Industrial metal forming operations generally require several deformation steps in order to create the final product. The mechanical behavior of materials undergoing strain path changes can be very different from those deformed in a given direction to fracture. The work presented here employed laser drilled model materials to better understand the effect of pre-strains and strain path changes on void growth and linkage leading to fracture is studied. The experimental results show that increasing pre-strain results in faster void growth which was justified in terms work hardening rate in the sample. Scanning electron microscope images revealed that the ductility of the sample decreased with increasing pre-strain but only slightly compared to the large decrease in far field strain at failure. This suggests that pre-strain affects strain localization significantly and to a lesser extent the ductility. Finally a finite element model has been built to predict the linkage between voids.

In November 2013, the Islamic Republic of Iran reached an interim agreement with six world powers, including the United States. After the agreement was implemented in January 2014, Iran froze uranium enrichment in exchange for limited sanctions relief from the United States. This was the first diplomatic exchange between the United States and Iran in over thirty years. Keeping Iran from the Bomb analyzes how each country’s respective domestic politics and stereotypes of the other have, until recently, impeded diplomacy between the two nations. This study examines American-Iranian relations during the hostage crisis, the Bush administration, and the Obama administration to do the following: analyze what has prevented diplomacy in the past, explain the circumstances that made the interim agreement possible, and show what factors threaten this diplomatic progress. The primary argument of this thesis is that the leaders of both the United States and Iran are encouraged, and often rewarded, by various entities to demonize the other nation. If the leaders of the United States and Iran can convince their domestic constituents that continued cooperation with the other country will be beneficial, diplomacy can move forward.

The ultimate goal in robotics is to create machines which are more independent and rely less on humans to guide them in their operation. There are many sub-systems which may be present in such a robot, one of which is path planning — the ability to determine a sequence of positions or configurations between an initial and goal position within a particular obstacle cluttered workspace. Many classical path planning techniques have been developed, but these tend to have drawbacks such as their computational requirements; the suitability of the plans they produce for a particular application; or how well they are able to generalise to unseen problems. In recent years, evolutionary based problem solving techniques have seen a rise in popularity, possibly coinciding with the improvement in the computational power afforded researches by successful developments in hardware. These techniques adopt some of the features of natural evolution and mimic them in a computer. The increase in the number of publications in the areas of Genetic Algorithms (GA) and Genetic Programming (GP) demonstrate the success achieved when applying these techniques to ever more problem areas. This dissertation presents research conducted to determine whether there is a place for Evolutionary Approaches, and specifically GA and GP, in the development of future path planning techniques.

Motion planning is an increasingly important field of research. Factory automation is becoming more prevalent and at the same time, production runs are shortening in the name of customisation. With computer controlled equipment becoming cheaper and more modular, setting up near-fully automated production lines is becoming fast and easy. This means that the actual programming of the robots and assembly system is becoming the rate determining step. Automated motion planning is a possible solution to this—but only if it can run fast enough. Many heuristic planners have been created in an attempt to achieve the necessary speeds in off-line (or more ambitiously, on-line) processing. This thesis aims to show that different types of heuristic planners can be designed to take advantage of specialised environments or robot characteristics. To show this, three distinct classes of heuristic planners are put forward for discussion. The first of these classes, addressed in Chapter 2, is of very generic planners which will work in virtually all situations (ie. almost any combination of robot and environment). This generality is obviously useful when lacking more specific domain knowledge. However these methods do suffer performance-wise in comparison with more specialised planners when there are characteristics of the problem which can be targeted. Chapter 3 moves to planners which are designed to specifically address certain peculiarities of the environment. Particular focus is given to environments whose corresponding configuration-spaces contain narrow gaps and passages. Finally Chapter 4 addresses a third class of planners: those which are designed for specific types of robots and movements. The particular focus is on locomotion for legged vehicles. For each of these three classes, some discussion is made of existing planners which can be so characterised. In addition, a novel algorithm is introduced in each as an example for particular consideration.

