Academic literature on the topic 'Autonomous drones'
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Journal articles on the topic "Autonomous drones"
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Yulianto, Ahmad Wilda, Dhandi Yudhit Yuniar, and Yoyok Heru Prasetyo. "Navigation and Guidance for Autonomous Quadcopter Drones Using Deep Learning on Indoor Corridors." Jurnal Jartel Jurnal Jaringan Telekomunikasi 12, no. 4 (December 30, 2022): 258–64. http://dx.doi.org/10.33795/jartel.v12i4.422.
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Autonomous drones require accurate navigation and localization algorithms to carry out their duties. Outdoors drones can utilize GPS for navigation and localization systems. However, GPS is often unreliable or not available at all indoors. Therefore, in this research, an autonomous indoor drone navigation model was created using a deep learning algorithm, to assist drone navigation automatically, especially in indoor corridor areas. In this research, only the Caddx Ratel 2 FPV camera mounted on the drone was used as an input for the deep learning model to navigate the drone forward without a collision with the wall in the corridor. This research produces two deep learning models, namely, a rotational model to overcome a drone's orientation deviations with a loss of 0.0010 and a mean squared error of 0.0009, and a translation model to overcome a drone's translation deviation with a loss of 0.0140 and a mean squared error of 0.011. The implementation of the two models on autonomous drones reaches an NCR value of 0.2. The conclusion from the results obtained in this research is that the difference in resolution and FOV value in the actual image captured by the FPV camera on the drone with the image used for training the deep learning model results in a discrepancy in the output value during the implementation of the deep learning model on autonomous drones and produces low NCR implementation values.
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Suzuki, Satoshi, and Kenzo Nonami. "Special Issue on Novel Technology of Autonomous Drone." Journal of Robotics and Mechatronics 33, no. 2 (April 20, 2021): 195. http://dx.doi.org/10.20965/jrm.2021.p0195.
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In the past three years, there has been rapid progress in the use of drones in society. Drones, which were previously used only experimentally in various industrial fields, are now being used in earnest in everyday operations. Drones are becoming indispensable tools in several industrial fields, such as surveying, inspection, and agriculture. At the same time, there has also been dramatic progress in autonomous drone technology. With the advancement of image processing, simultaneous localization and mapping (SLAM), and artificial intelligence technologies, many intelligent drones that apply these technologies are being researched. At the same time, our knowledge of multi-rotor helicopters, the main type of drones, has continued to deepen. As the strengths and weaknesses of multi-rotor helicopters have gradually become clearer, drones with alternate structures, such as flapping-wing drones, have come to attract renewed attention. In addition, the range of applications for drones, including passenger drones, has expanded greatly, and research on unprecedented drone operations, as well as research on systems and controls to ensure operational safety, is actively being conducted. This special issue contains the latest review, research papers, and development reports on autonomous drones classified as follows from the abovementioned perspectives. · Research on drone airframes and structures · Research on drone navigation and recognition with a focus on image processing · Research on advanced drone controls · Research and development of drone applications We hope that the readers will actively promote the use of drones in their own research and work, based on the information obtained from this special issue.
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Kang, Tae-Won, and Jin-Woo Jung. "A Drone’s 3D Localization and Load Mapping Based on QR Codes for Load Management." Drones 8, no. 4 (March 29, 2024): 130. http://dx.doi.org/10.3390/drones8040130.
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The ongoing expansion of the Fourth Industrial Revolution has led to a diversification of drone applications. Among them, this paper focuses on the critical technology required for load management using drones. Generally, when using autonomous drones, global positioning system (GPS) receivers attached to the drones are used to determine the drone’s position. However, GPS integrated into commercially available drones have an error margin on the order of several meters. This paper, proposes a method that uses fixed-size quick response (QR) codes to maintain the error of drone 3D localization within a specific range and enable accurate mapping. In the drone’s 3D localization experiment, the errors were maintained within a specific range, with average errors ranging from approximately 0 to 3 cm, showing minimal differences. During the mapping experiment, the average error between the actual and estimated positions of the QR codes was consistently around 0 to 3 cm.
