Relevant bibliographies by topics / Reconnaissance drone

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Reconnaissance drone'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Reconnaissance drone"

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Guo, Yinjing, Jianhua Zhang, Yuanyuan Ju, and Xiaohan Guo. "Climbing Reconnaissance Drone Design." IOP Conference Series: Materials Science and Engineering 452 (December 13, 2018): 042060. http://dx.doi.org/10.1088/1757-899x/452/4/042060.

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Do, Sangwon, Myeongjae Lee, and Jong-Seon Kim. "The Effect of a Flow Field on Chemical Detection Performance of Quadrotor Drone." Sensors 20, no. 11 (June 8, 2020): 3262. http://dx.doi.org/10.3390/s20113262.

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The determination of a suitable sensor location on quadrotor drones is a very important issue for chemical reconnaissance platforms because the magnitude and direction of air velocity is different for each location. In this study, we investigated a customized chemical reconnaissance system consisting of a quadrotor drone and a chip-sized chemical sensor for detecting dimethyl-methylphosphonate (DMMP; a Sarin simulant) and investigated the chemical detection properties with respect to the sensor position through indoor experiments and particle image velocimetry (PIV) analysis of the system. The PIV results revealed an area free of vortex–vortex interaction between the drone rotors, where there was distinctly stable and uniform chemical detection of DMMP. The proposed chemical reconnaissance system was found to be realistic for practical application.
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Valentino, Rico, Woo-Sung Jung, and Young-Bae Ko. "A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks." Sensors 18, no. 11 (November 2, 2018): 3751. http://dx.doi.org/10.3390/s18113751.

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Drones have recently become extremely popular, especially in military and civilian applications. Examples of drone utilization include reconnaissance, surveillance, and packet delivery. As time has passed, drones’ tasks have become larger and more complex. As a result, swarms or clusters of drones are preferred, because they offer more coverage, flexibility, and reliability. However, drone systems have limited computing power and energy resources, which means that sometimes it is difficult for drones to finish their tasks on schedule. A solution to this is required so that drone clusters can complete their work faster. One possible solution is an offloading scheme between drone clusters. In this study, we propose an opportunistic computational offloading system, which allows for a drone cluster with a high intensity task to borrow computing resources opportunistically from other nearby drone clusters. We design an artificial neural network-based response time prediction module for deciding whether it is faster to finish tasks by offloading them to other drone clusters. The offloading scheme is conducted only if the predicted offloading response time is smaller than the local computing time. Through simulation results, we show that our proposed scheme can decrease the response time of drone clusters through an opportunistic offloading process.
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Kolamunna, Harini, Thilini Dahanayaka, Junye Li, Suranga Seneviratne, Kanchana Thilakaratne, Albert Y. Zomaya, and Aruna Seneviratne. "DronePrint." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 1 (March 19, 2021): 1–31. http://dx.doi.org/10.1145/3448115.

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With the ubiquitous availability of drones, they are adopted benignly in multiple applications such as cinematography, surveying, and legal goods delivery. Nonetheless, they are also being used for reconnaissance, invading personal or secure spaces, harming targeted individuals, smuggling drugs and contraband, or creating public disturbances. These malicious or improper use of drones can pose significant privacy and security threats in both civilian and military settings. Therefore, it is vital to identify drones in different environments to assist the decisions on whether or not to contain unknown drones. While there are several methods proposed for detecting the presence of a drone, they have limitations when it comes to low visibility, limited access, or hostile environments. In this paper, we propose DronePrint that uses drone acoustic signatures to detect the presence of a drone and identify the make and the model of the drone. We address the shortage of drone acoustic data by relying on audio components of online videos. In drone detection, we achieved 96% accuracy in a closed-set scenario, and 86% accuracy in a more challenging open-set scenario. Our proposed method of cascaded drone identification, where a drone is identified for its 'make' followed by the 'model' of the drone achieves 90% overall accuracy. In this work, we cover 13 commonly used commercial and consumer drone models, which is to the best of understanding is the most comprehensive such study to date. Finally, we demonstrate the robustness of DronePrint to drone hardware modifications, Doppler effect, varying SNR conditions, and in realistic open-set acoustic scenes.
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Zhang, Ge, Kangli Ma, and Chang Liu. "A DroneGo Disaster Relief Response System." Remote Sensing 9, no. 1 (August 12, 2020): 6. http://dx.doi.org/10.18282/rs.v9i1.1094.

