Autonomy and Unmanned Vehicles: Augmented Reactive Mission and Motion Planning Architecture

ISBN-13: 9789811322440 / Angielski / Twarda / 2018 / 107 str.

Somaiyeh Mahmoudzadeh; David M. W. Powers; Reza Bairam Zadeh

Opis

This book addresses higher-lower level decision autonomy for autonomous vehicles, and discusses the addition of a novel architecture to cover both levels. the use of diverse algorithms (subjected to the real-time performance of the algorithm) does not negatively affect the system's real-time performance.

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Spis treści

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

O autorze

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.

Blurb

This book addresses higher–lower level decision autonomy for autonomous vehicles, and discusses the addition of a novel architecture to cover both levels. The proposed framework’s performance and stability are subsequently investigated by employing different meta-heuristic algorithms. The performance of the proposed architecture is shown to be largely independent of the algorithms employed; the use of diverse algorithms (subjected to the real-time performance of the algorithm) does not negatively affect the system’s real-time performance. By analyzing the simulation results, the book demonstrates that the proposed model provides perfect mission timing and task management, while also guaranteeing secure deployment. Although mainly intended as a research work, the book’s review chapters and the new approaches developed here are also suitable for use in courses for advanced undergraduate or graduate students.