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Gupta, Arpit. "Simulation and Detection of Small Drones/Suspicious UAVs in Drone Grid." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 5452–58. http://dx.doi.org/10.22214/ijraset.2021.36144.
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Today’s technology is evolving towards autonomous systems and the demand in autonomous drones, cars, robots, etc. has increased drastically in the past years. This project presents a solution for autonomous real-time visual detection and tracking of hostile drones by moving cameras equipped on surveillance drones. The algorithm developed in this project, based on state-of-art machine learning and computer vision methods, succeeds at autonomously detecting and tracking a single drone by moving a camera and can run at real-time. The project can be divided into two main parts: the detection and the tracking. The detection is based on the YOLOv3 (You Only Look Once v3) algorithm and a sliding window method. The tracking is based on the GOTURN (Generic Object Tracking Using Regression Networks) algorithm, which allows the tracking of generic objects at high speed. In order to allow autonomous tracking and enhance the accuracy, a combination of GOTURN and tracking by detection using YOLOv3 was developed.
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Ma, Jinyu, Puhui Chen, Xinhan Xiong, Liangcheng Zhang, Shengdong Yu, and Dongyuan Zhang. "Research on Vision-Based Servoing and Trajectory Prediction Strategy for Capturing Illegal Drones." Drones 8, no. 4 (March 28, 2024): 127. http://dx.doi.org/10.3390/drones8040127.
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A proposed strategy for managing airspace and preventing illegal drones from compromising security involves the use of autonomous drones equipped with three key functionalities. Firstly, the implementation of YOLO-v5 technology allows for the identification of illegal drones and the establishment of a visual-servo system to determine their relative position to the autonomous drone. Secondly, an extended Kalman filter algorithm predicts the flight trajectory of illegal drones, enabling the autonomous drone to compensate in advance and significantly enhance the capture success rate. Lastly, to ensure system robustness and suppress interference from illegal drones, an adaptive fast nonsingular terminal sliding mode technique is employed. This technique achieves finite time convergence of the system state and utilizes delay estimation technology for the real-time compensation of unknown disturbances. The stability of the closed-loop system is confirmed through Lyapunov theory, and a model-based hardware-in-the-loop simulation strategy is adopted to streamline system development and improve efficiency. Experimental results demonstrate that the designed autonomous drone accurately predicts the trajectory of illegal drones, effectively captures them using a robotic arm, and maintains stable flight throughout the process.
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Hutchinson, William. "Deceiving Autonomous Drones." International Journal of Cyber Warfare and Terrorism 10, no. 3 (July 2020): 1–14. http://dx.doi.org/10.4018/ijcwt.2020070101.
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This speculative article examines the concept of deceiving autonomous drones that are controlled by artificial intelligence (AI) and can work without operational input from humans. This article examines the potential of autonomous drones, their implications and how deception could possibly be a defence against them and /or a means of gaining advantage. It posits that officially, no truly autonomous drone is operational now, yet the development of AI and other technologies could expand the capabilities of these devices, which will inevitably confront society with a number of deep ethical, legal, and philosophical issues. The article also examines the impact of autonomous drones and their targets in terms of the power/deception nexus. The impact of surveillance and kinetic impacts on the target populations is investigated. The use of swarms can make deception more difficult although security can be breached. The Internet of Things can be considered as based on the same model as a swarm and its impact on human behaviour indicates that deception or perhaps counter-deception should be considered as a defence. Finally, the issues raised are outlined. However, this article does not provide definitive answers but, hopefully, exposes a number of issues that will stimulate further discussion and research in this general area.
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Montes-Romero, Ángel, Arturo Torres-González, Jesús Capitán, Maurizio Montagnuolo, Sabino Metta, Fulvio Negro, Alberto Messina, and Aníbal Ollero. "Director Tools for Autonomous Media Production with a Team of Drones." Applied Sciences 10, no. 4 (February 21, 2020): 1494. http://dx.doi.org/10.3390/app10041494.