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<p>To support the Puerto Rico hurricane disaster scenario, we develop a DroneGo disaster response system by establishing the following models. First, we establish a location analysis model for ISO containers based on the coverage of video reconnaissance and the priority comparison between the two required missions–medical supply delivery and video reconnaissance. According to the locations of 11 harbors in Puerto Rico, we select three suitable harbors to position three cargo containers called CON 1, 2 and 3 to conduct the missions. Second, we build two packing configuration models to design the packing configuration for containers. In one model, we recommend a drone fleet for CON 1 and 3 according to reconnaissance conditions, and then put drones into containers in order. In another model for CON 2, we determine the type of drones according to the medical supply demands of hospitals. For both models, the number of drones of each type is determined by the enumeration method and the packing placement is determined by the greedy algorithm. The algorithms are coded in Visual C++ and MATLAB. The computational results show that the space utilizations for the three containers are all above 80.8%. Third, we design a drone flight plan model based on graph theory. According to the time and space constraints of drones, we devise flight plans as well as delivery routes and schedule. The computational results show that the coverage of video reconnaissance is up to 70.1%. Finally, we carry out the error and sensitivity analysis, discuss the strengths and weaknesses of our models, and design the future work. In addition, a two-page memo that summarizes our modeling results, conclusions, and recommendations is given at the end of the paper.</p>
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Gardner, P., and C. R. Day. "Options for Control and Navigation of Unmanned Aircraft." Journal of Navigation 45, no. 3 (September 1992): 352–68. http://dx.doi.org/10.1017/s0373463300010936.

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Whilst unmanned aircraft (UMA) can be said to encompass missiles, targets for weapon training, preprogrammed reconnaissance drones and civilian systems such as radio-controlled models used to carry cameras, for the purpose of this paper we will restrict ourselves to aircraft which are capable of sustaining autonomous flight and accepting navigation commands whilst airborne. The terms applied to unmanned aircraft are variously UMA, remotely piloted vehicle (RPV), Drone and Unmanned Aerial Vehicle (UAV).
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Kindervater, Katharine Hall. "The emergence of lethal surveillance: Watching and killing in the history of drone technology." Security Dialogue 47, no. 3 (January 25, 2016): 223–38. http://dx.doi.org/10.1177/0967010615616011.

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This article examines the history of the development of drone technology to understand the longer histories of surveillance and targeting that shape contemporary drone warfare. Drawing on archival research, the article focuses on three periods in the history of the drone: the early years during World Wars I and II, the Cold War, and the 1990s. The history of the drone reveals two key trends in Western warfare: the increasing importance of intelligence, surveillance, and reconnaissance (ISR) and the development of dynamic targeting. These trends converge today in a practice of lethal surveillance where ISR capabilities are directly linked to targeted killing, effectively merging mechanisms of surveillance and knowledge production with decisions on life and death. Taking this history of lethal surveillance into account not only reframes current debates on drone warfare, but also connects the drone to other practices of security and control.
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Sulkowski, Jarosław, Janusz Błaszczyk, Adam Grzybowski, Kacper Karcz, and Paweł Kowaleczko. "Investigations of the susceptibility of unmanned aerial vehicles on intensive microwave radiation." Journal of KONBiN 48, no. 1 (December 1, 2018): 107–18. http://dx.doi.org/10.2478/jok-2018-0049.

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Abstract Commercially available drone type unmanned aerial vehicles (UAV), as well as parts and subsystems enabling the assemble of UAVs, are becoming more and more widely available, and their performance is constantly improving. The current possibilities of a drone assembled by a hobbyist predispose are not only to be used as a reconnaissance tool but also as a guided missile. The use of a camera with a video stream transmitter simultaneously with virtual reality goggles and video stream receiver gives the pilot an impression of being on the drone, which makes it possible to control it with extreme precision, even at high speeds. The article presents research aimed at determining the extent and type of damage made on exemplary racer drone caused by intense microwave field radiations.
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Jafari, Navid H., Qin Chen, and Jack Cadigan. "RAPID DEPLOYMENT AND POST-STORM RECONNAISSANCE OF HURRICANE LAURA." Coastal Engineering Proceedings, no. 36v (December 28, 2020): 60. http://dx.doi.org/10.9753/icce.v36v.waves.60.

