Over the last few years, the concept of civil Unmanned Aircraft System(s) (UAS) has been realised, with small UASs commonly used in industries such as law enforcement, agriculture and mapping. With increased development in other areas, such as logistics and advertisement, the size and range of civil UAS is likely to grow. Taken to the logical conclusion, it is likely that large scale UAS will be operating in civil airspace within the next decade. Although the airborne operations of civil UAS have already gathered much research attention, work is also required to determine how UAS will function when on the ground. Motivated by the assumption that large UAS will share ground facilities with manned aircraft, this thesis describes the preliminary development of an Automated Taxiing System(ATS) for UAS operating at civil aerodromes. To allow the ATS to function on the majority of UAS without the need for additional hardware, a visual sensing approach has been chosen, with the majority of work focusing on monocular image processing techniques. The purpose of the computer vision system is to provide direct sensor data which can be used to validate the vehicle s position, in addition to detecting potential collision risks. As aerospace regulations require the most robust and reliable algorithms for control, any methods which are not fully definable or explainable will not be suitable for real-world use. Therefore, non-deterministic methods and algorithms with hidden components (such as Artificial Neural Network (ANN)) have not been used. Instead, the visual sensing is achieved through a semantic segmentation, with separate segmentation and classification stages. Segmentation is performed using superpixels and reachability clustering to divide the image into single content clusters. Each cluster is then classified using multiple types of image data, probabilistically fused within a Bayesian network. The data set for testing has been provided by BAE Systems, allowing the system to be trained and tested on real-world aerodrome data. The system has demonstrated good performance on this limited dataset, accurately detecting both collision risks and terrain features for use in navigation.

A Tilt-Wing Unmanned Aerial Vehicle (TW-UAV) and the preliminary evaluation of its hovering characteristics in extended simulation studies and experiments are presented in this Master Thesis. In the beginning, an overview of the TW-UAV's design properties are established, highlighting the novelties of the proposed structure and the overall merits. The TW-UAV's design and structural properties are mathematically modelled and utilized for the synthesis of a cascaded P-PI and PID based control structure for the regulation of its hovering performance. In addition, extensive simulation trials are performed in order to evaluate the structure's efficiency in controlling the TW-UAV's attitude and position under various noise and disturbance scenarios. The model and aircraft are then put through experimental evaluation with an on-board processor, namely the KFly, in a Motion-capture equipped laboratory to evaluate the control structure and physical behaviour of the TW-UAV. The results of these experiments are presented and discussed. The system and control scheme are shown to work well. However, an unfortunate crash forced the premature termination of experimentation and thus the conclusion of this thesis. Nevertheless, the reason for the crash is understood and discussed for future work.

