Chapter 1: Introduction to Autonomy and Applications 1.1 Background 1.1.1 Autonomous Mission Planning 1.1.2 Autonomous Motion Planning 1.2 Problem Statements and Research Motivation 1.2.1 Challenges in the Scope of Mission Planning, Vehicle Routing and Task Assigning 1.2.2 Challenges in the Scope of the Autonomous Motion Planning 1.2.3 Research Motivation 1.3 Research Objectives 1.4 Research Assumptions and Scope 1.5 Statement of Contributions Chapter 2: Autonomy, Decision Making and Situational Awareness 2.1 Introduction 2.2 Decision Autonomy in Mission Planning and Time Management 2.2.1 Mission Planning-Timing by Autonomous Vehicle Routing Problem (VRP) and Task Allocation 2.3 Situational Awareness in Autonomous Vehicles Motion Planning 2.3.1 Environmental Impact on Autonomous Vehicles Motion Planning 2.4 Chapter Summary Chapter 3: Meta-Heuristics Optimization Algorithms 3.1 None-Polynomial Hard Problems 3.2 Overview of the Meta-Heuristics 3.2.1 Ant Colony Optimisation (ACO) 3.2.2 Biogeography-Based Optimisation (BBO) 3.2.3 Differential Evolution (DE) 3.2.4 Firefly Optimization Algorithm (FOA) 3.2.5 Genetic Algorithm (GA) 3.2.6 Imperialist Competitive Algorithm (ICA) 3.2.7 Particle Swarm Optimization (PSO) 3.3 Advantages and Disadvantages of Meta-Heuristics 3.4 Chapter Summary Chapter 4: Mission Planning and Time Management 4.1 Introduction and Definitions 4.2 Autonomy, Decision Making and Situational Awareness 4.3 Existing Approaches in Vehicle Routing Problem (VRP) and Task-Time Management 4.3.1 Open Problems and Research Challenges 4.4 Mission Planning and Time Management in Case Study of Autonomous Underwater Vehicles (AUV) 4.4.1 Problem Formulation of the AUV Task-Assign/Mission-Planning Approach 4.4.2 Shrinking the Search Space to Feasible Task Sequences 4.4.3 Optimization Criterion for Task-Assign/Mission-Planning 4.4.4 Application of ACO, GA, BBO, PSO, and ICA on Task-Assign/Mission-Planning Approach 4.4.5 Simulation Results in Case Study of Underwater Vehicles 4.5 Chapter Summary Chapter 5: Autonomous Motion Planning and Situational Awareness 5.1 Autonomous Vehicles Motion Planning 5.2 Path Construction Methods 5.3 Path Planning and Optimization 5.4 Methodological Point of View to the Existing Autonomous Motion Planning Approaches 5.5 Technical Point of View to the Existing Autonomous Motion Planning Approaches 5.6 Open Problems Motion Planning in Case Study of AUVs 5.7 Motion Planning in Case Study of AUV 5.7.1 Modelling Operational Ocean Environment 5.7.1.1 Offline Map 5.7.1.2 Mathematical Model with Uncertainty of Static/Dynamic Obstacles 5.7.1.3 Mathematical Model of Static/Dynamic Current Field 5.7.2 5.7.2.2 On-line Path Re-planning Based on Previous Solution 5.7.3 Application of PSO, BBO, FA, and DE on AUV Motion Planning Approach 5.7.4 Simulation Results of the Local ORPP Approach 5.8 Chapter Summary Chapter 6: Autonomous Reactive Mission-Motion Planning Architecture 6.1 Introduction 6.2 Shortcomings Associated with the Existing Mission-Motion Planners 6.3 Chapter Motivation 6.4 Mechanism of the Proposed Modular Architecture 6.4.1 Modelling of the 'Synchron' Module 6.4.2 Architecture Evaluation Criterion 6.5 Discussion and Analysis of Simulation Results 6.5.1 Simulation Setup and Research Assumption 6.5.2 Architecture's Performance on Scheduling and Time Management for Reliable and Efficient Operation 6.5.3 Evaluation of the Architecture through the Examination of Multiple Meta-heuristics 6.5.4 Single Run and 100 Monte Carlo Runs 6.6 Chapter Summary Chapter 7: Conclusions and Future Work 7.1 Summary 7.2 Conclusions 7.2.1 Autonomous Mission Planning-Timing 7.2.2 Online Real-Time Motion Planning 7.2.3 Autonomous Reactive Mission-Motion Planning Architecture 7.3 Future Research Directions Bibliography Skryť popis