Long range path planning for an autonomous ground vehicle with minimal a-priori data is still very much an open problem. Previous research has demonstrated that least cost paths generated from aerial LIDAR and GIS data could play a role in automatically determining suitable routes over otherwise unknown terrain. However, most of this research has been theoretical. Consequently, there is very little literature the effectiveness of these techniques in plotting paths of an actual autonomous vehicle. This research aims to develop an algorithm for using aerial LIDAR and imagery to plan paths for a full size autonomous car. Methods of identifying obstacles and potential roadways from the aerial LIDAR and imagery are reviewed. A scheme for integrating the path planning algorithms into the autonomous vehicle existing systems was developed and eight paths were generated and driven by an autonomous vehicle. The paths were then analyzed for their drivability and the model itself was validated against the vehicle measurements. The methods described were found to be suitable for generating paths both on and off road. / Master of Science

<p> A stream function finite element model is developed to solve for particle paths within a large ice mass. A steady-state primitive variable finite element model, treating ice as an incompressible non-Newtonian fluid, is used to furnish the necessary input velocities and rotations for the stream function finite element model. Time-integration along the particle paths is used to determine the age of the ice within the ice mass.</p> <p> Two ice masses are studied: the Barnes Ice Cap, Baffin Island, N.W.T., and Mount Logan, Yukon Territory. It is shown that if a realistic approximation of the velocity field of an ice mass can be established, the age of ice determined by time-integration along particle paths corresponds to the age determined by standard methods. Results of simulations using a transient model suggest that the elastic response of large ice masses is negligible.</p> / Thesis / Master of Engineering (MEngr)

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

The concept of the Forming Limit Diagram (FLD) has proved to be useful for representing conditions for the onset of sheet necking, and is now a standard tool for characterizing materials in terms of their overall forming behavior. In this study, the M-K approach, in conjunction with Gurson model, is used to calculate FLDs. The influences of mechanical properties, including strain hardening, strain rate sensitivity, as well as the void nucleation, growth and coalescence, on the FLDs are examined. Most sheet metals undergo multiple deformation modes (strain paths) when being formed into complex manufacturing parts. When the strain path is changed in the deformation processing of metal, it's work-hardening and flow strength differs from the monotonic deformation characteristics. As a consequence, sheet metal formability is very sensitive to strain path changes. In this study, the hardening behavior and damage evolution under non-proportional loading paths are investigated. The effect of strain path change on FLDs is studied in detail. FLDs are conventionally constructed in strain space and are very sensitive to strain path changes. Alternatively, many researchers represented formability based on the state of stress rather than the state of strain. They constructed a Forming Limit Stress Diagram (FLSD) by plotting the calculated principal stresses at necking. It was concluded that FLSDs were almost path-independent. In this work, the FLSD has been constructed under non-proportional loading conditions to assess its path dependency when damage effect is included. / Thesis / Master of Applied Science (MASc)

As a result of recent technological advances in modernized sensor sets and sensor platforms, sensor management combined with sensor platform path planning are studied to conduct intelligence, surveillance and reconnaissance (ISR) operations in novel ways. This thesis addresses the path planning and sensor management for aerial vehicles to cover areas of interest (AOIs), scan objects of interest (OOIs) and/or track multiple detected targets in surveillance missions. The problems in this thesis, which include 1) the spatio-temporal coordination of sensor platforms to observe AOIs or OOIs, 2) the optimal sensor geometry and path planning for localization and tracking of targets in a mobile three-dimensional (3D) space, and 3) the scheduling of sensors working in different (i.e., active and passive) modes combined with path planning to track targets in the presence of jammers, emerge from real-world demands and scenarios. The platform path planning combined with sensor management is formulated as optimization problems with problem-dependent performance evaluation metrics and constraints. Firstly, to cover disjoint AOIs over an extended time horizon using multiple aerial vehicles for persistent surveillance, a joint multi-period coverage path planning and temporal scheduling, which allows revisiting in a single-period path, is formulated as a combinatorial optimization with novel objective functions. Secondly, to use a group of unmanned aerial vehicles (UAVs) cooperatively carrying out search-and-track (SAT) in a mobile 3D space with a number of targets, a joint path planning and scanning (JPPS) is formulated based on the predictive information gathered from the search space. The optimal 3D sensor geometry for target localization is also analyzed with the objective to minimize the estimation uncertainty under constraints on sensor altitude, sensor-to-sensor and sensor-to-target distances for active or passive sensors. At last, to accurately track targets in the presence of jammers broadcasting wide-band noise by taking advantage of the platform path planning and the jammer's information captured by passive sensors, a joint path planning and active-passive scheduling (JPPAPS) strategy is developed based on the predicted tracking performance at the future time steps in a 3D contested environment. The constraints on platform kinematic, flyable area and sensing capacity are included in these optimization problems. For these multisensor path planning and decision making, solution techniques based on the genetic algorithm are developed with specific chromosome representations and custom genetic operators using either the non-dominated sorting multiobjective optimization (MOO) architecture or the weighted-sum MOO architecture. Simulation results illustrate the performance and advantage of the proposed strategies and methods in real-world surveillance scenarios. / Thesis / Doctor of Philosophy (PhD)