The present study proposes a novel approach to Coverage Path Planning for unmanned aerial vehicle (UAV) inspired by the Orienteering Problem. The main goal is to develop an algorithm suitable for Search and Rescue Missions, which can produce a search pattern with dynamical constrains, that is not limited to the traditional back-and-forth motion or spiral patterns. This method leads to a more flexible and diverse coverage of the Area of Interest. In order to generate dynamically correct trajectories, we utilize cubic splines as motion primitives to solve the Orienteering Problem. To accomplish this, we implement and test three different types of cubic splines, namely Catmull-Rom, Freya, and B-splines. To determine the coverage of the search area, the sensor's projection (footprint) is evaluated along the spline trajectory onto a costmap. This method accounts for the footprint's orientation and size, which depend on the UAV's attitude to some extent. This version of the Orienteering Problem using splines for dynamical control and calculating coverage, we call the Mapping Motion Orienteering Problem (MMOP). \\The heuristic method Simulated Annealing is used to address the combinatorial challenges of the MMOP, and two cost functions are tested for optimization. The study shows that the choice of spline has a significant impact on the algorithm's efficacy, and B-splines are the most effective in generating dynamic and adaptable trajectories. However, the study also shows that the Simulated Annealing algorithm with identical settings produced varied resulting paths. Finally, further research is needed to solve the challenges faced with the computational time, which heavily depends on factors such as the sampling rate for the footprint along the path and the resolution of the costmap and footprint itself.

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This bachelor thesis will, through a method study, make an overlook of hypothetical movement patterns and travel routes in northern Sweden between Mesolithic sites/settlements.The common interpretation of the early movement patterns of northern Sweden has been heavily based on waterways as travel routes. With the building of dams along the major rivers in northern Sweden since 1940s many sites have been found along these rivers. This thesis will explore alternative routes based on least cost path analyses and movement patterns over land.Through data of topographic elevation and the Least Cost Path tool through QGIS, hypothetical connections will appear between the sites. The hypothetical travel routes will be tested with data from the cultural register of Sweden to find sites with similar dating near the routes. Through this thesis connections between sites over a large area will test if people moved through these hypothetical routes.

A major factor that negatively impacts flight operations globally is adverse weather. To reduce the impact of adverse weather, avoidance procedures such as finding an alternative flight path can usually be carried out. However, such procedures usually introduce extra costs such as flight delay. Hence, there exists a need for alternative flight paths that efficiently avoid adverse weather regions while minimising costs. Existing weather avoidance methods used techniques, such as Dijkstra’s and artificial potential field algorithms that do not scale adequately and have poor real time performance. They do not adequately consider the impact of weather and its avoidance on passengers. The contributions of this work include a new development of an improved integrated model for weather avoidance, that addressed the impact of weather on passengers by defining a corresponding cost metric. The model simultaneously considered other costs such as flight delay and fuel burn costs. A genetic algorithm (GA)-based rerouting technique that generates optimised alternative flight paths was proposed. The technique used a modified mutation strategy to improve global search. A discrete firefly algorithm-based rerouting method was also developed to improve rerouting efficiency. A data framework and simulation platform that integrated aeronautical, weather and flight data into the avoidance process was developed. Results show that the developed algorithms and model produced flight paths that had lower total costs compared with existing techniques. The proposed algorithms had adequate rerouting performance in complex airspace scenarios. The developed system also adequately avoided the paths of multiple aircraft in the considered airspace.

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This thesis examines the existence of a dominant route between a hub pair and factors that influence bike share cyclists route choices. This research collects 132,396 hub to-hub global positioning system (GPS) trajectories over a 12-month period between April 1, 2015 and March 31, 2016 from 750 bicycles provided by a bike share program (BSP) called SoBi (Social Bicycles) Hamilton. Then, a GIS-based map-matching toolkit is used to convert GPS points to map-matched trips and generate a series of route attributes. In order to create choice sets, unique routes between the same hub pair are extracted from all corresponding repeated trips using a link signature tool. The results from t statistics and Path-size logit models indicate that bike share cyclists are willing to detour for some positive features, such as bicycle facilities and low traffic volumes, but they also try to avoid too circuitous routes, turns, and steep slopes over 4% though detouring may come with a slight increase in turns. This research not only helps us understand BSP cyclist route preferences but also presents a GIS-based approach to determine potential road segments for additional bike facilities on the basis of such preferences. / Thesis / Master of Science (MSc)

Aerobiological sampling using unmanned aerial vehicles (UAVs) is an exciting research field blending various scientific and engineering disciplines. The biological data collected using UAVs helps to better understand the atmospheric transport of microorganisms. Autopilot-equipped UAVs can accurately sample along pre-defined flight plans and precisely regulated altitudes. They can provide even greater utility when they are networked together in coordinated sampling missions: such measurements can yield further information about the aerial transport process. In this work flight vehicle path planning, control and coordination strategies are considered for unmanned autonomous aerial vehicles. A time-optimal path planning algorithm, that is simple enough to be solved in real time, is derived based on geometric concepts. The method yields closed-form solution for an important subset of candidate extremal paths; the rest of the paths are found using a simple numerical root-finding algorithm. A multi-UAV coordination framework is applied to a specific control-volume sampling problem that supports aerobiological data-collection efforts conducted in the lower atmosphere. The work is part of a larger effort that focuses on the validation of atmospheric dispersion models developed to predict the spread of plant diseases in the lower atmosphere. The developed concepts and methods are demonstrated by field experiments focusing on the spread of the plant pathogen <i>Phytophthora infestans</i>. / Ph. D.

