saeed, U.B., Ali, T., Amin, I., An approach to assess offshore wind power potential using bathymetry and near-hub-height reanalysis data.

Ocean Eng.

, 280, 114458, 2023.

Ai, B., Jia, M., Xu, H., Xu, J., Wen, Z., Li, B., Zhang, D., Coverage path planning for maritime search and rescue using reinforcement learning.

Ocean Eng.

, 241, 110098, 2021.

Asadi, K., Suresh, A.K., Ender, A., Gotad, S., Maniyar, S., Anand, S., Noghabaei, M., Han, K., Lobaton, E., Wu, T., An integrated UGV-UAV system for construction site data collection.

Autom. Constr.

, 112, 103068, 2020.

Baum-Talmor, P. and Kitada, M., Industry 4.0 in shipping: Implications to seafarers’ skills and training.

Transp. Res. Interdiscip. Perspect.

, 13, 100542, 2022.

Bingham, B., Foley, B., Singh, H., Camilli, R., Delaporta, K., Eustice, R., Mallios, A., Mindell, D., Roman, C., Sakellariou, D., Robotic tools for deep water archaeology: Surveying an ancient shipwreck with an autonomous underwater vehicle.

J. Field Rob.

, 27, 702–717, 2010.

Deng, T., Xu, X., Ding, Z., Xiao, X., Zhu, M., Peng, K., Automatic collaborative water surface coverage and cleaning strategy of UAV and USVs.

Digit. Commun. Netw.

, 2022.

Durrant-Whyte, H., Where am I? A tutorial on mobile vehicle localization.

Ind. Robot An Int. J.

, 21, 11–16, 1994.

Finn, A. and Scheding, S., Developments and challenges for autonomous unmanned vehicles.

Intell. Syst. Ref. Lib.

, 3, 128–154, 2010.

Fossen, T.I., Line-of-sight path-following control utilizing an extended Kalman filter for estimation of speed and course over ground from GNSS positions.

J. Mar. Sci. Technol.

, 27, 806–813, 2022.

Fossen, T.I.,

Handbook of marine craft hydrodynamics and motion control

, John Wiley & Sons, Hoboken, New Jersey, 2011.

Gage, D.W.,

UGV history 101: A brief history of Unmanned Ground Vehicle (UGV) development efforts

, Naval Ocean Systems Center San Diego, CA, USA, 1995.

Huang, J., Luo, Y., Quan, Q., Wang, B., Xue, X., Zhang, Y., An autonomous task assignment and decision-making method for coverage path planning of multiple pesticide spraying UAVs.

Comput. Electron. Agric.

, 212, 108128, 2023.