Thesis (Master of Military Studies)-Marine Corps Command and Staff College, 2008. / Title from title page of PDF document (viewed on: Feb 11, 2010). Includes bibliographical references.

Les travaux de recherches sur la commande, le diagnostic et la tolérance aux défauts appliqués aux drones deviennent de plus en plus populaires. Il est judicieux de concevoir des lois de commande qui garantissent la stabilité et les performances du drone, non seulement dans le cas nominal, mais également en présence de fortes perturbations et de défauts.Dans cette thèse, un nouvel algorithme bio-inspiré adapté pour la recherche de solutions dans des problèmes d’optimisation est développé. Cet algorithme est utilisé pour trouver les gains des différents contrôleurs conçus pour les drones. La commande par mode glissant est utilisée pour développer deux contrôleurs passifs tolérants aux défauts pour les quadrirotors: un contrôleur par mode glissant augmentée avec un intégrateur, et un contrôleur par mode glissant implémenté en cascade. Parce que les commandes passives ont une robustesse réduite, une commande active par mode glissant est développée. Pour traiter les défauts extrêmes, un contrôleur d’urgence basé sur la conversion du quadrirotor en trirotor est développé. Les commandes actives, passives, et le contrôleur d’urgences sont ensuite intégrés pour former un contrôleur tolérant aux défauts capable de gérer un grand nombre de défaillances tout en garantissant les ressources actionneur et en limitant la charge de calcul du processeur. Finalement, des contrôleurs tolérants aux défauts, actifs et passifs, basés sur des méthodes par mode glissant du premier et deuxième ordre sont développées pour les octorotors. La commande active utilise des méthodes d’allocation de contrôles pour redistribuer les efforts sur les actionneurs sains, réduisant ainsi l’effet du défaut. / Unmanned Aerial Vehicles (UAV) are more and more popular for their civil and military applications. Classical control laws usually show weaknesses in the presence of parameter uncertainties, environmental disturbances, and actuator and sensor faults. Therefore, it is judicious to design a control law capable of stabilizing the UAV not only in the fault-free nominal cases, but also in the presence of disturbances and faults. In this thesis, a new bio-inspired search algorithm called Ecological Systems Algorithm (ESA) suitable for engineering optimization problems is developed. The algorithm is used over the thesis to find optimal gains for the fault tolerant controllers. Sliding Mode Control theory is used to develop two Passive Fault Tolerant Controllers for quadrotor UAVs: Regular and Cascaded SMC. Because Passive Controllers handle a few numbers of faults, an Active Sliding Mode Fault Tolerant Controller using Kalman Filter is developed. To overcome severe faults and failures, an emergency controller based on the Quadrotor-to-Trirotor conversion maneuver is developed. The Controllers developed so far (Passive, Active, and emergency controllers) are then integrated to form the Integrated Fault Tolerant Controller (IFTC). The IFTC is a powerful controller that is able to handle a wide number of faults, and save actuator resources as well as processor computational effort. Finally, Passive and Active Fault Tolerant Controllers are designed for octorotor UAVs based on First Order and Second Order Sliding Mode Control. The AFTC uses Dynamic and Pseudo-Inverse Control Allocation methods to redistribute the control effort among healthy actuators reducing the effect of fault.

This thesis examines the foreign policy of the 44th President of the United States of America, Barack Obama. A significant theme of Barack Obama's candidacy for presidency was "change"; more specifically change in policy from previous administration. Therefore, the thesis looks at the changes brought about by President Obama once he was elected. First, we look at foreign policy of the United States under President George W. Bush, then we look at foreign policy-related assertions of Barack Obama during his candidacy, and lastly we look at the actual policies of the new administration, with particular focus on the wars in Iraq and Afghanistan, drones and extrajudicial killing, Guantanamo detention facility and extraordinary rendition, and NSA surveillance.