This work is inspired by robotic search applications where a robot or team of robots is equipped with sensors and tasked to autonomously acquire as much information as possible from a region of interest. To accomplish this task, robots must plan paths through the region of interest that maximize the effectiveness of the sensors they carry. Receding horizon path planning is a popular approach to addressing the computationally expensive task of planning long paths because it allows robotic agents with limited computational resources to iteratively construct a long path by solving for an optimal short path, traversing a portion of the short path, and repeating the process until a receding horizon path of the desired length has been constructed. However, receding horizon paths do not retain the optimality properties of the short paths from which they are constructed and may perform quite poorly in the context of achieving the robotic search objective. The primary contributions of this work address the worst-case performance of receding horizon paths by developing methods of using terminal rewards in the construction of receding horizon paths. We prove that the proposed methods of constructing receding horizon paths provide theoretical worst-case performance guarantees. Our result can be interpreted as ensuring that the receding horizon path performs no worse in expectation than a given sub-optimal search path. This result is especially practical for subsea applications where, due to use of side-scan sonar in search applications, search paths typically consist of parallel straight lines. Thus for subsea search applications, our approach ensures that expected performance is no worse than the usual subsea search path, and it might be much better. The methods proposed in this work provide desirable lower-bound guarantees for a single robot as well as teams of robots. Significantly, we demonstrate that existing planning algorithms may be easily adapted to use our proposed methods. We present our theoretical guarantees in the context of subsea search applications and demonstrate the utility of our proposed methods through simulation experiments and field trials using real autonomous underwater vehicles (AUVs). We show that our worst-case guarantees may be achieved despite non-idealities such as sub-optimal short-paths used to construct the longer receding horizon path and unreliable communication in multi-agent planning. In addition to theoretical guarantees, An important contribution of this work is to describe specific implementation solutions needed to integrate and implement these ideas for real-time operation on AUVs. / Doctor of Philosophy / This work is inspired by robotic search applications where a robot or team of robots is equipped with sensors and tasked to autonomously acquire as much information as possible from a region of interest. To accomplish this task, robots must plan paths through the region of interest that maximize the effectiveness of the sensors they carry. Receding horizon path planning is a popular approach to addressing the computationally expensive task of planning long paths because it allows robotic agents with limited computational resources to iteratively construct a long path by solving for an optimal short path, traversing a portion of the short path, and repeating the process until a receding horizon path of the desired length has been constructed. However, receding horizon paths do not retain the optimality properties of the short paths from which they are constructed and may perform quite poorly in the context of achieving the robotic search objective. The primary contributions of this work address the worst-case performance of receding horizon paths by developing methods of using terminal rewards in the construction of receding horizon paths. The methods proposed in this work provide desirable lower-bound guarantees for a single robot as well as teams of robots. We present our theoretical guarantees in the context of subsea search applications and demonstrate the utility of our proposed methods through simulation experiments and field trials using real autonomous underwater vehicles (AUVs). In addition to theoretical guarantees, An important contribution of this work is to describe specific implementation solutions needed to integrate and implement these ideas for real-time operation on AUVs.

We introduce a framework that would enable using autonomous aerial vehicles in search and rescue scenarios associated with missing person incidents to assist human searchers. We formulate a lost person behavior model and a human searcher model informed by data collected from past search missions. These models are used to generate a probabilistic heatmap of the lost person's position and anticipated searcher trajectories. We use Gaussian processes with a Gibbs' kernel for data fusion to accurately model a limited field-of-view sensor. Our algorithm thereby computes a set of trajectories for a team of aerial vehicles to autonomously navigate, so as to assist and complement human searchers' efforts. / Master of Science / Our goal is to assist human searchers using autonomous aerial vehicles in search and rescue scenarios associated with missing person incidents. We formulate a lost person behavior model and a human searcher model informed by data collected from past search missions. These models are used to generate a probabilistic heatmap of the lost person’s position and anticipated searcher trajectories. We use Gaussian processes for data fusion with Gibbs’ kernel to accurately model a limited field-of-view sensor. Our algorithm thereby computes a set of trajectories for a team of aerial vehicles to autonomously navigate, so as to assist and complement human searchers’ efforts.

URL parser and normalization processes are common and important operations in different web frameworks and technologies. In recent years, security researchers have targeted these processes and discovered high impact vulnerabilities and exploitation techniques. In a different approach, we will focus on semantic disconnect among different framework-independent web technologies (e.g., browsers, proxies, cache servers, web servers) which results in different URL interpretations. We coined the term “Path Confusion” to represent this disagreement and this thesis will focus on analyzing enabling factors and security impact of this problem.In this thesis, we will show the impact and importance of path confusion in two attack classes including Style Injection by Relative Path Overwrite (RPO) and Web Cache Deception (WCD). We will focus on these attacks as case studies to demonstrate how utilizing path confusion techniques makes targeted sites exploitable. Moreover, we propose novel variations of each attack which would expand the number of vulnerable sites and introduce new attack scenarios. We will present instances which have been secured against these attacks, while being still exploitable with introduced Path Confusion techniques. To further elucidate the seriousness of path confusion, we will also present the large scale analysis results of RPO and WCD attacks on high profile sites. We present repeatable methodologies and automated path confusion crawlers which detect thousands of sites that are still vulnerable to RPO or WCD only with specific types of path confusion techniques. Our results attest the severity of path confusion based class of attacks and how extensively they could hit the clients or systems. We analyze some browser-based mitigation techniques for RPO and discuss that WCD cannot be dealt as a common vulnerability of each component; instead it arises when an ecosystem of individually impeccable components ends up in a faulty situation.