Autonomous Marine Vehicles Planning and Control

• Autore: Bai Yong
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2025; ©2026
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (505 pages)
• Altri autori: ZhaoLiang
• Nota di contenuto: Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 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 -- Chapter 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 -- Chapter 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 -- Chapter 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 -- Chapter 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 -- Chapter 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 -- Chapter 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 -- Chapter 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 -- Chapter 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Autonomous Marine Vehicles Planning and Control
• ISBN: 1-394-35507-6; 1-394-35505-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911034469103321
• Lo trovi qui: Univ. Federico II