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This paper proposes a set of director tools for autonomous media production with a team of drones. There is a clear trend toward using drones for media production, and the director is the person in charge of the whole system from a production perspective. Many applications, mainly outdoors, can benefit from the use of multiple drones to achieve multi-view or concurrent shots. However, there is a burden associated with managing all aspects in the system, such as ensuring safety, accounting for drone battery levels, navigating drones, etc. Even though there exist methods for autonomous mission planning with teams of drones, a media director is not necessarily familiar with them and their language. We contribute to close this gap between media crew and autonomous multi-drone systems, allowing the director to focus on the artistic part. In particular, we propose a novel language for cinematography mission description and a procedure to translate those missions into plans that can be executed by autonomous drones. We also present our director’s Dashboard, a graphical tool allowing the director to describe missions for media production easily. Our tools have been integrated into a real team of drones for media production and we show results of example missions.
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Gugan, Gopi, and Anwar Haque. "Path Planning for Autonomous Drones: Challenges and Future Directions." Drones 7, no. 3 (February 28, 2023): 169. http://dx.doi.org/10.3390/drones7030169.
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Unmanned aerial vehicles (UAV), or drones, have gained a lot of popularity over the last decade. The use of autonomous drones appears to be a viable and low-cost solution to problems in many applications. Path planning capabilities are essential for autonomous control systems. An autonomous drone must be able to rapidly compute feasible and energy-efficient paths to avoid collisions. In this study, we review two key aspects of path planning: environmental representation and path generation techniques. Common path planning techniques are analyzed, and their key limitations are highlighted. Finally, we review thirty-five highly cited publications to identify current trends in drone path planning research. We then use these results to identify factors that need to be addressed in future studies in order to develop a practical path planner for autonomous drones.
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Pikalov, Simon, Elisha Azaria, Shaya Sonnenberg, Boaz Ben-Moshe, and Amos Azaria. "Vision-Less Sensing for Autonomous Micro-Drones." Sensors 21, no. 16 (August 5, 2021): 5293. http://dx.doi.org/10.3390/s21165293.
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This work presents a concept of intelligent vision-less micro-drones, which are motivated by flying animals such as insects, birds, and bats. The presented micro-drone (named BAT: Blind Autonomous Tiny-drone) can perform bio-inspired complex tasks without the use of cameras. The BAT uses LIDARs and self-emitted optical-flow in order to perform obstacle avoiding and maze-solving. The controlling algorithms were implemented on an onboard micro-controller, allowing the BAT to be fully autonomous. We further present a method for using the information collected by the drone to generate a detailed mapping of the environment. A complete model of the BAT was implemented and tested using several scenarios both in simulation and field experiments, in which it was able to explore and map complex building autonomously even in total darkness.
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Rodriguez, Angel A., Mohammad Shekaramiz, and Mohammad A. S. Masoum. "Computer Vision-Based Path Planning with Indoor Low-Cost Autonomous Drones: An Educational Surrogate Project for Autonomous Wind Farm Navigation." Drones 8, no. 4 (April 17, 2024): 154. http://dx.doi.org/10.3390/drones8040154.
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The application of computer vision in conjunction with GPS is essential for autonomous wind turbine inspection, particularly when the drone navigates through a wind farm to detect the turbine of interest. Although drones for such inspections use GPS, our study only focuses on the computer vision aspect of navigation that can be combined with GPS information for better navigation in a wind farm. Here, we employ an affordable, non-GPS-equipped drone within an indoor setting to serve educational needs, enhancing its accessibility. To address navigation without GPS, our solution leverages visual data captured by the drone’s front-facing and bottom-facing cameras. We utilize Hough transform, object detection, and QR codes to control drone positioning and calibration. This approach facilitates accurate navigation in a traveling salesman experiment, where the drone visits each wind turbine and returns to a designated launching point without relying on GPS. To perform experiments and investigate the performance of the proposed computer vision technique, the DJI Tello EDU drone and pedestal fans are used to represent commercial drones and wind turbines, respectively. Our detailed and timely experiments demonstrate the effectiveness of computer vision-based path planning in guiding the drone through a small-scale surrogate wind farm, ensuring energy-efficient paths, collision avoidance, and real-time adaptability. Although our efforts do not replicate the actual scenario of wind turbine inspection using drone technology, they provide valuable educational contributions for those willing to work in this area and educational institutions who are seeking to integrate projects like this into their courses, such as autonomous systems.
