Autonomous underwater vehicles have become a popular countermeasure to naval mines. Saab’s AUV62-MR detects, locates and identifies mine-like objects through three phases. By extracting functionality from the AUV62-MR and placing it on a second vehicle, it is suggested that the second and third phases can be performed in parallel. This thesis investigates how to design the second vehicle so that the runtime of the mine reconnaissance process is minimized. A simulation framework is implemented to simulate the second and third phases of the mine reconnaissance process in order to test various design choices. The vehicle design choices in focus are the size and the route planning of the second vehicle. The route-planning algorithms investigated in this thesis are a nearest neighbour algorithm, a simulated annealing algorithm, an alternating algorithm, a genetic algorithm and a proposed Dubins simulated annealing algorithm. The algorithms are evaluated both in a static environment and in the simulation framework. Two different vehicle sizes are investigated, a small and a large, by evaluating their performances in the simulation framework. This thesis takes into account the limited travelling distance of the vehicle and implements a k-means clustering algorithm to help the route planner determine which mine-like objects can be scanned without exceeding the distance limit. The simulation framework is also used to evaluate whether parallel execution of the second and third phases outperforms the current sequential execution. The performance evaluation shows that a major reduction in runtime can be gained by performing the two phases in parallel. The Dubins simulated annealing algorithm on average produces the shortest paths and is considered the preferred route-planning algorithm according to the performance evaluation. It also indicates that a small vehicle size results in a reduced runtime compared to a larger vehicle.

In recent years, unmanned aerial vehicles (UAVs) have become a dominant force in the aerospace industry. Recent technological developments have moved these aircraft from remote operation roles to more active response missions. Of particular interest is the possibility of applying UAVs toward solving complex problems in long-endurance missions. Under that belief, the feasibility of utilizing UAVs for wildfire detection and response was investigated in a partnership that included NASA’s Aeronautics Research Mission Directorate and Science Mission Directorate, and the United States Forest Service. Under NASA’s Intelligent Mission Management (IMM) project, research was conducted to develop a mission architecture that would enable use of a high altitude UAV to search for reported wildfires with a separate low altitude UAV supporting ground assets. This research proposes a “straw man” concept incorporating both a High Altitude Long Endurance (HALE) UAV and a Low Altitude Short Endurance (LASE) UAV in a loosely coupled, low cost solution tailored towards wildfire response. This report identifies the communications architecture, algorithms, and required system configuration that meets the outlined goals of the IMM project by mitigating wildfires and addressing the United States Forest Service immediate needs. The end product is a defined parent-child framework capable of meeting all wildfire mission goals. The concept has been implemented in simulation, the results of which are presented in this report.

Le problème de tournées de véhicules est un des problèmes d’optimisation combinatoire les plus connus et les plus difficiles. Il s’agit de déterminer les tournées optimales pour une flotte de véhicules afin de servir un ensemble donné de clients. Dans les problèmes classiques de transport, chaque client est normalement servi à partir d’un seul nœud (ou arc). Pour cela, on définit toujours un ensemble donné de nœuds (ou arcs) obligatoires à visiter ou traverser, et on recherche la solution à partir de cet ensemble de nœuds (ou arcs). Mais dans plusieurs applications réelles où un client peut être servi à partir de plus d’un nœud, (ou arc), les problèmes généralisés qui en résultent sont plus complexes. Le but principal de cette thèse est d’étudier trois problèmes généralisés de tournées de véhicules. Le premier problème de la tournée sur arcs suffisamment proche (CEARP), comporte une application réelle intéressante en routage pour le relevé des compteurs à distance ; les deux autres problèmes, problème de tournées couvrantes multi-véhicules (mCTP) et problème généralisé de tournées sur nœuds (GVRP), permettent de modéliser des problèmes de conception des réseaux de transport à deux niveaux. Pour résoudre ces problèmes, nous proposons une approche exacte ainsi que des métaheuristiques. Pour développer la méthode exacte, nous formulons chaque problème comme un programme mathématique, puis nous construisons des algorithmes de type branchement et coupes. Les métaheuristiques sont basées sur le ELS (ou Evolutionary Local Search) et sur le GRASP (ou Greedy Randomized Adaptive Search Procedure). De nombreuses expérimentations montrent la performance de nos méthodes. / The Routing Problem is one of the most popular and challenging combinatorial optimization problems. It involves finding the optimal set of routes for fleet of vehicles in order to serve a given set of customers. In the classic transportation problems, each customer is normally served by only one node (or arc). Therefore, there is always a given set of required nodes (or arcs) that have to be visited or traversed, and we just need to find the solution from this set of nodes (or arcs). But in many real applications where a customer can be served by from more than one node (or arc), the generalized resulting problems are more complex. The primary goal of this thesis is to study three generalized routing problems. The first one, the Close-Enough Arc Routing Problem(CEARP), has an interesting real-life application to routing for meter reading while the others two, the multi-vehicle Covering Tour Problem (mCTP) and the Generalized Vehicle Routing Problem(GVRP), can model problems concerned with the design of bilevel transportation networks. The problems are solved by exact methods as well as metaheuristics. To develop exact methods, we formulate each problem as a mathematical program, and then develop branch-and-cut algorithms. The metaheuristics are based on the evolutionary local search (ELS) method et on the greedy randomized adaptive search procedure (GRASP) method. The extensive computational experiments show the performance of our methods.

