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Autonomous Vehicles Planning and Control

By Yong Bai & Liang Zhao
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394355044  |  Hardcover  |  
690 pages
Price: $225 USD
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One Line Description
Master the future of marine exploration and technology with Autonomous Vehicles Planning and Control, which provides a comprehensive, interdisciplinary guide to the principles, control, and real-world applications of autonomous marine vehicles.

Audience
Academics, researchers, industry professionals, and maritime and environmental agencies specializing in marine robotics, autonomous systems, control engineering, and artificial intelligence

Description
Autonomous 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.

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Author / Editor Details
Yong Bai, PhD is a professor in the College of Civil Engineering and Architecture at Zhejiang University. He has written over 200 academic papers in national and international academic journals and internationally published over 20 books. His research interests include marine engineering structures, unmanned surface vehicles, autonomous underwater vehicles, hydrogen vessels, marine pipelines and risers, engineering risk analysis, and safety assessment.

Liang Zhao, PhD is a research fellow at Zhejiang University. He has co-authored over 20 research articles in top engineering journals. His current research focuses on planning and decision making for marine robotics, asynchronous maritime perception, and green and intelligent shipping.

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Table of Contents
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
2.2.1 Dynamic Positioning and Position Mooring Systems
2.2.1.1 Dynamic Positioning Control System
2.2.1.2 Position Mooring Control System
2.2.2 Waypoint Tracking and Path-Following Control Systems
2.2.2.1 Waypoint Tracking Control System
2.2.2.2 Path-Following Control System
2.3 Advanced Control System Development
2.3.1 Linear Quadratic Optimal Control
2.3.2 State Feedback Linearization
2.3.2.1 Decoupling in the BODY Frame (Velocity Control)
2.3.2.2 Decoupling in the NED Frame (Position and Attitude Control)
2.3.3 Integrator Backstepping Control
2.3.4 Sliding-Mode Control
2.3.4.1 SISO Sliding-Mode Control
2.3.4.2 Sliding-Mode Control Using the Eigenvalue Decomposition
References
3. Perception and Sensing Module
3.1 Low-Pass and Notch Filtering
3.1.1 Low-Pass Filtering
3.1.2 Cascaded Low-Pass and Notch Filtering
3.2 Fixed Gain Observer Design
3.2.1 Observability
3.2.2 Luenberger Observer
3.2.3 Case Study: Luenberger Observer for Heading Autopilots Using Only Compass Measurements
3.3 Kalman Filter Design
3.3.1 Discrete-Time Kalman Filter
3.3.2 Continuous-Time Kalman Filter
3.3.3 Extended Kalman Filter
3.3.4 Corrector–Predictor Representation for Nonlinear Observers
3.3.5 Case Study: Kalman Filter for Heading Autopilots Using Only Compass Measurements
3.3.5.1 Heading Sensors Overview
3.3.5.2 System Model for Heading Autopilot Observer Design
3.3.6 Case Study: Kalman Filter for Dynamic Positioning Systems Using GNSS and Compass Measurements
3.4 Nonlinear Passive Observer Designs
3.4.1 Case Study: Passive Observer for Dynamic Positioning Using GNSS and Compass Measurements
3.4.2 Case Study: Passive Observer for Heading Autopilots Using only Compass Measurements
3.4.3 Case Study: Passive Observer for Heading Autopilots Using Both Compass and Rate Measurements
3.5 Integration Filters for IMU and Global Navigation Satellite Systems
3.5.1 Integration Filter for Position and Linear Velocity
3.5.2 Accelerometer and Compass Aided Attitude Observer
3.5.3 Attitude Observer Using Gravitational and Magnetic Field Directions