Dissertations / Theses on the topic "Autonomous drones"
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Petkova, Mia. "Deploying drones for autonomous detection of pavement distress." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106049.
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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2016.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 57-61).
Road repair expenditure comprises a significant portion of US federal and municipal budgets. Inspection and timely maintenance are crucial preventative measures against pavement distress formation that can lower the monetary burden of repairs. Yet state of the art road inspection techniques still employ technicians to perform distress measurements manually. These methods are often too costly, time-consuming, labor-intensive and require technical expertise. Meanwhile, autonomous systems are increasingly deployed in place of human operators where tasks are monotonous and where risk of exposure to hostile conditions is great. As a time-consuming but highly repetitive task, road inspection presents a promising candidate for task automation. Automating road inspection can present significant efficiency gains that can aid agencies in responding to early signs of erosion in a timely manner. In this work, I explore the capacity of drones to perform autonomous pavement inspections. I develop a system that dispatches drones to survey an area, diagnose the presence of pavement distress in real time, and record imagery and coordinates of locations requiring repair. This system presents an alternative to on-ground inspections and tools that draw on crowd-sourced mechanisms to identify potholes. It builds on other recent technological solutions that employ remote sensing to collect and interpret data on pavement health. The results from this mission will be visualized through a web platform that can not only aid cities in consolidating a fragmented and costly data collection process, but also in minimize human error in the identification and prioritization of problem areas.
by Mia Petkova.
S.M.
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Jiang, Ziwen M. EngMassachusetts Institute of Technology. "An autonomous landing and charging system for drones." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123029.
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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 69-71).
A sensor-equipped consumer-grade drone can help collect data about the world. However, a drone's flight time today is measured in tens of minutes. Charging multiple drones in a network also requires manual assistance. In this thesis, we develop an integrated system with an autonomous charging pad that a drone can accurately land on. At the same time, the charging system allows the drone to land in environments with low visibility, especially during the nighttime. This research combined vision-based marker detection and flight control algorithms to create accurate landing procedures with different camera modules. Two charging platform designs of wireless and wired contact charging were built with the marker. The autonomous charging system enables a drone to land more accurately than the GPS-based navigation and gets charged without human assistance.
by Ziwen Jiang.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Chadha, Abhimanyu. "Vision Based Localization of Drones in a GPS Denied Environment." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99887.
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In this thesis, we build a robust end-to-end pipeline for the localization of multiple drones in
a GPS-denied environment. This pipeline would help us with cooperative formation control,
autonomous delivery, search and rescue operations etc. To achieve this we integrate a custom
trained YOLO (You Only Look Once) object detection network, for drones, with the ZED2
stereo camera system. With the help of this sensor we obtain a relative vector from the left
camera to that drone. After calibrating it from the left camera to that drone's center of mass,
we then estimate the location of all the drones in the leader drone's frame of reference. We
do this by solving the localization problem with least squares estimation and thus acquire
the location of the follower drone's in the leader drone's frame of reference. We present the
results with the stereo camera system followed by simulations run in AirSim to verify the
precision of our pipeline.Master of Science
In the recent years, technologies like Deep Learning and Machine Learning have seen many rapid developments. This has lead to the rise of fields such as autonomous drones and their application in fields such as bridge inspection, search and rescue operations, disaster management relief, agriculture, real estate etc. Since GPS is a highly unreliable sensor, we need an alternate method to be able to localize the drones in various environments in real time. In this thesis, we integrate a robust drone detection neural network with a camera which estimates the location. We then use this data to get the relative location of all the follower drones from the leader drone. We run experiments with the camera and in a simulator to show the accuracy of our results.
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Theodorakopoulos, Panagiotis. "On autonomous target tracking for UAVs." Toulouse 3, 2009. http://thesesups.ups-tlse.fr/545/.
