To gain an understanding about what information requirements there are for future operators of autonomous drone swarms at airport, this study examined how work at airports is structured today, what staff at airports think about the potential implementation of autonomous drones, and what potential interface components that are necessary to be able to control these drones. Interviews and observations were made at five different airports and air traffic control towers with tower and ground staff. Based on the collected data a Cognitive Work Analysis (CWA) was made to understand the domain and a Thematic Analysis (TA) was made to understand the ideas regarding the drones. Finally, a workshop with fellow researchers was made to generate ideas about solutions and interface requirements, which was analyzed together with the CWA and TA. It was concluded that the main values of the system are to maintain Situational Awareness, Avoid collisions, and Keep time in order to ensure safe flight traffic. A major aspect to make this possible is communication. This was also a main factor for the implementation of autonomous drones, where it was considered important to know what the drones are doing and where they are. However, it was not always necessary for every person at the airport to have complete oversight and control over the drone, but having the possibility to gain that information was central. Having overview of the drones’ present activities, being able to see what they had done, being able to control them directly, and getting notified about relevant information were interface requirements identified. It was concluded that despite there being some constraints and challenges to implementing autonomous drones at airports, there are aspects that can be taken into account and information to be presented in the right way for the future operator to enable implementation of the autonomous drones at airports.

Examensarbetet presenterat i den här rapporten utfördes i syfte att kartlägga vilka sensorer och metoder som skulle kunna vara tillämpbara inom kartläggning av snölager i den arktiska regionen. För att göra den bedömningen har flera olika metoder använts. Intervjuer med experter inom relevanta områden hölls för att göra en initial kartläggning av vilken information som kan tänkas ligga till grund för det efterföljande steget, en kvantitativ regressionsanalys baserad på information insamlad genom systematisk analys av plattformar och sensorer som används i kartläggning baserad på fjärranalys. I denna analys identifierades relevanta korrelationsområden vilka sedan kunde analyseras närmare i en ’State of the art’-sammanställning av relevanta publikationer för att ge en rättvisande insyn i vilka metoder som finns tillgängliga och vad deras respektive styrkor och svagheter är. Slutligen genomfördes en flygning i syfte att praktiskt bedöma vilken kvalitet man kan uppnå i snökartläggning med enkelt tillgängliga konsumentprodukter. Resultaten pekar på att snökartläggning med drönare är ett lovande komplement till vanliga, manuella mätningar. Rekommendationer för vidare studier inkluderar komponentspecifika undersökningar, plattformsdesign och marknadskartläggning. / The thesis presented in this report was conducted to identify which sensors and methods could be applicable in the mapping of snow stocks in the Arctic region. To make that assessment, several different methods have been used. Interviews with experts in relevant areas were held to make an initial survey of what information might be the basis for the subsequent step, a quantitative regression analysis based on information collected through systematic review of the use of platforms and sensors in mapping by remote sensing. In this analysis, relevant correlation areas were identified, which could then be further analyzed in a state of the art compilation of relevant publications to provide a fair understanding of what methods are available and what their respective strengths and weaknesses are. Finally, a flight was carried out with a view to practically assessing what quality can be achieved in snow mapping with readily available consumer products. The results point out that snow mapping exploration with drones is a promising complement to manual measurements. Recommendations for further studies include component-specific surveys, platform design and market mapping.

No description available.

Peanut digging timing is difficult to predict due to indeterminate growth and peanut pods maturing underground, resulting in the need to research methods that provide consistent measurements, while reducing time and effort for farmers and researchers. Experiments were conducted to evaluate the accuracy of the Maturity Index 1 and Maturity Index 2 in predicting peanut grade, the accuracy of the North Carolina 2 degree day method in predicting peanut yield, and remote sensing vegetative indices sensitivity equivalence (SEq) to peanut Maturity Index 2 and harvest grade (TSMK) for cultivars IPG-914 and Georgia-06G in Mississippi. Maturity Index 1 and Maturity Index 2 were found to be inaccurate predictions of peanut grade in Mississippi, suggesting a need to examine the contributions of individual color classes in new genotypes to predict grade and yield. The North Carolina 2 degree day method was found to have a moderate to strong relationship with yield, indicating its potential usefulness in determining digging timing. Results also showed red edge indices were more sensitive to changes in pod maturity and grade. Peanut genotype selection is critical for maximizing peanut grade and yield on farm. Experiments were conducted to evaluate 32 genotypes for maturity, grade, and yield. Several early maturing genotypes showed promise for improving yield and grade without reducing quality, particularly 'UF11x23-3-6-1-1', '16-1-2147', '16-1-2142', '14x029-1-5-1-1', and '14x022-1-2-1-2'. The results suggest earlier maturing genotypes may be a solution to the late-season harvest risk of crop loss due to poor digging conditions, rain, and frost, while maintaining similar pod grades and yield to the current market-leading cultivars. The findings of this study contribute to the ongoing effort to optimize digging timing and improve peanut yields in Mississippi, where peanut farmers face the dual challenges of climatic variability and genotype selection. Future research is needed to examine the adaptability of genotypes on differing soil types, management, and climates throughout Mississippi. Overall, this study highlights the need for more effective and accurate methods for determining digging timing in peanut crops, which is crucial for their grade, and yield.