Unmanned Aerial Vehicle (UAV), also called drones, are used for Search And Rescue (SAR) missions, mainly in the form of a pilot manoeuvring a single drone. However, the increase in labour to cover larger areas quickly would result in a very high cost and time spent per rescue operation. Therefore, there is a need for an easy to use, low-cost, and highly autonomous swarm of drones for SAR missions where the detection and rescue times are kept to a minimum. In this thesis, a Subsumption-based architecture is proposed, which combines multiple behaviours to create more complex behaviours. An investigation of (1) what are the critical aspects of controlling a swarm of drones, (2) how can a combination of different behavioural algorithms increase the performance of a swarm of drones, and (3) what benchmarks are necessary when evaluating the fitness of the behavioural algorithms. The proposed architecture was simulated in AirSim using the SimpleFlight flight controller through experiments that evaluated the individual layers and missions that simulated real-life scenarios. The results validate the modularity and reliability of the architecture, where the architecture has the potential for improvements in future iterations. For the search area of 400×400meters, the swarm consistently produced an average area coverage of at least 99.917% and found all the missing people in all missions, with the slowest average being 563 seconds. Compared to related work, the result produced similar or better times when scaled to the same proportions and higher area coverage. As comparisons of results in SAR missions can be difficult, the introduction of Active time can serve as a benchmark for others in future swarm performance measurements.

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<p dir="ltr">This dissertation presents the design, development, and testing environment of a low-cost, self-built ground based Counter Unmanned Aerial or Aircraft Vehicle (CUAV) system aimed at providing effective aerial security solutions in resource-limited environments. The kinetic CUAV technique was selected and identified for the current study as it is the most feasible, low-cost and reusable mitigation path as last-resort defense. Utilizing commonly available materials, including parts from online retailers and hardware stores, and incorporating a self-made pneumatic system with a reusable 3D-printed projectile and interchangeable parts design. This study explores the feasibility of cost-effective drone defense and introduces a short-range accuracy metric to evaluate the system’s trajectory behavior. Through rigorous indoor testing in Purdue University Hangar 4, the research evaluates the system's performance in terms of projectile height, range, and accuracy under various environmental conditions. A 90 degrees field of view of pneumatic launcher was tested with a small error margin comparison table to highlight on areas for potential technical refinement. TPU filament was found to be the best material for this study, with 10% infill, printing temperature in 225°C (437°F), and 70 mm/s printing speed settings for the 3D-printed projectile (4.16a). These findings in Figures 4.10, 4.11, 4.12, 4.13 will significantly advance the research of low-cost drone defense technologies by providing empirical evidence on material and design choices that will impact the system performance. Findings indicate that the system’s performance is affected by the climate temperatures, which influences its consistency in different settings. This offers practical implications for enhancing security measures against unauthorized drones using similar technology. The study fills a significant gap in current drone defense technologies with kinetic apprehension by proving that effective solutions can be both affordable and accessible. This work not only contributes to the advancement of counter drone technology but also encourages ongoing design innovation in the field, paving the way for further research and development into scalable and adaptable drone defense systems.</p>