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La plupart des applications des avions drones sont liées à l'observation d'événements au sol. En particulier, les suivi de cibles terrestres mobiles, qu'elles soient statiques, lentes ou rapides, est une tâche essentielle pour un drone. L'objectif global de la thèse est de proposer des méthodes qui permettent à un drone de suivre une cible terrestre, dans les conditions suivantes: - Le drone est de type voilure fixe équipé d'une caméra monoculaire. - Présence d'obstacles qui occultent la visibilité de zones au sol. - Existence de zones d'exclusion aérienne qui limitent le mouvement aérien. - Restrictions sur le champ de vue du capteur qui assure le suivi (caméra) - Différents comportements de la cible : elle peut évoluer librement ou sous contraintes dynamiques (cas d'une voiture par exemple), et peut être neutre ou évasive~: dans ce dernier cas, elle peut exploiter la présence d'obstacles pour éviter d'être perçue par le drone. Trois approches pour aborder ce problème sont proposées dans la thèse : - Une méthode basée aux lois de contrôle et de la navigation, - Une méthode basée sur la prédiction des déplacements de la cible, - Et une approche basée sur la théorie des jeux. Des résultats obtenus par des simulations réalistes et avec un drone sont présentés, pour évaluer et comparer les avantages et inconvénients de chacune des approches. Des extensions au cas "multi-drones" sont aussi proposéesMost applications of Unmanned Aerial Vehicles are related to events that occur on the ground. In particular, ground target tracking, be the target static, slowly moving or maneuvering at high speeds, is an essential task for UAVs. The overall objective of this thesis is to provide methods to endow a drone to autonomously track a moving ground target, under the following conditions: - A fixed wing UAV equipped with a monocular camera. - Presence of obstacles that hinder ground visibility. - No Fly Zones that limit the airspace. - Restrictions on the field of view of the observing sensor (a camera) - Various target dynamics and behavior: the target may be either moving on an open field or on a road network, and also has dynamic constraints (e. G. If it is a car). It can be neutral or evasive: in the latter case, it can exploit the presence of obstacles, denoted as "shadows" to avoid being tracked by the UAV, making the problem akin to a "hide and seek" game. The thesis proposes three approaches to tackle this problem: - A control based navigation method, - An adversarial predictive method, - And a discrete game theoretic approach. Results obtained in realistic simulations and with an actual UAV are presented to evaluate and compare the pros and cons of each approach. Extensions to the multi-drone case are also considered
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Alvarez, Custodio Maria. "Autonomous Recharging System for Drones: Detection and Landing on the Charging Platform." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-245197.
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In the last years, the use of indoor drones has increased significantly in many different areas. However, one of the main limitations of the potential of these drones is the battery life. This is due to the fact that the battery size has to be limited since the drones have a maximum payload in order to be able to take-off and maintain the flight. Therefore, a recharging process need to be performed frequently, involving human intervention and thus limiting the drones applications. In order to solve this problem, this master thesis presents an autonomous recharging system for a nano drone, the Crazyflie 2.0 by Bitcraze AB. By automating the battery recharging process no human intervention will be needed, and thus overall mission time of the drone can be considerably increased, broadening the possible applications. The main goal of this thesis is the design and implementation of a control system for the indoor nano drone, in order to control it towards a landing platform and accurately land on it. The design and implementation of an actual recharging system is carried out too, so that in the end a complete full autonomous system exists. Before this controller and system are designed and presented, a research study is first carried out to obtain a background and analyze existing solutions for the autonomous landing problem. A camera is integrated together with the Crazyflie 2.0 to detect the landing station and control the drone with respect to this station position. A visual system is designed and implemented for detecting the landing station. For this purpose, a marker from the ArUco library is used to identify the station