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Hurricane Laura made landfall on the southwest Louisiana coast near Cameron, LA on August 26th. As Laura approached the Louisiana coast, the Coastal Emergency Risks Assessment predicted a storm surge of approximately 5.2 m (17 ft), which marked the strongest surge to impact southwest Louisiana since the catastrophic Hurricane Rita in 2005. As a result, a team led by LSU and NEU mobilized to deploy surge and wave sensors and collect drone imagery at Rockefeller Wildlife Refuge and Cameron, LA on August 25th before the arrival of tropical storm winds. Rockefeller Refuge was selected to measure the capacity of wetlands and breakwaters to attenuate hurricane surge and waves, and pressure sensors were strategically placed at locations of civil infrastructure at Cameron to capture hurricane-induced overland flow (see Fig. 1). After the surge water receded, LSU retrieved the sensors, collected RTK elevation transects and multispectral drone imagery, and surveyed infrastructure damage along the southwest corridor of Louisiana, following the Highway 82 from Abbeville to Cameron.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/IevnFZ2YVfI
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Neroba, Vadym. "DEVELOPMENT OF METHODS FOR ASSESSMENT AND SELECTION OF UNMANNED AERIAL VEHICLE FOR MINE RECONNAISSANCE." ScienceRise, no. 5 (November 11, 2020): 44–50. http://dx.doi.org/10.21303/2313-8416.2020.001496.

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Object of research: comparative assessment and selection of an unmanned aerial vehicle for mine reconnaissance sample. Investigated problem: substantiation of the methodological apparatus for comparative assessment and selection of an unmanned aerial vehicle for mine reconnaissance sample, taking into consideration the presence of both quantitative and qualitative indicators. Main scientific results: the methods of comparative assessment and selection of an unmanned aerial vehicle for mine reconnaissance sample is developed. The technique is based on an expert method, which allows a drone sample to be evaluated and selected objectively, taking into consideration the presence of both quantitative and qualitative indicators. At the same time, group interaction and discussion of experts are realized. When the judgments do not coincide, an artificial consensus is not imposed. The number of experts is not limited. The experts are not linked in any way. The need to ensure transitive consistency (10–12 %) makes it possible to record attempts by an expert (experts) to artificially overestimate the indicators of one of the drone samples (or the one being evaluated), therefore, the indicators of another sample will automatically deteriorate. The principle of impartiality and fairness is maintained. A well-trained objective coordinator is not required, and the reality is that the absence of the disrupting the problem solution possibility is due to a change in the psychological situation among the experts. Area of practical use of research results: humanitarian demining in the interests of ensuring the detection of mines for various purposes by sappers from a safe distance. At the same time, an increase within the probability of mines detecting is ensured due to special equipment installed onboard the drone. Innovative technological product: a technique has been developed that allows not only assessing the drone samples for mine reconnaissance objectively, but making an objective choice of a sample for specific requirements also. Scope of application of the innovative technological product: clearance of the terrain remaining after the end of hostilities. With the help of unmanned aerial vehicles, a significant acceleration of the demining process is possible, especially in those territories where mines are installed and being for a sufficiently long time.
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Dissertations / Theses on the topic "Reconnaissance drone"

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Hung, David, Cinthya Tang, Coby Allred, Kennon McKeever, James Murphy, and Ricky Herriman. "AUTONOMOUS GROUND RECONNAISSANCE DRONE USING ROBOT OPERATING SYSTEM (ROS)." International Foundation for Telemetering, 2017. http://hdl.handle.net/10150/627005.

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The Arizona Autonomous Club is a student organization at the University of Arizona which designs, builds, and competes with Unmanned Air Systems (UAS). This year, a 25% scale Xtreme Decathlon model aircraft was selected and successfully converted into a fully autonomous UAS for the AUVSI Student Unmanned Aerial Systems (SUAS) 2017 competition. The UAS utilizes a Pixhawk autopilot unit, which is an independent, open-hardware project aiming at providing high-end autopilot hardware at low costs and high availability. The Pixhawk runs an efficient real time operating system (RTOS) and includes sensors such as a GPS unit, IMUs, airspeed, etc. The UAS also includes an onboard imaging system, which is controlled by an onboard computer (OBC). The Pixhawk and OBC are interconnected with two ground control stations (GCS) using the Robot Operating System (ROS) framework, which is capable of extending overall system capabilities to include an expanded telemetry downlink, obstacle avoidance, and manual overrides.
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Raffetto, Mark. "Unmanned aerial vehicle contributions to intelligence, surveillance, and reconnaissance missions for expeditionary operations." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Sep%5FRaffetto.pdf.