Combiner les drones et l’intelligence artificielle afin de cartographier la végétation représente une nouvelle approche prometteuse susceptible d’améliorer la détection des espèces végétales exotiques envahissantes (EVEE). La résolution au sol élevée atteignable avec les drones et les innovations récentes en vision par ordinateur, notamment avec les réseaux neuronaux convolutifs, laissent penser qu'une détection précoce des EVEE serait possible, favorisant ainsi leur gestion. Dans cette étude, nous avons évalué la pertinence de cette approche pour cartographier l’emplacement du roseau commun (Phragmites australis subsp. australis) à l’intérieur d'un parc national situé dans le sud du Québec, Canada. Nous avons collecté des données à six dates distinctes durant la saison de croissance, couvrant des environnements présentant différents niveaux d'envahissement par le roseau. De façon générale, la performance du modèle était élevée pour les différentes dates et zones, surtout au niveau du rappel (moyenne globale de 0.89). Les résultats ont montré une augmentation de la performance pour atteindre un sommet à la suite de l’apparition de l’inflorescence en septembre (F1-score le plus haut à 0.98). De plus, une diminution de la résolution spatiale affectait négativement le rappel (diminution de 18% entre une résolution au sol de 0,15 cm pixel-1 et 1,50 cm pixel-1), mais n’avait pas un impact important sur la précision (diminution de 2%). Malgré des défis associés à la cartographie du roseau commun dans un contexte de gestion post-traitement, l'utilisation de drones et de l'apprentissage profond montre un grand potentiel pour la détection des EVEE lorsque appuyé par un jeu de données adapté. Nos résultats montrent que, d'un point de vue opérationnel, cette approche pourrait être un outil efficace pour accélérer le travail des biologistes sur le terrain et assurer une meilleure gestion des EVEE. / The combination of unoccupied aerial vehicles (UAVs) and artificial intelligence to map vegetation represents a promising new approach to improve the detection of invasive alien plant species (IAPS). The high spatial resolution achievable with UAVs and recent innovations in computer vision, especially with convolutional neural networks, suggest that early detection of IAPS could be possible, thus facilitating their management. In this study, we evaluated the suitability of this approach for mapping the location of common reed (Phragmites australis subsp. australis) within a national park located in southern Quebec, Canada. We collected data on six distinct dates during the growing season, covering environments with different levels of reed invasion. Overall, model performance was high for the different dates and zones, especially for recall (mean of 0.89). The results showed an increase in performance, reaching a peak following the appearance of the inflorescence in September (highest F1-score at 0.98). Furthermore, a decrease in spatial resolution negatively affected recall (18% decrease between a spatial resolution of 0.15 cm pixel-1 and 1.50 cm pixel-1) but did not have a strong impact on precision (2% decrease). Despite challenges associated with common reed mapping in a post-treatment monitoring context, the use of UAVs and deep learning shows great potential for IAPS detection when supported by a suitable dataset. Our results show that, from an operational point of view, this approach could be an effective tool for speeding up the work of biologists in the field and ensuring better management of IAPS.

<p>This dissertation examines the impact of active shooters upon patrons attending large outdoor events. There has been a spike in shooters targeting densely populated spaces in recent years, to include open-air venues. The 2019 Gilroy Garlic Festival was selected for modeling replication using AnyLogic software to test various experiments designed to reduce casualties in the event of an active shooter situation. Through achievement of validation to produce identical outcomes of the real-world Gilroy Garlic Festival shooting, the researcher established a reliable foundational model for experimental purposes. This active shooter research project identifies the need for rapid response efforts to neutralize the shooter(s) as quickly as possible to minimize casualties. Key findings include the importance of armed officers patrolling event grounds to reduce response time, the need for adequate exits during emergency evacuations, incorporation of modern technology to identify the shooter’s location, and applicability of a 1:548 police to patron ratio.</p>