and estimate the distance to the marker and the camera orientation with respect to it. Finally, some tests are carried out to evaluate the system. The flight time obtained is 4.6 minutes and the landing performance (the rate of correct landings) is 80%.Under de senaste åren har användningen av inomhusdrönare ökat betydligt på många olika områden. En av de största begränsningarna för dessa drönare är batteritiden. Detta beror på att batteristorleken måste begränsas eftersom drönarna har en väldigt begränsad maximal nyttolast för att kunna flyga. Därför måste de laddas ofta, vilket involverar mänskligt ingripande och därmed begränsar drönartillämpningarna. För att lösa detta problem presenterar detta examensarbete ett autonomt laddningssystem för en nanodrönare, Crazyflie 2.0. Genom att automatisera batteriladdningsprocessen behövs inget mänskligt ingrepp, och därigenom kan uppdragstiden för drönaren ökas avsevärt och bredda de möjliga tillämpningarna. Huvudmålet med denna avhandling är designen och implementationen av ett styrsystem för en inomhusdrönare, för att styra den mot en landningsplattform och landa korrekt på den. Arbetet inkluderar det faktiska laddningssystemet också, så att slutresultatet är ett fullständigt autonomt system. Innan regulatorn och systemet utformas och presenteras presenteras en genomgång av bakgrundsmaterial och analys av befintliga lösningar för problemet med autonom landning. En kamera monteras på Crazyflie 2.0 för att kunna detektera och positionera landningsstationen och styra drönaren med avseende på detta. För detektion används ArUcobibliotekets markörer vilka också gör det möjligt att räkna ut kamerans position och orientering med avseende på markören och därmed laddstationen. Slutligen utförs tester för att utvärdera systemet. Den erhållna flygtiden är 4,6 minuter och landningsprestandan (andel korrekta landningar på första försöket) är 80%.
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Ait, Saadi Amylia. "Coordination of scout drones (UAVs) in smart-city to serve autonomous vehicles." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG064.
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Le sujet des véhicules aériens sans pilote (VAP) est devenu un domaine d'étude prometteurtant dans la recherche que dans l'industrie. En raison de leur autonomie et de leur efficacitéen vol, les drones sont considérablement utilisés dans diverses applications pour différentestâches. Actuellement, l'autonomie du drone est un problème difficile qui peut avoir un impactà la fois sur ses performances et sur sa sécurité pendant la mission. Pendant le vol, les dronesautonomes sont tenus d'investiguer la zone et de déterminer efficacement leur trajectoire enpréservant leurs ressources (énergie liée à la fois à l'altitude et à la longueur de la trajectoire) et en satisfaisant certaines contraintes (obstacles et rotations d'axe). Ce problème estdéfini comme le problème de planification de trajectoire UAV qui nécessite des algorithmesefficaces pour être résolus, souvent des algorithmes d'intelligence artificielle. Dans cettethèse, nous présentons deux nouvelles approches pour résoudre le problème de planificationde trajectoire UAV. La première approche est un algorithme amélioré basé sur l'algorithmed'optimisation des vautours africains, appelé algorithmes CCO-AVOA, qui intègre la cartechaotique, la mutation de Cauchy et les stratégies d'apprentissage basées sur l'oppositiond'élite. Ces trois stratégies améliorent les performances de l'algorithme AVOA original entermes de diversité des solutions et d'équilibre de recherche exploration/exploitation. Unedeuxième approche est une approche hybride, appelée CAOSA, basée sur l'hybridation deChaotic Aquila Optimization avec des algorithmes de recuit simulé. L'introduction de lacarte chaotique améliore la diversité de l'optimisation Aquila (AO), tandis que l'algorithmede recuit simulé (SA) est appliqué comme algorithme de recherche locale pour améliorer larecherche d'exploitation de l'algorithme AO traditionnel. Enfin, l'autonomie et l'efficacitédu drone sont abordées dans une autre application importante, qui est le problème de placement du drone. La question du placement de l'UAV repose sur la recherche de l'emplacementoptimal du drone qui satisfait à la fois la couverture du réseau et la connectivité tout entenant compte de la limitation de l'UAV en termes d'énergie et de charge. Dans ce contexte, nous avons proposé un hybride efficace appelé IMRFO-TS, basé sur la combinaisonde l'amélioration de l'optimisation de la recherche de nourriture des raies manta, qui intègreune stratégie de contrôle tangentiel et d'algorithme de recherche taboueThe subject of Unmanned Aerial Vehicles (UAVs) has