Recent technological advances in unmanned observational platforms, including remotely operated vehicles (ROVs) and small unmanned aerial systems (sUAS), have made them highly effective tools for research and monitoring within marine and coastal environments. One of the primary types of data collected by these systems is video imagery, which is often captured at an angle oblique to the Earth’s surface, rather than normal to it (e.g., downward looking). This thesis presents a newly developed suite of tools designed to digitally map oblique imagery data collected with ROV and sUAS in coastal and marine environments and quantitatively evaluates the accuracy of the resultant maps. Results indicate that maps generated from oblique imagery collected with unmanned vehicles have highly variable accuracy relative to maps generated with imagery data collected with conventional mapping platforms. These results suggest that resultant maps have the potential to match or even surpass the accuracy of maps generated with imagery data collected with conventional mapping platforms but realizing that potential is largely dependent upon careful survey design.

In recent years, the interest in flying multiple Unmanned Aerial Vehicles (UAVs) in formation has increased. One challenging aspect of achieving this is the relative positioning within the swarm. This thesis evaluates two different methods for estimating the relative position and orientation between two fixed wing UAVs by fusing range measurements from Ultra-wideband (UWB) sensors and orientation estimates from Inertial Measurement Units (IMUs). To investigate the problem of estimating the relative position and orientation using range measurements, the performance of the UWB nodes regarding the accuracy of the measurements is evaluated. The resulting information is then used to develop a simulation environment where two fixed wing UAVs fly in formation. In this environment, the two estimation solutions are developed. The first solution to the estimation problem is based on the Extended Kalman Filter (EKF) and the second solution is based on Factor Graph Optimization (FGO). In addition to evaluating these methods, two additional areas of interest are investigated: the impact of varying the placement and number of UWB sensors, and if using additional sensors can lead to an increased accuracy of the estimates. To evaluate the EKF and the FGO solutions, multiple scenarios are simulated at different distances, with different amounts of changes in the relative position, and with different accuracies of the range measurements. The results from the simulations show that both solutions successfully estimate the relative position and orientation. The FGO-based solution performs better at estimating the relative position, while both algorithms perform similarly when estimating the relative orientation. However, both algorithms perform worse when exposed to more realistic range measurements. The thesis concludes that both solutions work well in simulation, where the Root Mean Square Error (RMSE) of the position estimates are 0.428 m and 0.275 m for the EKF and FGO solutions, respectively, and the RMSE of the orientation estimates are 0.016 radians and 0.013 radians respectively. However, to perform well on hardware, the accuracy of the UWB measurements must be increased. It is also concluded that by adding more sensors and by placing multiple UWB sensors on each UAV, the accuracy of the estimates can be improved. In simulation, the lowest RMSE is achieved by fusing barometer data from both UAVs in the FGO algorithm, resulting in an RMSE of 0.229 m for the estimated relative position.

The implementation and application of Geographic Information Systems (GIS) and spatial analyses have become standard practice in many archaeological projects. In this study, we demonstrate how GIS can play a crucial role in the study of taphonomy, i.e., understanding the processes that underpinned the creation of archaeological deposits, in this case the distribution of artifacts across an archeological site. The Stelida Naxos Archeological Project (SNAP) is focused on the exploration of a Paleolithic-Mesolithic stone tool quarry site located on the island of Naxos, Greece. An extensive pedestrian survey was conducted during the 2013 and 2014 archeological field seasons. An abundance of lithic material was collected across the surface, with some diagnostic pieces dating to more than 250 Kya. Spatial statistical analysis (Empirical Bayesian Kriging) was conducted on the survey data to generate predictive distribution maps for the site. This study then determined the contextual integrity of the surface artifact distributions through a study of geomorphic processes. A digital surface model (DSM) of the site was produced using Unmanned Aerial Vehicle (UAV) aerial photography and Structure from Motion (SfM) terrain modeling. The DSM employed to develop a Revised Universal Soil Loss Equation (RUSLE) model and hydrological flow models. The model results provide important insights into the site geomorphological processes and allow categorization of the diagnostic surface material locations based on their contextual integrity. The GIS analysis demonstrates that the surface artifact distribution has been significantly altered by post-depositional geomorphic processes, resulting in an overall low contextual integrity of surface artifacts. Conversely, the study identified a few areas with high contextual integrity, loci that represent prime locations for excavation. The results from this study will not only be used to inform and guide further development of the archeological project (as well as representing significant new data in its own right), but also contributes to current debates in survey archaeology, and in mapping and prospection more generally. This project demonstrates the benefit of using spatial analysis as a tool for planning of pedestrian surveys and for predictive mapping of artifact distributions prior to archaeological excavations. / Thesis / Master of Science (MSc)