No description available.

This thesis explores the ethical and legal implications of targeted killing operations employingCombat Unmanned Aerial Vehicles (CUAVs), focusing on the practices of the United Statesand Israel. Grounded in Just War Theory and international law, the research critically examineshow both nations justify these operations under the principles of self-defence and nationalsecurity. Through a comparative analysis of specific case studies, such as the assassinations ofQasem Soleimani and Baha Abu Al Ata, the study highlights the complexities of balancingsecurity imperatives with adherence to international humanitarian and human rights laws. Thefindings reveal significant ethical tensions, particularly concerning the principles ofproportionality, distinction, and the risk of extrajudicial killings. The lack of transparency andaccountability in drone operations further complicates their legitimacy. Additionally, itadvocates for comprehensive policy and legal reforms to enhance oversight and regulation,ensuring compliance with international standards and ethical norms. This research aims tocontribute to the ongoing discourse on modern military practices, urging a more just andaccountable framework for the use of lethal force in contemporary conflicts.

The proliferation of reusable space vehicles has fundamentally changed how we inject assets into orbit and beyond, increasing the reliability and frequency of launches. Leading to the rapid development and adoption of new technologies into the Aerospace sector, such as computer vision (CV), machine learning (ML), and distributed networking. All these technologies are necessary to enable genuinely autonomous decision-making for space-borne platforms as our spacecraft travel further into the solar system, and our missions sets become more ambitious, requiring true ``human out of the loop" solutions for a wide range of engineering and operational problem sets. Deployment of systems proficient at classifying, tracking, capturing, and ultimately manipulating orbital assets and components for maintenance and assembly in the persistent dynamic environment of space and on the surface of other celestial bodies, tasks commonly referred to as On-Orbit Servicing and In Space Assembly, have a unique automation potential. Given the inherent dangers of manned space flight/extravehicular activity (EVAs) methods currently employed to perform spacecraft construction and maintenance tasking, coupled with the current limitation of long-duration human flight outside of low earth orbit, space robotics armed with generalized sensing and control machine learning architectures is a tremendous enabling technology. However, the large amounts of sensor data required to adequately train neural networks for these space domain tasks are either limited or non-existent, requiring alternate means of data collection/generation. Additionally, the wide-scale tools and methodologies required for hardware in the loop simulation, testing, and validation of these new technologies outside of multimillion-dollar facilities are largely in their developmental stages. This dissertation proposes a novel approach for simulating space-based computer vision sensing and robotic control using both physical and virtual reality testing environments. This methodology is designed to both be affordable and expandable, enabling hardware in the loop simulation and validation of space systems at large scale across multiple institutions. While the focus of the specific computer vision models in this paper are narrowly focused on solving imagery problems found on orbit, this work can be expanded to solve any problem set that requires robust onboard computer vision, robotic manipulation, and free flight capabilities. / Doctor of Philosophy / The lack of real-world imagery of space assets and planetary surfaces required to train neural networks to autonomously identify, classify, and perform decision-making in these environments is either limited, none existent, or prohibitively expensive to obtain. Leveraging the power of the unreal engine, motion capture, and theatre projections technologies combined with robotics, computer vision, and machine learning to provide a means to recreate these worlds for the purpose of optical machine learning testing and validation for space and other celestial applications. This dissertation also incorporates domain randomization methods to increase neural network performance for the above mentioned applications.