The operation of sophisticated unmanned aircraft systems (UAS) involves complex interactions between human and machine. Unlike other areas of aviation where technological advancement has flourished to accommodate the modernization of the National Airspace System (NAS), the scientific paradigm of UAS and UAS user interface design has received little research attention and minimal effort has been made to aggregate accurate data to assess the effectiveness of current UAS human-machine interface (HMI) representations for command and control. UAS HMI usability is a primary human factors concern as the Federal Aviation Administration (FAA) moves forward with the full-scale integration of UAS in the NAS by 2025. This study examined system learnability of an industry standard UAS HMI as minimal usability data exists to support the state-of-the art for new and innovative command and control user interface designs. This study collected data as it pertained to the three classes of objective usability measures as prescribed by the ISO 9241-11. The three classes included: (1) effectiveness, (2) efficiency, and (3) satisfaction. Data collected for the dependent variables incorporated methods of video and audio recordings, a time stamped simulator data log, and the SUS survey instrument on forty-five participants with none to varying levels of conventional flight experience (i.e., private pilot and commercial pilot). The results of the study suggested that those individuals with a high level of conventional flight experience (i.e., commercial pilot certificate) performed most effectively when compared to participants with low pilot or no pilot experience. The one-way analysis of variance (ANOVA) computations for completion rates revealed statistical significance for trial three between subjects [F (2, 42) = 3.98, p = 0.02]. Post hoc t-test using a Bonferroni correction revealed statistical significance in completion rates [t (28) = -2.92, p<0.01] between the low pilot experience group (M = 40%, SD =. 50) and high experience group (M = 86%, SD = .39). An evaluation of error rates in parallel with the completion rates for trial three also indicated that the high pilot experience group committed less errors (M = 2.44, SD = 3.9) during their third iteration when compared to the low pilot experience group (M = 9.53, SD = 12.63) for the same trial iteration. Overall, the high pilot experience group (M = 86%, SD = .39) performed better than both the no pilot experience group (M = 66%, SD = .48) and low pilot experience group (M = 40%, SD =.50) with regard to task success and the number of errors committed. Data collected using the SUS measured an overall composite SUS score (M = 67.3, SD = 21.0) for the representative HMI. The subscale scores for usability and learnability were 69.0 and 60.8, respectively. This study addressed a critical need for future research in the domain of UAS user interface designs and operator requirements as the industry is experiencing revolutionary growth at a very rapid rate. The deficiency in legislation to guide the scientific paradigm of UAS has generated significant discord within the industry leaving many facets associated with the teleportation of these systems in dire need of research attention. Recommendations for future work included a need to: (1) establish comprehensive guidelines and standards for airworthiness certification for the design and development of UAS and UAS HMI for command and control, (2) establish comprehensive guidelines to classify the complexity associated with UAS systems design, (3) investigate mechanisms to develop comprehensive guidelines and regulations to guide UAS operator training, (4) develop methods to optimize UAS interface design through automation integration and adaptive display technologies, and (5) adopt methods and metrics to evaluate human-machine interface related to UAS applications for system usability and system learnability.

After a huge amount of success within the military, the benefits of the use of unmanned aerial systems over manned aircraft is obvious. They are becoming cheaper and their functions advancing to such a point that there is now a large drive for their use by civilian operators. However there are a number of significant challenges that are slowing their inevitable integration into the national airspace systems of countries. A large array of emergency situations will need to be dealt with autonomously by contingency management systems to prevent potentially deadly incidences. One such emergency situation that will need autonomous intervention, is the total loss of thrust from engine failure. The complex multi faceted task of landing the stricken aircraft at a potentially unprepared site is called a forced landing. This thesis presents methods to address a number of critical parts of a forced landing system for use by an unmanned aerial system. In order for an emergency landing site to be considered, it needs to be within glide range. In order to find a landing site s reachability from the point of engine failure the aircraft s glide performance and a glide path must be known. A method by which to calculate the glide performance, both from aircraft parameters or experiments is shown. These are based on a number of steady state assumptions to make them generic and quick to compute. Despite the assumptions, these are shown to have reasonable accuracy. A minimum height loss path to the landing site is defined, which takes account of a steady uniform wind. While this path is not the path to be flown it enables a measure of how reachable a landing site is, as any extra height the aircraft has once it gets to the site makes a site more reachable. It is shown that this method is fast enough to be run online and is generic enough for use on a range of aircraft. Based on identified factors that make a landing site more suitable, a multi criteria decision making Bayesian network is developed to decide upon which site a unmanned aircraft should land in. It can handle uncertainty and non-complete information while guaranteeing a fast reasonable decision, which is critical in this time sensitive situation. A high fidelity simulation environment and flight test platform are developed in order to test the performance of the developed algorithms. The test environments developed enable rapid prototyping of algorithms not just within the scope of this thesis, but on a range of vehicle types. In simulation the minimum height loss paths show good accuracy, for two completely different types of aircraft. The decision making algorithms show that they are capable of being ran online in a flight test. They make a reasonable decision and are capable of quickly reacting to changing conditions, enabling redirection to a more suitable landing site.