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Gerhardt, Damon. "Feature-based mini unmanned air vehicle video Euclidean stabilization with local mosaics /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1698.pdf.

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Jackson, Joseph A. "Panoramic video for efficient ground surveillance from small unmanned air vehicles /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1811.pdf.

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Jenkins, Glenn E., and William J. Snodgrass. "The Raven Small Unmanned Aerial Vehicle (SUAV), investigating potential dichotomies between doctrine and practice." Monterey, California. Naval Postgraduate School, 2005. http://hdl.handle.net/10945/834.

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MBA Professional Report
The goal of this MBA Project is to investigate possible disconnects between doctrine and practice in the employment of the Raven Small Unmanned Aerial Vehicle (SUAV). The Army's current Small UAV requirements are based upon the Future Combat System's Operations Requirements Document and has not been validated at the platoon or company level. The Raven SUAV is a Commercial off the Shelf (COTS) item that swiftly became the Army's Small UAV of choice for operations in Afghanistan and Iraq. Doctrine and Techniques, Tactics, and Procedures (TTP) have been written for the Raven SUAV; however, it is not standard practice for all units operating the system abroad. The last review of the SUAV operational requirements was conducted in 2003 but did not specifically address its usage on the battlefield. In an attempt to fill that gap, this project focuses on real-world usage of the Raven SUAV system. We compare doctrine versus practice using the Department of Defense's (DOD) Doctrine, Organization, Training, Material, Leadership, Personnel, Facilities (DOTML-PF) model as the primary logic construct. The report begins by providing a background of the Raven SUAV, to include its evolution from a COTS item to the Army's SUAV of choice, and how it has impacted the warfighter. Next, the authors provide an overview of DOTML-PF in order to provide a basis for comparing doctrine and practice. The study then looks in-depth at doctrine and practice using DOTML-PF as the model for revealing differences between the two. Finally, the authors analyze these differences and recommend solutions to mitigate shortfalls in actual Raven SUAV usage on the battlefield.--p. i.
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Nilsson, Patrik. "Blända kommersiella UAV:er med laservapen." Thesis, Försvarshögskolan, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:fhs:diva-6773.

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Försvarsmakten har idag problem under övningar med oidentifierade UAV:er som befinner sig över övningsområdet. UAV:er som har möjlighet till att spana på övningar och kartlägga infrastruktur och förmågor. Denna uppsats syftar till att undersöka vilka möjligheter en handhållen laserpekare skulle skapa för att motverka problemet med UAV:er som befinner sig över övningsområden alternativt intill skyddsobjekt. Tanken med att undersöka handhållna laserpekare är att handhållna laserpekare inte är stora, tunga eller energikrävande. Detta skulle möjliggöra att sprida dem inom Försvarsmakten till samtliga förband. Experiment med olika handhållna laserpekare genomförs för att kontrollera vilka effekter de ger på bestämda avstånd. Det genomförs även experiment för att kontrollera vilka avstånd en specifik UAV kan genomföra spaningsuppdrag på samt möjligheter att upptäcka en UAV på olika avstånd med och utan hjälpmedel. Experimenten visar att om observatören är medveten om i vilken riktning UAV befinner sig är det möjligt att upptäcka den på 600m avstånd. Detta skapar ett problem eftersom UAV kan genomföra spaning på längre avstånd. Experimenten visar även att laser ger en bländningseffekt på 75m vilket inte är i närheten av UAV:ers möjliga spaningsavstånd
The Swedish Armed Forces today have problems during exercises with unidentified UAVs located over the exercise area. UAVs which can observe exercises and map infrastructure and abilities. This paper aims to investigate what capabilities a handheld laser pointer would create to counteract the problem of UAVs that are located over training areas or adjacent to protected area. The thought of examining handheld laser pointers is that handheld laser pointers are not large, heavy or energy-intensive, which would enable them to spread within the Armed Forces to all services. Experiments with different handheld laser pointers are performed to investigate the effects they give at a certain distance. Experiments are also carried out to check at which distances a specific UAV can perform reconnaissance assignments, as well as the ability to detect a UAV at various distances with and without aids. The experiments showed that if the observer is aware of the direction of the UAV, it is possible to detect it at 600m distance with aids and at 500m without. The experiments also showed that laser gives a glare effect at 75m which is not close to a UAV's possible reconnaissance distances.
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Poh, Seng Cheong Telly. "Simulations of diversity techniques for urban UAV data links." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FPoh.pdf.