References
4. Model Predictive Control for Autonomous Marine Vehicles: A Review
4.1 Introduction
4.1.1 Object Introduction
4.1.2 Previous Reviews
4.2 Fundamental Models and a General Picture
4.2.1 Model of AMVs
4.2.1.1 6-DOF Model
4.2.1.2 3-DOF Model
4.2.2 Model Predictive Control
4.2.3 Literature Search
4.3 Methodology
4.3.1 MPC Applications of AMVs
4.3.1.1 Real-Coded Chromosome
4.3.1.2 Path Following
4.3.1.3 Trajectory Tracking
4.3.1.4 Cooperative Control/Formation Control
4.3.1.5 Collision Avoidance
4.3.1.6 Energy Management
4.3.1.7 Other Topics
4.4 Discussion
4.4.1 Limitations of Existing Techniques and Challenges in Developing MPC
4.4.1.1 Uncertainties of AMV Motion Models
4.4.1.2 Stability and Security of the New MPC Method
4.4.1.3 The Balance Between Effectiveness and Efficiency of the Methods
4.4.1.4 The Practical Application Scenario of the MPC and the Discussion of the Working Conditions
4.4.1.5 Challenges Posed by the Marine Environment Affect MPC Development
for AMVs
4.4.2 Trends in the Technology Development for MPC in AMV
4.4.2.1 More Cooperative Control with MPC
4.4.2.2 Rigorous Theoretical Derivation and Experimental Verification
4.4.2.3 Real-Time MPC for AMVs Applications
4.4.2.4 The Combination of Machine Learning/Neural Networks and MPC for AMVs Applications
4.4.2.5 Address the Challenges Posed by the Marine Environment
4.4.2.6 Potential Interdisciplinary Approaches that Combine MPC with Other Innovative Fields
4.5 Conclusion
Acknowledgement
References
5. Controller-Consistent Path Planning for Unmanned Surface Vehicles
5.1 Introduction
5.2 Problem Formulation
5.3 Methodology
5.3.1 Improved Artificial Fish Swarm Algorithm
5.3.1.1 Prey Behavior
5.3.1.2 Follow Behavior
5.3.1.3 Swarm Behavior
5.3.1.4 Random Behavior
5.3.1.5 Adaptive Visual and Step
5.3.2 Expanding Technique
5.3.3 Node Cutting and Path Smoother
5.3.4 Establishment of USV Model
5.4 Simulation
5.4.1 Monte Carlo Simulation
5.4.2 Path Quality Test
5.4.3 Simulation Using USV Control Model in Practical Environment
5.5 Conclusion
References
6. Nonlinear Model Predictive Control and Routing for USV-Assisted Water Monitoring
6.1 Introduction
6.2 Problem Formulation
6.2.1 Heterogeneous Global Path Planning Problem
6.2.1.1 USV Model
6.2.1.2 Task Model
6.2.1.3 Problem Statement
6.2.2 Problem Analysis
6.2.3 Path Following Problem
6.2.3.1 Basic Assumptions
6.2.3.2 Vessel Model
6.2.3.3 Problem Description
6.3 Methodology
6.3.1 Greedy Partheno Genetic Algorithm
6.3.1.1 Dual-Coded Chromosome
6.3.1.2 Fitness Function
6.3.1.3 Greedy Randomized Initialization
6.3.1.4 Local Exploration
6.3.1.5 Mutation Operators
6.3.1.6 Algorithm Flow
6.3.2 Nonlinear Model Predictive Control
6.3.2.1 State Space Model
6.3.2.2 NMPC Design
6.3.2.3 Solver
6.3.2.4 Stability
6.4 Results and Discussion
6.4.1 Simulation: Global Task Planning
6.4.1.1 Convergence Test
6.4.1.2 Heterogeneous Task Planning
6.4.2 Simulation: NMPC Control Performance
6.4.2.1 Test 1: Simulation Under Different Model Uncertainties
6.4.2.2 Test 2: Comparative Study with Other Methods
6.4.3 Simulation Verification of the Framework
6.5 Conclusion
References
7. Global-Local Hierarchical Framework for USV Trajectory Planning
7.1 Introduction
7.2 Problem Formulation
7.2.1 Marine Environment
7.2.2 Dynamic Obstacles
7.2.3 Effects of Currents
7.2.4 USV Model and Constraints
7.2.5 Protocol Constraints
7.2.6 Objective Functions
7.2.6.1 The Minimum Cruising Time
7.2.6.2 The Minimum Variation of Heading Angle
7.2.6.3 The Safest Path
7.2.7 Problem Statement
7.3 Methodology
7.3.1 Adaptive-Elite GA with Fuzzy Inference (AEGAfi)
7.3.1.1 Real-Coded Chromosome
7.3.1.2 Initialization Based on Adaptive Random Testing (ART)
7.3.1.3 Adaptive Elite Selection
7.3.1.4 Double-Functioned Crossover
7.3.1.5 Mutation Operators
7.3.1.6 Fuzzy-Based Probability Choice