become a promising study field in bothresearch and industry. Due to their autonomy and efficiency in flight, UAVs are considerablyused in various applications for different tasks. Actually, the autonomy of the UAVis a challenging issue that can impact both its performance and safety during the mission.During the flight, the autonomous UAVs are required to investigate the area and determineefficiently their trajectory by preserving their resources (energy related to both altitude andpath length) and satisfying some constraints (obstacles and axe rotations). This problem isdefined as the UAV path planning problem that requires efficient algorithms to be solved,often Artificial Intelligence algorithms. In this thesis, we present two novel approachesfor solving the UAV path planning problem. The first approach is an improved algorithmbased on African Vultures Optimization Algorithm (AVOA), called CCO-AVOA algorithms,which integrates the Chaotic map, Cauchy mutation, and Elite Opposition-based learningstrategies. These three strategies improve the performance of the original AVOA algorithmin terms of the diversity of solutions and the exploration/exploitation search balance. Asecond approach is a hybrid-based approach, called CAOSA, based on the hybridization ofChaotic Aquila Optimization with Simulated Annealing algorithms. The introduction of thechaotic map enhances the diversity of the Aquila Optimization (AO), while the SimulatedAnnealing (SA) algorithm is applied as a local search algorithm to improve the exploitationsearch of the traditional AO algorithm. Finally, the autonomy and efficiency of the UAVare tackled in another important application, which is the UAV placement problem. Theissue of the UAV placement relays on finding the optimal UAV placement that satisfies boththe network coverage and connectivity while considering the UAV's limitation from energyand load. In this context, we proposed an efficient hybrid called IMRFO-TS, based on thecombination of Improved Manta Ray Foraging Optimization, which integrates a tangentialcontrol strategy and Tabu Search algorithms
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ADER, MARIA, and DAVID AXELSSON. "Drones in arctic environments." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217918.
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This is a master thesis by Maria Ader and David Axelsson, students at the Master of Science in Engineering degree program in Design and Product Realization at KTH, within the master program Integrated Product Design. The thesis work will benefit ÅF and the EU project ɪɴᴛᴇʀᴀᴄᴛ. The ɪɴᴛᴇʀᴀᴄᴛ project is part of the EU’s effort to forward climate research, and aims to “coordinate and harmonize research and monitoring efforts that will greatly contribute to our knowledge and understanding of changes occurring in the arctic environment.” One out of 12 subprojects within ɪɴᴛᴇʀᴀᴄᴛ aims to “increase awareness of drone technology and sensors among researchers and research station managers while making industry aware of innovative potential uses requiring drone and sensor development.” A drone is an unmanned aerial system/vehicle (UAS/UAV), i.e. an airborne vehicle without a human pilot aboard. This master thesis examines the need of drones at the ɪɴᴛᴇʀᴀᴄᴛ research stations and how arctic climates affect drone technology and the ergonomics of piloting a drone. The thesis also provides an overview of the current state of the drone market and the laws and regulations that affect the use of drones. A survey was distributed within ɪɴᴛᴇʀᴀᴄᴛ to map the researchers’ need of, and attitudes towards, drones, followed by exhaustive interviews with researchers and other key figures. Field testing at Tarfala Research Station provided complementing data. The primary insight from the study was that the researchers’ need, as well as the tasks and methods that they employ, vary greatly. Another insight was that many researchers want to use drones primarily as a sensor platform to collect data from large areas in a short time span. A situation-based drone recommendation and a concept proposal for a simple water sampling solution were made based on the results of the studyDetta är ett examensarbete utfört av Maria Ader och David Axelsson, studenter på civilingenjörsprogrammet Design och Produktframtagning på KTH, med masterinriktning Teknisk Design. Arbetet är utfört åt ÅF i syfte att bidra till EU-projektet ɪɴᴛᴇʀᴀᴄᴛ. Iɴᴛᴇʀᴀᴄᴛ är EU:s satsning på klimatforskning i Arktis och syftar till att “koordinera och harmonisera forskning och miljöbevakning som bidrar till vår kunskap och förståelse av förändringar som sker i de arktiska miljöerna.” Ett av tolv delprojekt inom ɪɴᴛᴇʀᴀᴄᴛ-projektet syftar