Path planning and image processing are critical signal processing tasks for robots, autonomous vehicles, animated characters, etc. The ultimate goal of the path planning problem being addressed in this dissertation is how to use a reconfigurable Analog Very Large Scale Integration (AVLSI) circuit to plan a path for a Micro Aerial Vehicle (MAV) (or similar power constrained ground or sea robot) through an environment in an effort to conserve its limited battery resources. Path planning can be summarized with the following three tasks given that states, actions, an initial state, and a goal state are provided. The robot should: 1) Find a sequence of actions that take the robot from its Initial state to its Goal state. 2) Find actions that take the robot from any state to the Goal state, and 3) Decide the best action for the robot to take now in order to improve its odds of reaching the Goal. Image processing techniques can be used to visually track an object. Segmenting the object from the background is one subtask in this problem. Digital image processing can be very computationally expensive in terms of memory and data manipulation. Path planning and image processing computations are typically executed on digital microprocessors. This dissertation explores an evolution of analog signal processing using Field Programmable Analog Arrays (FPAAs); it describes techniques for mapping different solutions onto the hardware, and it describes the benefits and limitations. The motivation is lower power, more capable solutions that also provide better algorithm performance metrics such as time and space complexity. This may be a significant advantage for MAVs, ocean gliders or other robot applications where the power budget for on-board signal processing is limited.

This thesis consists of two parts united by the theme of path/action planning for a mobile robot. Part I presents the Second Opinion Planner (SOP), and Part II presents a new paradigm for navigating, growing, and planning on a Network of Reusable Paths (NRP). Path/action planning is common to both parts in that the planning algorithm must choose the terrain assessment or localization technique at the path-planning stage. Terrain-assessment algorithms follow the trend of low-fidelity at low-cost and high-fidelity at high-cost. Using a high-fidelity method on all the raw terrain data can drastically increase a robot's total path cost (cost of driving, planning, and doing the terrain assessment). SOP is a path-planning algorithm that uses a hierarchy of terrain-assessment methods, from low-fidelity to high-fidelity, and seeks to limit high-cost assessment to areas where it is beneficial. The decision to assess some terrain with a higher-fidelity method is made considering potential path benefits and the cost of assessment. SOP provides a means to triage large amounts of terrain data. The system is demonstrated on simulated problems and in real terrain from an experimental field test carried out on Devon Island, Canada. The SOP plans are quite close to the minimum possible cost. Growing a NRP is an approach to navigation that allows a mobile robot to autonomously explore unmapped, GPS-denied environments. This new paradigm results in closer goal acquisition and a more robust approach to exploration with a mobile robot, when compared to a classic approach to guidance, navigation, and control. A NRP is a simple Simultaneous Localization And Mapping system that can be shown to be a physical embodiment of a Rapidly-exploring Random Tree planner. Simulation results are presented, as well as the results from two different robotic test systems that were tested in planetary analogue environments. NRP offers benefits to planetary exploration by allowing a rover to be used for the parallel scientific investigations. This increases the number of sites that can be investigated in a short time, as compared to a serial approach to exploration. Two mock missions were carried out at planetary analogue sites.