7.3.1.7 Fitness Function Design
7.3.2 Replanning Strategy Based on Sensory Vector
7.3.2.1 Sensory Vector Structure
7.3.2.2 Formulation of Vs
7.3.2.3 Formulation of Gap Vector Vg Based on COLREGs
7.3.2.4 Formulation of Transition Path
7.4 Simulation Study
7.4.1 Convergence Benchmark Analysis
7.4.2 Simulation Under Static Environment
7.4.3 Simulation Under Time-Varying Environment
7.4.4 Simulation on Real-World Geography
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
8.2.1 Environment Model
8.2.2 Sensor Node and Communication Model
8.2.3 USV Model
8.2.3.1 Kinematic Model
8.2.3.2 Sensing Module
8.3 Methodology
8.3.1 Brief States on Q-Learning
8.3.2 Interactive Learning
8.3.2.1 Heuristic Reward Design
8.3.2.2 Design of Value-Iterated Global Cost Matrix
8.3.2.3 Local Cost Matrix and Path Generation
8.3.2.4 USV Actions with Discrete Precise Clothoid Path
8.3.3 Summary of the Path Planning Algorithm
8.3.4 Time Complexity
8.4 Results and Discussion
8.4.1 Performance Indicators
8.4.2 Hyper-Parameter Analysis
8.4.3 Comparative Study with State of the Art
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
9.2.1 Environment Modeling
9.2.1.1 Motion Area
9.2.1.2 Effects of Currents
9.2.2 Dynamic Obstacles
9.2.3 Motion Constraints
9.2.4 Objective Functions
9.2.4.1 Path Length
9.2.4.2 Path Smoothness
9.2.4.3 Energy Consumption
9.2.4.4 The Safest Path
9.2.5 Optimization Problem Statement
9.3 Methodology
9.3.1 Framework of NSGA-II
9.3.2 AENSGA-II
9.3.2.1 Real-Coded Representation
9.3.2.2 Initialization Using Candidate Set Adaptive Random Testing (CSART)
9.3.2.3 Adaptive Crowding Distance (ACD) Strategy
9.3.2.4 Improved Binary Tournament Selection
9.3.3 Fuzzy Satisfactory Degree
9.3.4 Replanning Strategy Based on Sensory Vector
9.3.4.1 Sensory Vector Structure
9.3.4.2 Formulation of Gap Vector Vg Based on COLREGs
9.3.4.3 Formulation of Transition Path
9.4 Results and Discussion
9.4.1 Convergence and Diversity Analysis
9.4.2 Implementation in Static Environment
9.4.2.1 Fixed Currents
9.4.2.2 Time-Varying Currents
9.4.3 Simulation Under Dynamic Environment
9.5 Conclusion
Acknowledgements
References
10. Coordinated Trajectory Planning for Multiple AUVs
10.1 Introduction
10.1.1 Background
10.1.2 Related Work
10.1.3 Contributions
10.2 Problem Model
10.2.1 Environment Model
10.2.2 AUV Model
10.2.3 Space and Time Constraint Model
10.2.4 Optimization Terms
10.2.5 Problem Statement
10.3 Solver Design
10.3.1 Brief States on Grey Wolf Optimizer
10.3.2 Parallel Grey Wolf Optimizer Design
10.4 Results and Discussion
10.4.1 Simulation 1: Allocation Task
10.4.2 Simulation 2: Rendezvous Task
10.5 Conclusion
Acknowledgements
References
11. Coverage Strategy for USV-Assisted Coastal Bathymetric Mapping
11.1 Introduction
11.2 Fundamental Models
11.2.1 Region of Interest
11.2.2 USV Model
11.3 Methodology
11.3.1 Coastal Line Approximation
11.3.2 Coverage Strategy
11.3.2.1 Trapezoidal Cellular Decomposition
11.3.2.2 Optimal Back and Forth Coverage Algorithm
11.3.2.3 Theoretical Analysis
11.3.3 Fuzzy-Biased Random Key Evolutionary Algorithm (FRKEA)
11.3.3.1 Chromosome Mapping
11.3.3.2 Evaluation in Real Space
11.3.3.3 Elitist Breeding
11.3.3.4 Mutating
11.3.3.5 Fuzzy Bias
11.4 Results and Discussion
11.4.1 Convergence Analysis
11.4.2 Simulation Study
11.4.2.1 Competitive Study
11.4.2.2 Parameter Analysis
11.4.3 Lake Trials
11.5 Conclusion
References
12. Energy-Efficient Coverage for USV-Assisted Bathymetric Survey Under Currents
12.1 Introduction
12.2 Methodology
12.2.1 Problem Models
12.2.1.1 Region of Interest
12.2.1.2 Current Model
12.2.1.3 USV Kinematics Under Currents
12.2.1.4 Energy Estimation
12.2.2 Coverage Strategy
12.3 Results and Discussion
12.3.1 Preparation
12.3.2 Analysis on Polygon Shapes
12.3.3 Analysis on Attacking Angle