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Louargant, Marine. "Proxidétection des adventices par imagerie aérienne : vers un service de gestion par drone." Thesis, Dijon, 2016. http://www.theses.fr/2016DIJOS029/document.

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Le contexte agricole actuel vise à réduire l’utilisation des produits phytosanitaires sur les parcelles. Dans ce cadre, la gestion des adventices consommant de grandes quantités d’herbicides est devenue une problématique majeure. Afin de mettre en place un outil de gestion localisée des adventices par drone, cette thèse étudie l’adaptation du système d’acquisition (drone + dispositif multispectral) actuellement proposé par AIRINOV à la détection des adventices sur des cultures sarclées. La chaîne d’acquisition a été modélisée afin d’évaluer l’impact de différents paramètres du modèle (filtres optiques et résolution spatiale) sur la qualité de la détection des adventices. Des orthophotographies et images ortho-rectifiées ont été acquises à l’aide d’un capteur multispectral (4 et 8 filtres) à des résolutions spatiales de 6 mm et 6 cm. Plusieurs méthodes de localisation des adventices adaptées à l’étude de ces images ont été développées. Elles reposent sur 1) l’analyse de la distribution spatiale de la végétation (détection de rang par la transformée de Hough et analyse de forme), 2) la classification spectrale des pixels (méthodes supervisées : LDA, QDA, distance de Mahalanobis, SVM). Enfin, une classification spectrale basée sur un apprentissage issu des informations spatiales été proposée, améliorant ainsi la détection des adventices.Des cartes d’infestation des parcelles et de préconisation en pulvérisation localisée ont alors été créées
The agricultural framework aims to reduce pesticide use on fields. Weed management, which is highly herbicide consuming, became a great issue. In order to develop a weed management service using UAV, this PhD dissertation studies how to adapt the acquisition system (UAV + multispectral camera) developed by AIRINOV to detect weeds in row crops. The acquisition chain was modeled to assess some of its parameters (optical filters and spatial resolution) impact on weed detection quality. Orthoimages and orthorectified images were created using a multispectral camera (4 to 8 filters) with 6 mm to 6 cm spatial resolutions. Several weed location methods were specifically developed to study multispectral images acquired by UAV. They are based on 1) the analysis of vegetation spatial distribution (row detection using the Hough transform and shape analysis), 2) spectral classification of pixels (supervised methods: LDA, QDA, Mahalanobis distance, SVM). In order to improve weed detection, a spectral classification based on training data deduced from spatial analysis was then proposed.Weed infestation maps and recommendation for spot spraying applications were then produced
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Gigan, Daniel. "Modélisation des comportements d'un pilote expert en situation de collision en vol vers une nouvelle technologie "voir et éviter" pour les drones : Pour un fonctionnalisme holistique à vocation intégrative." Thesis, Toulouse, ISAE, 2013. http://www.theses.fr/2013ESAE0022.