<p>Modern mobile mapping systems (MMSs)  -- such as Uncrewed Aerial Vehicles (UAVs), backpack systems, Unmanned Ground Vehicles (UGVs), and wheel-based systems -- equipped with imaging/ranging modalities and navigation units -- i.e., integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) -- have emerged as promising platforms due to their ability to conduct fine spatial/temporal resolution mapping at a reasonable cost. The integration of camera and LiDAR data acquired by these MMSs can result in an accurate and comprehensive description of the object space, due to their complementary characteristics. Meaningful integration of multi-temporal data/products from different modalities onboard single or multiple systems is contingent on their positional quality. The objective of this dissertation is to develop strategies that enable the derivation of accurately georeferenced data from LiDAR and camera units onboard UAVs and backpack systems across diverse mapping environments. To do so, accurate system calibration parameters -- including the sensor's interior orientation parameters (IOP) and mounting parameters relating the sensors to the INS's Inertial Measurement Unit (IMU) body frame -- and trajectory information need to be derived.</p> <p><br></p> <p>In this dissertation, to resolve the issues that arose from unstable IOP of consumer-grade camera onboard a GNSS/INS-assisted UAV, a LiDAR-aided camera IOP refinement strategy is first proposed. Additionally, in a more general case where system calibration is required for both camera and LiDAR units onboard single or multiple GNSS/INS-assisted UAV(s), an automated, tightly-coupled camera/LiDAR integration workflow through simultaneous system calibration and trajectory refinement is developed. While UAVs typically operate in open sky conditions, conducting in-canopy mapping using backpack systems for forest inventory applications is significantly affected by GNSS signal outages induced by the canopy cover. To derive accurate trajectory information in such scenarios, a system-driven strategy for trajectory enhancement and mounting parameters refinement of UAV and backpack LiDAR systems in forest applications is developed. Furthermore, considering that this approach requires an initial trajectory with limited drift errors, the Simultaneous Localization and Mapping (SLAM) technique is adopted to directly derive the trajectory information. Specifically, a comprehensive forest feature-based (i.e., tree trunks and ground) LiDAR SLAM framework using 3D LiDAR mounted on backpack systems is developed. These proposed strategies are tested using multiple datasets from UAV and backpack mobile mapping systems. Experimental results verify that the proposed approaches successfully derive accurate system calibration parameters and trajectory information, and consequently well-aligned multi-system, multi-temporal, multi-sensor data with high relative/absolute accuracy.</p>

<p dir="ltr">This thesis adapts and applies methodologies for optimizing the sensing topology of a counter-UAS (CUAS) network to the problem of optimizing the geospatial distribution of emergency response drone bases subject to resource limitations while ensuring alignment with emergency response requirements. The specific context for this work is a 911 call incident response.</p><p dir="ltr">Drone response time, time on scene, and sensor effectiveness are used as network performance metrics to develop a mission planning algorithm that attempts to maximize network response effectiveness. A composite objective function utilizes network response effectiveness and customer-defined region weights that indicate the probability of an incident occurring to represent the performance of the geospatial distribution of 911 drone bases. A Greedy Algorithm iterates upon this objective function to optimize the network topology.</p><p dir="ltr">Previous work [1] suggests that a heuristic based approach utilizing a hexagonal network topology centered around suburban/urban focal points is the preferred method for optimizing the dispatch topology of a 911 response drone network. The optimization strategy deployed here demonstrated an 11% improvement on the objective function compared to this heuristic when tested in Tippecanoe County, IN.</p><p dir="ltr">Previous work [2] also suggests that, of all drones in the design space compliant with FAA Part 107, a single Vertical Take-off and Landing (VTOL) type drone with an ability to transition into fixed wing horizontal flight adhering to specific performance requirements is the preferred drone for executing the emergency response mission. This thesis utilizes the optimization strategy deployed here to test this supposition by comparing the performance of a network with access to only this single drone type to a network with access to multiple types of fixed-wing VTOL drones. Findings indicate that access to only the single type of optimally-sized drone outperforms a network with access to multiple drone types; however, improvements to the greedy algorithm that consider the marginal value of each drone type and across diverse mission types may modify this conclusion.</p>

L'emploi de véhicules aériens sans pilote pour surveiller l'environnement entourant les navires militaires est une avenue intéressante pour contrecarrer des menaces potentielles. Cet ouvrage présente le banc d'essais développé pour l'évaluation et l'analyse comparative d'algorithmes de surveillance de cible. Un système vidéo commercial de poursuite de cible a été intégré à un système "hardware in the loop" (HIL) d'avion sans pilote afin de retrouver la position d'une cible dans un environnement virtuel 3D. La démarche pour l'évaluation de la position de la cible est présentée. Le système HIL utilisé, les modifications matérielles et logicielles apportées ainsi que la performance du système sont décrits. Des algorithmes de surveillance allant des plus simples comme la navigation circulaire aux plus complexes basés sur la commande prédictive sont présentés, simulés sur le système HIL et comparés. Les résultats de cette analyse permettent d'établir les lignes directrices d'une stratégie de guidage efficace.