Space debris growth and an influx in space traffic will create a need for increased space traffic management. Due to orbital population density and likely future growth, the implementation of a slot architecture to Sun-synchronous orbit is considered in order to mitigate conjunctions among active satellites. This paper furthers work done in Sun-synchronous orbit slot architecture design and focuses on two main aspects. First, an in-depth relative motion analysis of satellites with respect to their assigned slots is presented. Then, a method for developing a slot architecture from a specific set of user defined inputs is derived.

<p>In this thesis the problem of multitarget tracking in multistatic sensor networks is studied. This thesis focuses on tracking airborne targets by utilizing transmitters of opportunity in the surveillance region. Passive Coherent Location (PCL) system, which uses existing commercial signals (e.g., FM broadcast, digital TV) as the illuminators of opportunity for target tracking, is an emerging technology in air defence systems. PCL systems have many advantages over conventional radar systems such as low cost, covert operation and low vulnerability to electronic counter measures.</p> <p>One of another opportunistic signals available in the surveillance region is multipath signal. In this thesis, the multipath target return signals from distinct propagation modes that are resolvable by the receiver are exploited. When resolved multipath returns are not utilized within the tracker, i.e., discarded as clutter, potential information conveyed by the multipath detections of the same target is wasted. In this case, spurious tracks are formed using target-originated multipath measurements, but with an incorrect propagation mode assumption. Integrating multipath information into the tracker (and not discarding it) can help improve the accuracy of tracking and reduce the number of false tracks.</p> <p>In this thesis, these opportunistic measurements, i.e., commercial broadcast signals measurements in PCL tracking and resolvable multipath target return measurements in multipath assisted tracking are exploited. We give the optimal formulations for all of the above problems as well as the performance evaluations using PCRLB. Simulation results illustrate the performance of the algorithms.</p> / Doctor of Philosophy (PhD)

This research formulates and implements a novel closed-loop optimal control system that drives a train between two stations in an optimal time, energy efficient, or mixed objective manner. The optimal controller uses sensor feedback from the train and in real-time computes the most efficient control decision for the train to follow given knowledge of the track profile ahead of the train, speed restrictions and required arrival time windows. The control problem is solved both on an open track and while safely driving no closer than a fixed distance behind another locomotive. In contrast to other research in the field, this thesis achieves a real-time capable and embeddable closed-loop optimization with advanced modeling and numerical solving techniques with a non-linear optimal control problem. This controller is first formulated as a non-convex control problem and then converted to an advanced convex second-order cone problem with the intent of using a simple numerical solver, ensuring global optimality, and improving control robustness. Convex and non-convex numerical methods of solving the control problem are investigated and closed-loop performance results with a simulated vehicle are presented under realistic modeling conditions on advanced tracks both on desktop and embedded computer architectures. It is observed that the controller is capable of robust vehicle driving in cases both with and without modeling uncertainty. The benefits of pairing the optimal controller with a parameter estimator are demonstrated for cases where very large mismatches exists between the controller model and the simulated vehicle. Stopping performance is consistently within 25cm of target stations, and the worst case closed-loop optimization time was within 100ms for the computation of a 1000 point control horizon on an i7-6700 machine. / Thesis / Master of Applied Science (MASc) / This research formulates and implements a novel closed-loop optimal control system that drives a train between two stations in an optimal time, energy efficient, or mixed objective manner. It is deployed on a commuter vehicle and directly manages the motoring and braking systems. The optimal controller uses sensor feedback from the train and in real-time computes the most efficient control decision for the train to follow given knowledge of the track profile ahead of the train, speed restrictions and required arrival time windows. The final control implementation is capable of safe, high accuracy and optimal driving all while computing fast enough to reliably deploy on a rail vehicle.