Autonomy and Unmanned Vehicles (eBook)

Somaiyeh MahmoudZadeh completed PhD at the School of Computer Science, Engineering and Mathematics, Flinders University, Australia. She is acting as a postdoctoral research fellow in faculty of Information Technology at Monash University. Her area of research includes computational intelligence, autonomy and decision making, situational awareness, and motion planning of autonomous underwater vehicles.  Prof. David M W Powers is Professor of Computer Science and Director of the Centre for Knowledge and Interaction Technology and has research interests in the area of Artificial Intelligence and Cognitive Science. His specific research framework takes Language, Logic and Learning as the cornerstones for a broad Cognitive Science perspective on Artificial Intelligence and its practical applications. Prof. Powers is known as a pioneer in the area of Parallel Logic Programming, Natural Language Learning, Unsupervised Learning and Evaluation of Learning, and was Founding President of ACL SIGNLL, as well as initiating the CoNLL conference. His CV includes positions at Telecom Paris, University of Tilburg, University of Kaiserslautern, Macquarie University, as well as work with industry, and commercialization of research through several startup companies. Prof. Powers also serves on several programming committees and editorial boards and is Series Editor for the Springer book series on Cognitive Science and Technology. Reza Bairam Zadeh has been graduated of Electronics Engineering in 2011 at University of Yamagata, Japan. He is experienced in Engine ECU technical coordination, electronic hardware programming, digital circuits designing, designing and testing heavy duty vehicles’ Electronic Control Units (Engine ECU). His main research interests focus on robotics, smart structures, sensor data fusion and optimization, and intelligent control applications.

Chapter 1: Introduction to Autonomy and Applications1.1 Background1.1.1 Autonomous Mission Planning1.1.2 Autonomous Motion Planning1.2 Problem Statements and Research Motivation1.2.1 Challenges in the Scope of Mission Planning, Vehicle Routing and Task Assigning1.2.2 Challenges in the Scope of the Autonomous Motion Planning1.2.3 Research Motivation1.3 Research Objectives1.4 Research Assumptions and Scope1.5 Statement of ContributionsChapter 2: Autonomy, Decision Making and Situational Awareness2.1 Introduction2.2 Decision Autonomy in Mission Planning and Time Management2.2.1 Mission Planning-Timing by Autonomous Vehicle Routing Problem (VRP) and Task Allocation2.3 Situational Awareness in Autonomous Vehicles Motion Planning2.3.1 Environmental Impact on Autonomous Vehicles Motion Planning2.4 Chapter SummaryChapter 3: Meta-Heuristics Optimization Algorithms3.1 None-Polynomial Hard Problems3.2 Overview of the Meta-Heuristics3.2.1 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-Heuristics3.4 Chapter SummaryChapter 4: Mission Planning and Time Management4.1 Introduction and Definitions4.2 Autonomy, Decision Making and Situational Awareness4.3 Existing Approaches in Vehicle Routing Problem (VRP) and Task-Time Management4.3.1 Open Problems and Research Challenges4.4 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 Approach4.4.2 Shrinking the Search Space to Feasible Task Sequences4.4.3 Optimization Criterion for Task-Assign/Mission-Planning4.4.4 Application of ACO, GA, BBO, PSO, and ICA on Task-Assign/Mission-Planning Approach4.4.5 Simulation Results in Case Study of Underwater Vehicles4.5 Chapter SummaryChapter 5: Autonomous Motion Planning and Situational Awareness5.1 Autonomous Vehicles Motion Planning5.2 Path Construction Methods5.3 Path Planning and Optimization5.4 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 AUVs5.7 Motion Planning in Case Study of AUV5.7.1 Modelling Operational Ocean Environment5.7.1.1 Offline Map5.7.1.2 Mathematical Model with Uncertainty of Static/Dynamic Obstacles5.7.1.3 Mathematical Model of Static/Dynamic Current Field5.7.2 5.7.2.2 On-line Path Re-planning Based on Previous Solution5.7.3 Application of PSO, BBO, FA, and DE on AUV Motion Planning Approach5.7.4 Simulation Results of the Local ORPP Approach5.8 Chapter SummaryChapter 6: Autonomous Reactive Mission-Motion Planning Architecture6.1 Introduction6.2 Shortcomings Associated with the Existing Mission-Motion Planners6.3 Chapter Motivation6.4 Mechanism of the Proposed Modular Architecture6.4.1 Modelling of the ‘Synchron’ Module6.4.2 Architecture Evaluation Criterion6.5 Discussion and Analysis of Simulation Results6.5.1 Simulation Setup and Research Assumption6.5.2 Architecture’s Performance on Scheduling and Time Management for Reliable and Efficient Operation6.5.3 Evaluation of the Architecture through the Examination of Multiple Meta-heuristics6.5.4 Single Run and 100 Monte Carlo Runs6.6 Chapter SummaryChapter 7: Conclusions and Future Work7.1 Summary7.2 Conclusions7.2.1 Autonomous Mission Planning-Timing 7.2.2 Online Real-Time Motion Planning7.2.3 Autonomous Reactive Mission-Motion Planning Architecture7.3 Future Research DirectionsBibliography