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Le principe du “Voir et Eviter” est un principe essentiel pour la sécurité de l'espace aérienet nécessaire pour l'insertion des drones dans la Circulation Aérienne Générale (CAG). Lessolutions actuelles ne permettent pas toujours d'appliquer le principe du "Voir et Eviter" etn'assurent pas, par leurs actions d'évitements, une cohabitation sans risques des drones et desavions pilotés dans la CAG. Ces mêmes solutions ne permettent pas, avec un seul capteurpassif, la détermination, à tout moment, du temps restant avant collision. Ce temps estessentiel pour qualifier la dangerosité de la situation de collision et adapter, en conséquencede cette dangerosité, le comportement d'évitement. Aussi, pouvoir faire appliquer à unemachine le principe du "Voir et Eviter" tout en permettant une cohabitation sans risquesimplique un paradigme de conception basé sur la modélisation de l'être humain expert del'espace aérien, le pilote. Un tel paradigme s'applique d'ailleurs à toute problématiquenécessitant une cohabitation homme machine sans risque.Aussi, l'objectif de cette thèse est la modélisation des comportements d'un pilote expert ensituation de collision en vol. Cette modélisation permet de poser les premières basestechnologiques pour l'élaboration d'un nouveau système “Voir et Eviter” répondant auproblème d'intégration des machines dans la CAG.La modélisation proposée, résultat d'une démarche intégrative, s'appuie sur unedescription des processus cognitifs et des processus de traitement de l'information ainsi quesur l'organisation des unités ou systèmes qui encapsulent ces traitements. L'ensemble des cessystèmes et processus permet la résolution de problèmes et amène au comportementobservable qui est la solution du ou des problèmes posés par ou au travers de l'environnement.Cela a permis, notamment, la construction d'un modèle générique du processus cognitif etsensoriel d'identification par catégorisation et/ou classification d'objets. Cette modélisations'appuie mathématiquement sur les théories de régression non linéaire et sur l'utilisation desméthodes numériques pour la résolution de problèmes d'optimisation.Le nouveau système "Voir et Eviter" issu de cette modélisation, construit à partir d'unsimple capteur optique passif, émule, par des algorithmes simples, les processus de détection,de reconnaissance et de sélection d'une action amenant au comportement d'évitement dans unesituation de collision face à face avec un mobile protagoniste. Grâce au modèle générique decatégorisation et/ou de classification, le principal résultat technologique de cette thèse, qui estaussi la principale qualité de ce nouveau système "Voir et Eviter" proposé, est sa capacité àdéterminer, à tout moment, le temps restant avant la collision, avec un seul capteur passif. Deplus, par sa conception (bio et psycho mimétique), ce nouveau système pourrait bien poser lespremières bases d'une démarche de certification type Equivalent Level Of Safety (ELOS).Celle-ci permettrait alors une validation sécuritaire d'un système "Voir et Eviter" parcomparaison avec les pilotes de la CAG qui sont, par ailleurs, les véritables éléments desécurité de dernier recours de l'espace aérien actuel
The aim of this doctoral thesis is the modeling of expert pilot behaviors in flight collisions. This modeling gives the first echnologic steps to elaborate a new "sense and avoid" system allowing the future integration of Unmanned Air Vehicles in eneral air traffic. The proposed modeling is the result of global and holistic way and describes the cognitive process and he architecture of systems allowing the expression of these cognitive processes. This model allows solving the collision problem thanks to an observable and adapted piloted behavior. Besides a generic modeling of cognitive process of ategorization has been built and based on non linear regression theory and numeric methods for the resolution of ptimization problems.hanks to this global modeling, this new "sense and avoid" system is made of a simple passive optic sensor and it emulates he detection process, the recognition process and the and the actions selection process allowing the resolution of collision problem by a adapted piloted behavior. Thanks to the generic categorization modeling, the main technologic result is to be ble to determinate the Time To Collision (ITC) with a passive sensor. The determination of the TTC is essential for the 'sense and avoid" systems to get the level safety certification required to integrate drones in general air traffic
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Damien, Eynard. "Capteur de stéréovision hybride pour la navigation des drones." Phd thesis, Université de Picardie Jules Verne, 2011. http://tel.archives-ouvertes.fr/tel-00652615.

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La connaissance de l'attitude, de l'altitude, de la segmentation du sol et du mouvement est essentielle pour la navigation d'un drone, en particulier durant les phases critiques de décollage et d'atterrissage. Dans ce travail de thèse, nous présentons un système stéréoscopique hybride composé d'une caméra fisheye et d'une caméra perspective pour estimer les paramètres de navigation d'un drone. À partir de ce capteur, une approche systémique est proposée. Contrairement aux méthodes classiques de stéréovision basées sur l'appariement de primitives, nous proposons des méthodes qui évitent toute mise en correspondance entre les vues hybrides. Une technique de plane-sweeping est suggérée pour déterminer l'altitude et détecter le plan du sol. La rotation et la translation du mouvement sont ensuite découplés : la vue fisheye contribue à évaluer l'attitude et l'orientation tandis que la vue perspective contribue à apporter l'échelle métrique de la translation. Le mouvement peut ainsi être estimé de façon robuste et à l'échelle métrique grâce à la connaissance de l'altitude. Cette méthode repose sur l'algorithme des 2-points complété par un filtre de Kalman. Nous proposons des approches robustes, temps réel et précises, exclusivement basées sur la vision avec une implémentation C++. Bien que cette approche évite l'utilisation de capteurs autres que les caméras, ce système peut également être appuyé par une centrale inertielle.
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Books on the topic "Reconnaissance drone"