12.3.4 Analysis on Different Coverage Strategy
12.3.5 Test on a Complex Concave ROI
12.4 Conclusion
References
13. Modeling and Solving Time-Sensitive Task Allocation for USVs with Mixed Capabilities
13.1 Introduction
13.2 Problem Formulation
13.2.1 Fundamental Models
13.2.1.1 USV Model
13.2.1.2 Target Model
13.2.2 Extended-Restriction Multiple Traveling Salesman Problem (ER-MTSP)
13.2.3 Problem Analysis
13.3 Methodology
13.3.1 Dual-Coded Chromosome Representation
13.3.2 Adaptive Random Testing Initialization
13.3.3 Hierarchical Crossover
13.3.4 Customized Mutation Strategy
13.3.5 Two-Phase Refinement Strategy
13.3.6 Linguistic Satisfactory Degree
13.4 Results and Discussion
13.4.1 Convergence and Diversity Analysis
13.4.2 Case Studies
13.4.3 Field Test
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
14.2.1 Definitions
14.2.2 Problem Statement
14.2.3 Theoretical Analysis
14.3 Methods for Problem Solving
14.3.1 Bisection-Based Convex Decomposition
14.3.2 Hierarchical Heuristic Optimization Algorithm
14.3.2.1 Order Generation
14.3.2.2 Candidate Pattern Finding
14.3.2.3 Tour Finding
14.3.2.4 Final Optimization
14.4 Results and Discussion
14.4.1 Validation in Simulation
14.4.2 Lake Experiments
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
15.2.1 Pipe Segmentation
15.2.1.1 Descriptor-Based Methods
15.2.1.2 Learning-Based Methods
15.2.2 Dataset Preparation
15.2.3 Pipe Reconstruction
15.3 Methodology
15.3.1 BIM-Based Data Generating
15.3.2 Network Architecture
15.3.2.1 Overall Architecture
15.3.2.2 PipeSegNet Architecture
15.3.2.3 Feature Alignment Module
15.3.2.4. Label Alignment Module
15.3.2.5 Loss Function
15.3.3 Pipe Geometric Reconstruction
15.4 Experiment
15.4.1 Experimental Settings
15.4.2 Evaluation Metrics
15.4.3 Results and Discussion
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
16.2.1 Introduction to Arc Routing
16.2.2 Common Applications of Arc Routing
16.3 One Solution for Arc Problem: The Chinese Postman Problem
16.3.1 Basic Conception
16.3.2 Core Formulation
16.3.3 Variants of the Chinese Postman Problem
16.3.4 Algorithmic Approaches and Solution Methods
16.3.4.1 Polynomial-Time Solutions
16.3.4.2 NP-Hard Variants
16.4 Case Study
16.4.1 Background
16.4.2 Platform Design
16.4.3 Full Coverage Problem
16.4.3.1 Theoretical Formulation: Using the Chinese Postman Problem for Efficient Coverage
16.4.3.2 Coverage Path Generation
16.4.3.3 Discussion
16.5 Concluding Remarks
References
17. Atmospheric Scattering Model-Based Dataset for Maritime Object Detection with YOLOv11
17.1 Introduction
17.2 Methodology
17.2.1 Physics-Based Fog Simulation Using Depth Estimation
17.2.1.1 MiDaS: Monocular Depth Estimation
17.2.1.2 Atmospheric Scattering Model
17.2.2 YOLOv11
17.3 Experiment
17.3.1 Dataset
17.3.2 Foggy Dataset Generation and Model Training
17.3.2.1 Foggy Dataset Generation
17.3.2.2 Model Training
17.4 Result and Discussion
17.4.1 Baseline Training and Generalization Analysis
17.4.2 Improving Model Robustness with Mixed-Concentration Fog Training
17.4.3 Detection Result Comparison
17.5 Conclusion
References
18. Multisensor Perception and Data Fusion Technologies
18.1 Camera-Based Detection Approaches
18.1.1 RGB and Stereo Camera
18.1.2 Infrared and Thermal Camera
18.1.3 Object Detection Methodologies
18.2 LiDAR-Based Detection Approaches
18.2.1 Stages of Object Detection
18.2.2 Challenges and Resolutions
18.3 Data Fusion Methods
18.3.1 Radar
18.3.2 Fusion Level
18.3.3 Synchronization and Calibration
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
20.2.1 Recursive Cell Decomposition Method
20.2.2 Optimal Path Generation
20.2.3 Guidance Planning: Adaptive Line-of-Sight (ALOS) Method
20.3 Control Module Design: USV Dynamics Modeling
20.4 Combined Navigation Module Design
References
Index

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