This Master´s Thesis Usage of drones in the Logistics of the company SKODA AUTO a.s. reacts to the current trend of unmanned aerial vehicles (drones) which are being used in many areas. Aim of this thesis is to analyse laws which regulate the operation of drones in selected countries, to assess suitability of drones for specific process of inventory of empty containers in the company SKODA AUTO a.s. and to suggest an alternative solution for this process. Thesis investigates usage of drones in the Logistics mainly for goods delivery and stock inventory. Mass drone delivery is currently not feasible due to many problems and challenges. Stock inventory has become a reality. Analysis of legislation is focused on EU, Czech Republic, Germany and China. Technology for inventory of empty containers using drones in SKODA AUTO a.s. has a long payback period (21 years) and author does not recommend drones for this process based on this criterion. As an alternative solution, the technology designed at AUDI company is recommended (camera placed on forklift and software for automated container identification).

<div>The National Aeronautics and Space Administration (NASA) has a vision for Advanced Air Mobility (AAM) based on safely introducing aviation services to missions that were previously not served or under-served. Many potential AAM missions lie in metropolitan areas that are beset by various types of uncertainty and potential constraints. Radio interference from other electronic devices can render unreliable communication between flying vehicles to ground operators. Buildings have irregular surfaces that degrade GPS localization performance. Skyscrapers can induce spontaneous turbulence that degrades vehicles' navigational accuracy. However, the potential market demands for aerial passenger-carrying and package delivery services have attracted investments. For example, Google WingX, Amazon Prime Air, and Joby Aviation are well-known companies developing AAM systems and services. If the market visions are realized, how will safety be assessed and maintained with high-density AAM operations?</div><div><br></div><div>While there are multiple technology candidates for realizing high-density AAM operations in urban environments, the means to accomplish the requisite first step of assessing the airspace safety of an integrated AAM eco-system from the candidate technologies is crucial but as yet unclear. This dissertation proposes an entropy-based framework for assessing the airspace safety level for low-altitude airspace in an AAM setting. The framework includes a conceptual model for depicting the information flows between air vehicles and an air traffic authority (ATA) and the use of a probability distribution to represent the traffic state. Subsequently, the framework embeds three airspace-level metrics for assessing airspace safety and uncertainty levels. The traffic safety severity metric quantifies the traffic safety level. The traffic entropy quantifies the uncertainty level of the traffic state distribution. Finally, the temperature is the ratio of the traffic safety severity to the traffic entropy. The temperature is similar to the traffic safety severity but gives a higher weight to the instance with a safe traffic state. </div><div><br></div><div>Simulation studies show that the combined use of the three metrics can evaluate relative airspace safety levels even if the unsafe conditions do not occur. The use cases include using the metrics for real-time airspace safety level monitoring and comparing the design of airspace systems and operational strategies. Additionally, this study demonstrates using a heat map to visualize vehicle-level metrics and assess designs of UAM airspace structures. The contribution of this study includes two parts. First, the temperature metric can heuristically assess a probability function. Based on the definition of the cost function, the temperature metric gives a higher weighting to the instance of the probability function with a lower cost value. This study constructs several triggers for predicting if a near-miss event would happen in the airspace. The temperature-based trigger has a better prediction accuracy than the cost-function-based trigger. Secondly, the temperature can visualize the safety level of an airspace structure with the considerations of the environmental and vehicle state measurement uncertainty. The locations with high-temperature values indicate that the regions are more likely to have endangered vehicles. Although this framework does not provide any means of resolving the unsafe conditions, it can be powerful in the comparison of different airspace design concepts and identify the weaknesses of either airspace design or operational strategies. </div>