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Blom, John David. Manned and unmanned aerial reconnaissance in the US Army, 1917-2008. Fort Leavenworth, Kan: Combat Studies Institute Press US Army Combined Arms Center, 2009.

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Bill, Gunston. Jian die fei ji: Spy planes & electronic warfare aircraft. Taibei Shi: Mai tian chu ban gu fen yu xian gong si, 1997.

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Office, General Accounting. Unmanned aerial vehicles: Outrider demonstrations will be inadequate to justify further production : report to the Secretary of Defense. Washington, D.C. (P.O. Box 37050, Washington, D.C. 20013): The Office, 1997.

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Office, General Accounting. Unmanned aerial vehicles: Progress of the Global Hawk Advanced Concept Technology Demonstration : report to congressional committees. Washington, D.C. (P.O. Box 37050, Washington 20013): The Office, 2000.

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Office, General Accounting. Unmanned aerial vehicles: Questionable basis for revisions to Shadow 200 acquisition strategy : report to the Chairman, Subcommittee on Military Research and Development, Committee on Armed Services, House of Representatives. Washington, D.C. (P.O. Box 37050, Washington, D.C. 20013): The Office, 2000.

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Office, General Accounting. Unmanned aerial vehicles: No more Hunter systems should be bought until problems are fixed : report to the Secretary of Defense. Washington, D.C: The Office, 1995.

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Office, General Accounting. Unmanned aerial vehicles: Performance of short-range system still in question : report to the Chairman, Legislation and National Security Subcommittee, Committee on Government Operations, House of Representatives. Washington, D.C: The Office, 1993.

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Office, General Accounting. Unmanned aerial vehicles: More testing needed before production of short-range system : report to the Chairman, Legislation and National Security Subcommittee, Committee on Government Operations, House of Representatives. Washington, D.C: The Office, 1992.

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Subcommittee, United States Congress House Committee on Armed Services Tactical Air and Land Forces. Hearing on National Defense Authorization Act for Fiscal Year 2007 and previously authorized programs before the Committee on Armed Services, House of Representatives, One Hundred Ninth Congress, second session: Tactical Air and Land Forces Subcommittee hearing on budget request : unmanned aerial vehicles and intelligence, surveillance, and reconnaissance capabilities, hearing held, April 6, 2006. Washington: U.S. G.P.O., 2007.

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Hearing on National Defense Authorization Act for Fiscal Year 2007 and previously authorized programs before the Committee on Armed Services, House of Representatives, One Hundred Ninth Congress, second session: Tactical Air and Land Forces Subcommittee hearing on budget request : unmanned aerial vehicles and intelligence, surveillance, and reconnaissance capabilities, hearing held, April 6, 2006. Washington: U.S. G.P.O., 2007.

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Book chapters on the topic "Reconnaissance drone"

1

Paucar, Carlos, Lilia Morales, Katherine Pinto, Marcos Sánchez, Rosalba Rodríguez, Marisol Gutierrez, and Luis Palacios. "Use of Drones for Surveillance and Reconnaissance of Military Areas." In Smart Innovation, Systems and Technologies, 119–32. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78605-6_10.

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Radjawali, Irendra, Oliver Pye, and Michael Flitner. "Recognition through reconnaissance? Using drones for counter-mapping in Indonesia." In De-centring Land Grabbing, 120–36. Routledge, 2019. http://dx.doi.org/10.4324/9781351134873-6.

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Auslander, Leora, and Tara Zahra. "Epilogue." In Objects of War, 309–18. Cornell University Press, 2018. http://dx.doi.org/10.7591/cornell/9781501720079.003.0012.