Sjöräddning i Sverige sker genom samverkan mellan statliga verksamheter, kommuner och frivilligorganisationer för ett gemensamt mål att rädda personer som råkat i sjönöd. Tid är ofta en kritisk faktor i räddningsuppdragen men ett snabbt och oplanerat utryck riskerar samtidigt att sätta räddningsaktörerna själva i farozonen. Utvecklingen av obemannade autonoma farkoster för SAR (eng. Search And Rescue) ses som en lösning på behovet att kunna snabbt skicka hjälp till samt få ögon på incidentplatsen utan att försätta räddningsaktörerna för onödig risk. Nuvarande kommunikationssystem inom svensk sjöräddning kan dock inte hantera annan typ av information än muntlig varav räddningsaktörer endast känner till riktlinjer för hantering av muntlig information. Med ett framtida införande av autonoma farkoster kommer dock fler informationstyper att behöva hanteras i sjöräddningar varav oklarheten om vilka informationstyper autonoma farkoster samlar in och vilka datahanteringskrav som finns är problematiskt. Oklarhet om informationstyperna och deras datahanteringskrav är vidare problematiskt för utvecklingen och implementeringen av autonoma farkoster då risken finns att farkoster och tekniker utvecklas men inte får användas för att de inte är anpassade efter lagkraven på hantering av olika datatyper. I denna studie undersöks därför vilka informationstyper som autonoma farkoster kan samla in vid sjöräddning. Detta för att komma fram till vilka riktlinjer för datahantering som gäller vid sjöräddning med autonoma farkoster. Studien undersöker också vilka informationstyper som är kritiska för en SAR-sjöräddningssamverkan samt vilka informationsdelningsutmaningar som finns i dagens sjöräddning. Studien genomfördes i form av en kvalitativ fallstudie och har tillämpat ett socio-tekniskt systemperspektiv för att bättre se till helheten och besvara frågeställningarna. Resultatet av denna studie visar att autonoma farkoster kan samla in information om sin omgivning, vilket utgör grunden för att skapa en medvetenhet om situationen som är kritiskt för SAR-operationer, och kan även samla in information om sitt eget tillstånd. De lagverk som identifierats utgöra de huvudsakliga restriktionerna är kamerabevakningslagen, lagen för skydd av geografisk information, offentlighets- och sekretesslagen, GDPR och dataskyddslagen. Dessa lagverk innehåller riktlinjer för delning av information och personuppgiftsbehandling i SAR-sjöräddning. Kunskapsbidrag studien har genererat inkluderar bland annat identifiering av datatyper som kan samlas in av autonoma farkoster i en SAR-sjöräddning, och sannolikt andra typer av räddningsinsatser, och delning och hanteringskraven på de datatyperna i räddningsinsatser och därmed kunskap om vilka datatyper som är mest reglerade. Vidare kunskapsbidrag är kunskap om vilka informationstyper som är mest kritiska för SAR-sjöräddningar, och därför bör prioriteras att samlas in och delas, och identifieringen av utmaningar för informationsdelning mellan statliga verksamheter och frivilligorganisationer. / Maritime rescue in Sweden is performed through a cooperation between government agencies, municipalities and non-governmental organisations (NGOs) with the common goal of saving people in distress. Time is often a critical factor in the rescue missions but a fast and unplanned response may at the same time put the rescue workers in danger. The development of unmanned autonomous vehicles for SAR is seen as a solution to the need of being able to quickly sendhelp as well as get eyes on the scene of the incident without exposing the rescue workers for unnecessary risks. However, the current communications systems in Swedish maritime rescue are unable to handle any other type of information except verbal, meaning that rescue workers only know guidelines for handling verbal information. However, with a future implementation of autonomous vehicles, there will be a need to handle more information types in maritime rescue whereas the uncertainty regarding what kind of information autonomous vehicles collect and which data management requirements exist is problematic. The uncertainty about the information types and their data management requirements is also problematic for the development and implementation of autonomous vehicles as there is a risk that vehicles and technologies are developed but not allowed to be used because they are not adapted to the legal requirements on management of the different types of data. Therefore, in this study I examine what information types that autonomous vehicles can collect in a maritime rescue to find out what guidelines for data management that applies during a maritime rescue with autonomous vehicles. The study also examines what kind of information’s are critical for a SAR maritime rescue cooperation as well as what information sharing challenges exist in current maritime rescue. The study was performed as a qualitative case study and has used a socio-technical systems perspective so as to better see the overall picture and answer the research questions. The result shows that autonomous vehicles can collect information about their surroundings, which is the foundation for establishing situation awareness that is critical for SAR-operations, and that they can collect information about their own status. The main laws and regulations that have been identified as constituting the main restrictions are (translated from Swedish) the law of camera surveillance, the law for protection of geographical information, the public access to information and secrecy act, the GDPR and the data protection act. These contains guidelines for sharing information and the processing of personal data in SAR maritime rescue. The knowledge contributions of this study includes among others the identification of datatypes that can be collected by autonomous vehicles in SAR maritime rescue, and probably other types of rescue operations, and the sharing and management requirements on those datatypes in rescue operations and thus knowledge of what datatypes that are the most restricted. Further knowledge contributions is knowledge about which information types that are the most critical for SAR maritime rescue and thus should be prioritised for collection and sharing as well as the identification of challenges for information sharing between government agencies and NGOs.

During the last century, various countries' armed forces have used unmanned aerial vehicles, commonly known as drones. In recent years, strives have been made to develop small commercial drones that have allowed the general public to afford and use them for recreational purposes. The availability of drones has led to immoral applications of the technology. Such applications need to be faced with anti-measures and effective detection methods. Therefore, this thesis aims to develop a mobile reconnaissance robot that can detect commercial drones with radar. It describes integrating radar sensors with single-board computers to detect and localise air-bound objects. The finished product aims to be used for educational and exhibition purposes at the Swedish Armed Forces technical school to increase awareness of the technology. / <p>Försvarsmaktens tekniska skola i Halmstad var intressenter för uppsatsen.</p>