Safety is the top priority for the aviation industry and a safe airport environment is essential to aviation safety. However, due to the increasing prevalence of UAS in recent years, UAS sightings have become a potential threat to airports. When UAS appear in the vicinity of airports, they bring safety concerns and result in negative operational and economic impacts on airports. Since the FAA’s mission is to provide the safest and most efficient aerospace system in the world, further research regarding the threat of UAS sightings to airports is needed. The purpose of this study is to investigate the threat of UAS to airports and in the national airspace system (NAS). This study includes three primary components: the analysis of 6,551 Federal Aviation Administration (FAA) UAS sighting reports, a case study of the impacts of the UAS sighting at Newark Liberty International Airport (EWR) on January 22, 2019, and a synthesis of airport operator perspectives based on interviews with airport personnel at five airports. The analysis of UAS sighting reports shows the characteristics of UAS sightings, the case study on EWR UAS illustrates the impact of the UAS sighting at the airport, and interview results illustrate the current perspective of airport operators regarding the risk of UAS. Along with the results, the scientific methods of identifying and analyzing the characteristics of UAS sightings in controlled airspace close to airports could be used by researchers to study UAS sightings in the future. Findings from this study may be beneficial to multiple stakeholders, including airport personnel, regulators, entrepreneurs, and vendors in the aviation industry. <br>

Various remote sensing methods are being utilized to assess fundamental properties of forest stands, such as the height of trees, already for decades. Nevertheless, the use of Unmanned Aerial Vehicles in the combination with Structure from Motion software for these purposes, experiences its boom right now and thus not many pertinent studies exist yet. Such system producing image-based point clouds was utilized in this work to gain the canopy elevation data. To identify individual trees and to extract their height from these remote sensing data, a unique software tool called 'UAV Forest Explorer' was developed. Twenty forest study plots was established to acquire the field measurements ground truth data about almost 1 500 trees to thoroughly test the tool and calculate demanded statistics. The research proved, that the tool is fully applicable on all types of forest stands.

Great Basin bristlecone pine (Pinus longaeva D.K. Bailey) is a keystone species of the subalpine forest in the Great Basin and western Colorado Plateau ecoregions in Utah, Nevada, and California. Bristlecone pine is also the world's longest-lived non-clonal organism, with individuals occasionally reaching ages up to 5,000 years old. Because of its longevity, bristlecone pine contains an important proxy record of climate data in its growth rings. Despite its ecological and scientific importance, bristlecone pine's distribution and associated environmental drivers are poorly understood. Geospatial technologies, including unmanned aircraft systems (UAS), remote sensing, geographic information systems (GIS), and spatial modeling techniques can be used to quantify and characterize biotic and abiotic factors that constrain the fundamental and realized niches of bristlecone pine and other subalpine forest species. In Chapter 1, we describe workflows and important technical and logistical considerations for collecting aerial imagery in mountainous areas using small UAS, enabling high-quality remotely sensed datasets to be assembled to study the ecology of subalpine forests. In Chapter 2, we discuss a unique outlier population of bristlecone pine found in the Stansbury Mountains, Utah. We used GIS to delineate boundaries for five small stands of bristlecone pine and examined two competing hypotheses that could explain the species' presence in the range: 1) that the current population is a relict from the Pleistocene, or 2) that long-distance dispersal mechanisms led to bristlecone pine's migration from other mountain ranges during or after the warming period of the Pleistocene/Holocene transition. Potential migration routes and barriers to migration were considered in our effort to understand the dynamics behind the presence of this unique disjunct population of bristlecone pine. Chapter 3 describes a comprehensive mapping effort for bristlecone pine across its entire distribution. Using data from historic maps, vegetation surveys, herbarium records, and an online ecological database, we compiled nearly 500 individual map polygons in a public-facing online GIS database representing locations where bristlecone pine occurs. Using these occurrence data, we modeled the suitable habitat of the species with Maximum Entropy (MaxEnt), examining the relative importance of 60 environmental variables in constraining the species distribution. A probability map was generated for bristlecone pine, and the environmental variables were ranked in order of their predictive power in explaining the species distribution. We found that January mean dewpoint temperature and February precipitation explained over 80% of the species distribution according to the MaxEnt model, suggesting that the species favors drier air conditions and increased snowfall during winter months. These three studies demonstrate that geospatial tools can be effectively used to quantify and characterize the habitat of bristlecone pine, leading to improved management and conservation of the species in the face of multiple threats, including mountain pine beetle (MPB), white pine blister rust (WPBR), and possible habitat constriction due to climate change.

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