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This epilogue argues that war is a profound reminder of human materiality and fragility. Conflicts are made and decided through the interaction of human beings and the material world. The inquiries started in this book offer the foundation for a historically informed investigation of this interaction in the new forms of warfare in an age in which the virtual often seems to have more presence than the material. Indeed, new technologies have radically changed the materiality of war. Rather than physically encountering the enemy on the battlefield or entering homes as they invade enemy territory, for example, many soldiers are now sitting thousands of miles away launching missiles or operating drones that do first the reconnaissance and then the killing for them.
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Conference papers on the topic "Reconnaissance drone"

1

Bluman, James, Davonte Carter Vault, Wei Kang Soon, Ruth Talbott, Jonathan Willis, Andrew Kopeikin, and Ekaterina Prosser. "Autonomous Drone Delivery From Airdrop Systems (ADDAS): Aerially Deploying Folding-Wing Drones for Ground Reconnaissance." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-24046.

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Abstract Extending the long-range capabilities of unmanned aerial systems (UAS) is paramount to protecting small Soldier teams operating in remote battlefield scenarios. Currently, many small Unmanned Aerial Systems (sUAS) reconnaissance assets have the range necessary to facilitate small-unit missions. The purpose of this paper is to detail the design and testing of a system that fills this capability gap by creating folding-wing drones designed to be aerially deployed from an airdropped dispenser. The dispenser is attached to the Joint Precision Airdrop System (JPADS), which is an autonomously navigated cargo delivery parafoil that can glide several miles, and which can land within 100 meters of its target. To employ the system, the dispenser is launched from a high-altitude aircraft. The system must survive the opening of the parachute in high speed forward flight and provide cushioning to the drones and other components. Once the JPADS navigates the dispenser to a predetermined altitude and distance from the intended reconnaissance area, the dispenser deploys multiple folding-wing drones. The soldiers on the ground can access the drones’ live video feeds through a handheld video transmitter. The system combines the precision navigation and information-transmission capabilities of the fixed wing drone with the long-range capabilities of the JPADS. With a commercial-off-the-shelf drone as the folding-wing aircraft platform, the team designed a wing connection hub that allows for rapid folding and unfolding of the drone’s wings, a separate canister for each drone within the dispenser, and a dispenser capable of interfacing with both the canisters and the JPADS. Though currently in the technology-demonstration phase of the project, the team envisions the system being fully autonomous from launch of dispenser to end of mission.
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Cook, Joseph P., Brian F. Gootee, and Ann M. Youberg. "FLY A DRONE, TAKE SOME PICTURES, MAKE A MAP. APPLICATIONS OF UAVS IN GEOLOGIC MAPPING, RECONNAISSANCE, AND EDUCATING THE PUBLIC." In Joint 70th Annual Rocky Mountain GSA Section / 114th Annual Cordilleran GSA Section Meeting - 2018. Geological Society of America, 2018. http://dx.doi.org/10.1130/abs/2018rm-313958.

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Bürkle, Axel. "Collaborating miniature drones for surveillance and reconnaissance." In SPIE Europe Security + Defence, edited by Edward M. Carapezza. SPIE, 2009. http://dx.doi.org/10.1117/12.830408.

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Blank, Glenn D. "Multi-Level Re-Planning For Reconnaissance Drones." In Applications of Artificial Intelligence V, edited by John F. Gilmore. SPIE, 1987. http://dx.doi.org/10.1117/12.940644.

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Chan, Hau, Long Tran-Thanh, and Vignesh Viswanathan. "Fighting Wildfires under Uncertainty - A Sequential Resource Allocation Approach." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/596.

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Standard disaster response involves using drones (or helicopters) for reconnaissance and using people on the ground to mitigate the damage. In this paper, we look at the problem of wildfires and propose an efficient resource allocation strategy to cope with both dynamically changing environment and uncertainty. In particular, we propose Firefly, a new resource allocation algorithm, that can provably achieve optimal or near optimal solutions with high probability by first efficiently allocating observation drones to collect information to reduce uncertainty, and then allocate the firefighting units to extinguish fire. For the former, Firefly uses a combination of maximum set coverage formulation and a novel utility estimation technique, and it uses a knapsack formulation to calculate the allocation for the latter. We also demonstrate empirically by using a real-world dataset that Firefly achieves up to 80-90% performance of the offline optimal solution, even with a small amount of drones, in most of the cases.
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