This thesis consists of two parts united by the theme of path/action planning for a mobile robot. Part I presents the Second Opinion Planner (SOP), and Part II presents a new paradigm for navigating, growing, and planning on a Network of Reusable Paths (NRP). Path/action planning is common to both parts in that the planning algorithm must choose the terrain assessment or localization technique at the path-planning stage. Terrain-assessment algorithms follow the trend of low-fidelity at low-cost and high-fidelity at high-cost. Using a high-fidelity method on all the raw terrain data can drastically increase a robot's total path cost (cost of driving, planning, and doing the terrain assessment). SOP is a path-planning algorithm that uses a hierarchy of terrain-assessment methods, from low-fidelity to high-fidelity, and seeks to limit high-cost assessment to areas where it is beneficial. The decision to assess some terrain with a higher-fidelity method is made considering potential path benefits and the cost of assessment. SOP provides a means to triage large amounts of terrain data. The system is demonstrated on simulated problems and in real terrain from an experimental field test carried out on Devon Island, Canada. The SOP plans are quite close to the minimum possible cost. Growing a NRP is an approach to navigation that allows a mobile robot to autonomously explore unmapped, GPS-denied environments. This new paradigm results in closer goal acquisition and a more robust approach to exploration with a mobile robot, when compared to a classic approach to guidance, navigation, and control. A NRP is a simple Simultaneous Localization And Mapping system that can be shown to be a physical embodiment of a Rapidly-exploring Random Tree planner. Simulation results are presented, as well as the results from two different robotic test systems that were tested in planetary analogue environments. NRP offers benefits to planetary exploration by allowing a rover to be used for the parallel scientific investigations. This increases the number of sites that can be investigated in a short time, as compared to a serial approach to exploration. Two mock missions were carried out at planetary analogue sites.

This thesis investigates the control of motion for a general class of vehicles that rotate and translate in three-space, and are propelled by a thrust vector which has fixed direction in body frame. The thesis addresses the problems of path following and position control. For path following, a feedback linearization controller is presented that makes the vehicle follow an arbitrary closed curve while simultaneously allowing the designer to specify the velocity profile of the vehicle on the path and its heading. For position control, a two-stage approach is presented that decouples position control from attitude control, allowing for a modular design and yielding almost global asymptotic stability of any desired hovering equilibrium. The effectiveness of the proposed method is verified both in simulation and experimentally by means of a hardware-in-the-loop setup emulating a co-axial helicopter.

Water resources in Canada are of major environmental, social and economic value. It is expected that climate change will be accompanied by more intense competition for water supply in water-stressed agricultural areas such as the southern Prairies. Beyond physical impacts, drought can be seen as a socio-economic and political problem which ultimately has implications for community-level vulnerability to climate change. This thesis presents empirical vulnerability case studies focused on the exposure-sensitivity and adaptive capacity of Coronach and Gravelbourg in southern Saskatchewan. The results illustrate the fact that farmers or ranchers are not merely passive victims of drought. They also take an active role in shaping the environment around them, thus affecting their own vulnerability to drought. Therefore, by understanding the causal linkages of the coupled social-environment system, a more comprehensive understanding of community vulnerability is achievable and informed decisions can be made based on this thorough understanding of local conditions. In the second part of this thesis, the potential of soft water path is evaluated as a possible adaptation strategy. Based on the results gathered in the first part of this thesis, adaptation measures are tailored to address specific needs of different sectors in the Town of Coronach and the Town of Gravelbourg while ensuring ecological sustainability. Examples of possible paths (adaptation measures) are suggested in order to increase community adaptive capacity to water shortages in light of future climate changes.

Structures of machinery with practical importance, such as home appliances or transportation vehicles, can be considered as acoustically coupled spaces surrounded by elastic enclosures. When the structures of machinery are excited mechanically by means of prime movers incorporated into these structures through some elastic connections, generation of noise becomes an inevitable by-product. For noise control engineering purposes, a thorough understanding of emission, transmission and radiation of sound from structure is required prior to a possible and practical modeling of noise transfer mechanisms. Finally, development of a model for complete noise generation and transfer mechanisms is needed which is essential for the abatement of annoying sound generation. In this study, an experimental and analytical (finite element) methodology for the modal analysis of acoustical cavities is developed, and successfully applied to a case study. The acoustical transmission problem of the structure is investigated via vector intensity analysis. Results of this investigation are used for a noise path qualification, whereas the transfer functions between sources of noise and some relevant receiving points are obtained by use of vibro-acoustic reciprocity principle. The concept of transfer path analysis is investigated by using the multi input, multi output linear system theory for vibro-acoustic modeling of machinery structures. Finally, resolution and ranking of noise sources and transfer paths are accomplished via spectral correlation methodologies developed. The methodology can be extended to any system with linear, time invariant parameters, where principles of superposition and reciprocity are applicable.