UAVs have gained in popularity and the number of applications has soared over the past years, ranging from leisure to commercial activities. This thesis is discussing specifically railroad applications, which is a domain rarely explored. Two different aspects are analysed. While developing a new application or migrating a ground-based system to UAV platform, the different challenges encountered are often unknown. Therefore, this thesis highlights the most important ones to take into consideration during the development process. From a more technical aspect, the implementation of autonomous guidance for UAVs over railroads using vanishing point extraction is studied. Two different algorithms are presented and compared, the first one is using line extraction method whereas the second uses joint activities of Gabor filters. The results demonstrate that the applied methodologies provide good results and that a significant difference exists between both algorithms in terms of computation time. A second implementation tackling the detection of railway topologies to enable the use on multiple rail road configurations is discussed. A first technique is presented using exclusively vanishing points for the detection, however, the results for complex images are not satisfactory. Therefore, a second method is studied using line characteristics on top of the previous algorithm. This second implementation has proven to give good results.

As Unmanned Aerial Vehicles (UAVs) are getting more popular and their area of use is expanding rapidly, the security aspect becomes important to investigate. This thesis is a systematic literature review that examines which type of cyber attacks are most common among attacks directed at civilian use UAVs and what consequences they bring. All cyber attacks presented in the report are categorized using the STRIDE threat model, which risk they pose and what equipment is required for the adversary to follow through with the attack. The findings are that Spoofing and Denial of Service attacks are the most common cyber attack types against UAVs and that hijacking and crashing are the most common results of the attacks. No equipment that is difficult to access is required for either of the attack types in most cases, making the result an indicator that the security state for civilian use UAVs today needs improving. / Obemannade luftburna farkoster (OLF) blir mer vanliga allteftersom deras användningsområde utökas, vilket innebär att cybersäkerhetsaspekten behöver studeras. Detta arbete är en systematisk litteraturstudie som undersöker vilka typer av cyberattacker riktade mot drönare som är vanligast och vilka risker de medför. Attackerna i rapporten är kategoriserade med hjälp av metoden STRIDE samt efter vilka mål attackerna haft och vilken utrustning som krävs. Resultatet är att Spoofing och Denial of Service-attacker är vanligast och att de medför att attackeraren kan kapa eller krascha drönaren. Ingen svåråtkomlig utrustning krävs för någon av dessa attacktyper vilket indikerar att säkerhetsläget för civila drönare behöver förbättras.

Conservation of elasmobranch populations is often inhibited by a lack of data, particularly in understudied regions like the Red Sea. Survey efforts in this region have been infrequent and often highly localized. Establishing a broad baseline for elasmobranch diversity and abundance along the Saudi Arabian Red Sea coast could inform both conservation efforts and a nascent ecotourism industry. In this thesis, I describe a pilot study comparing biodiversity data from baited remote underwater video stations (BRUVS), unoccupied aerial vehicle surveys (UAVs), and eDNA sequencing at five islands in the Farasan Banks region of the Saudi Arabian Red Sea. Estimates of relative abundance were also compared between the BRUVS and UAVs. Each method identified species missed by the other two, but all three techniques exhibited clear habitat- and taxa-specific biases. I was able to identify key concerns for each approach that need to be addressed before large-scale implementation. If carefully planned and executed well, a full assessment of the Saudi Arabian coastline could establish a true baseline for shallow water elasmobranchs in the eastern Red Sea. Informing best conservation practices and identifying potential ecological attractions in accordance the environmental and economic goals of Saudi Arabia’s Vision 2030.

As unmanned air vehicle (UAV) utilization increases in Wilderness Search and Rescue (WiSAR) efforts, onboard sensors yielding more information will be desired. UAVs can assist WiSAR efforts by accelerating the ground search process through returning quality aerial footage of the terrain. Additionally, tracking the progress of a search by populating a digital map with video resolution data increases confidence that a comprehensive search of the region has been made. This thesis presents methods for acquiring video from multiple video sensors and fusing them into a single rendered video stream as a Virtual Gimbal. The panoramic video stream is the first of its kind to be constructed from video transmissions from a small UAV, and the first known video panorama to be used to quickly survey a region within a WiSAR context. The Virtual Gimbal comprises two video transmissions from a three camera array mounted in a downward-looking configuration on a UAV. This video stream has been shown to decrease the amount of time required to thoroughly survey a region by more than 40 percent.

Unmanned Aerial Vehicles (UAVs) equipped with lightweight, inexpensive cameras have grown in popularity by enabling new uses of UAV technology. However, the video retrieved from small UAVs is often unwatchable due to high frequency jitter. Beginning with an investigation of previous stabilization work, this thesis discusses the challenges of stabilizing UAV based video. It then presents a software based computer vision framework and discusses its use to develop a real-time stabilization solution. A novel approach of estimating intended video motion is then presented. Next, the thesis proceeds to extend previous target localization work by allowing the operator to easily identify targets—rather than relying solely on color segmentation—to improve reliability and applicability in real world scenarios. The resulting approach creates a low cost and easy to use solution for aerial video display and target localization.

With the recent development of reliable autonomous technologies for small unmanned air vehicles (UAVs), the algorithms utilizing teams of these vehicles are becoming an increasingly important research area. Unfortunately, there is no unified framework into which all (or even most) cooperative control problems fall. Five factors that affect the development of cooperative control algorithms are objective coupling, communication, completeness, robustness, and efficiency. We classify cooperative control algorithms by these factors and then present three algorithms with application to target and perimeter surveillance and a method for decentralized algorithm design. The primary contributions of this research are the development and analysis of decentralized algorithms for perimeter and target surveillance. We pose the cooperative perimeter surveillance problem and offer a decentralized solution that accounts for perimeter growth (expanding or contracting) and insertion/deletion of team members. By identifying and sharing the critical coordination information and by exploiting the known communication topology, only a small communication range is required for accurate performance. Convergence of the algorithm to the optimal configuration is proven to occur in finite-time. Simulation and hardware results are presented that demonstrate the applicability of the solution. For single target surveillance, a team of UAVs angularly spaced (i.e. in the splay state configuration) provides the best coverage of the target in a wide variety of circumstances. We propose a decentralized algorithm to achieve the splay state configuration for a team of UAVs tracking a moving target and derive the allowable bounds on target velocity to generate a feasible solution as well as show that, near equilibrium, the overall system is exponentially stable. Monte Carlo simulations indicate that the surveillance algorithm is asymptotically stable for arbitrary initial conditions. We conclude with high fidelity simulation and actual flight tests to show the applicability of the splay state controller to unmanned air systems.

The design, analysis, construction and flight testing of a miniature Vertical Take-Off and Landing (VTOL) tail-sitter Unmanned Aerial Vehicle (UAV) prototype is presented in detail. Classic aircraft design methods were combined with numerical analysis to estimate the aircraft performance and flight characteristics. The numerical analysis employed a propeller blade-element theory coupled with momentum equations to predict the influence of a propeller slipstream on the freestream flow field, then the aircraft was analyzed using 3-D vortex lifting-line theory to model finite wings immersed in the flow field. Four prototypes were designed, built, and tested and the evolution of these prototypes is presented. The final prototype design is discussed in detail. A method for sizing control surfaces for a tail-sitter was defined. The final prototype successfully demonstrated controllability both in horizontal flight and vertical flight. Significant contributions included the development of a control system that was effective in hover as well as descending vertical flight, and the development of a strong but light weight airframe. The aircraft had a payload weight fraction of 14.5% and a maximum dimension of one meter, making it the smallest tail-sitter UAV to carry a useful payload. This project is expected to provide a knowledge base for the future design of small electric VTOL tail-sitter aircraft and to provide an airframe for future use in tail-sitter research.