till att öka medvetenheten om drönarteknologi och sensorer bland forskare och föreståndare på forskningsstationerna inom ɪɴᴛᴇʀᴀᴄᴛ, samt att göra drönarindustrin medveten om nya potentiella användningsområden. En drönare är ett obemannat luftfartyg, d.v.s. en flygfarkost utan pilot ombord. Drönare benämns ibland som “UAS” och “UAV”. I den här rapporten används främst den engelska termen “drones”. Detta examensarbete undersöker behovet av drönare på de forskningsstationer som är delaktiga i ɪɴᴛᴇʀᴀᴄᴛ och hur det arktiska klimatet påverkar drönartekniken och ergonomin. Arbetet kartlägger även drönarmarknaden och de lagar och regler som påverkar användandet av drönare. En utförlig studie genomfördes, där forskarnas behov av drönare undersöktes. En enkät skickades ut inom ɪɴᴛᴇʀᴀᴄᴛ och utförliga intervjuer genomfördes med forskare och andra nyckelpersoner. Ett studiebesök på Tarfala forskningsstation kompletterade med fältdata. Den främsta insikten från studien var att behov, arbetsuppgifter och metoder varierar mycket mellan de olika forskarna. En annan insikt var att många ville använda drönare som sensorbärare, och på så sätt insamla data från stora områden på kort tid. Resultatet från studien låg till grund för en situationsbaserad drönarrekommendation samt ett konceptförslag för en enkel vattenprovtagningslösning.
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Falomir, Bagdassarian Ema. "Calcul dynamique de chemin par des drones autonomes en essaim compact dans le cadre de missions en environnements complexes." Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0450.
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Les drones sont aujourd’hui devenus des outils incontournables dans de nombreux domaines, aussi bien civils que militaires. Afin de diminuer la charge de travail de leurs opérateurs, certaines étapes des missions qui leur sont confiées peuvent être automatisées, jusqu’à atteindre l’autonomie complète de la plateforme qui opèrera sans intervention humaine. La mobilité en environnement complexe est une problématique majeure à traiter lorsqu’il est question d’autonomie. En effet, un drone qui découvre son environnement au fur et à mesure de ses déplacements doit régulièrement calculer un chemin vers son point cible, chemin qui doit éviter les obstacles présents sur le trajet. Dans ce contexte, la contribution principale de cette thèse est une méthode de calcul de chemin pour un drone autonome évoluant en environnement complexe. De plus, si la mission est trop complexe pour être réalisée par un seul drone (par exemple car elle nécessite l’utilisation de nombreux capteurs ou se déroule sur une zone étendue), alors on utilise un essaim composé de plusieurs appareils autonomes, qui pourront, collaborativement, remplir la mission donnée. En revanche, même si l’utilisation conjointe de plusieurs plateformes collaboratives permet d’augmenter les performances du système global, de nouvelles problématiques apparaissent, telles que la gestion des communications et l’évitement des drones voisins. La méthode de calcul de chemin que nous proposons prend en compte ces problématiques et est donc adaptée à l’utilisation par les drones d’un essaimDrones are major tools today to perform numerous tasks in a wide range of operations, both for military and civil activities. In order to reduce operators’ workload, some parts of the drones’ missions can be automated, until achieving a full autonomy of the platforms, which will reach their mission without any human intervention. Path planning is a major task to achieve autonomy. Indeed, the drones have to detect obstacles while it discovers its environment and dynamically calculate a path avoiding obstacle and allowing to fulfil their mission. In this context, our main contribution is a path planning method for an autonomous drone evolving in complex environment.Furthermore, the variety of tasks one would like to achieve increases quicker than the capacity of a single UAV, therefore one can use a swarm composed of several autonomous platforms. Usage of several drones increases the number and/or the variety of sensors, and the collaboration between them enhance the global system capabilities by supporting some form of resilience. However, swarming introduced new issues, such as communications and drone avoidance. Our path planning method considers these problematics and then, is adapted for drones forming a swarm
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Le, Barz Cédric. "Navigation visuelle pour les missions autonomes des petits drones." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066424/document.
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Lors de dette dernière décennie, l'évolution des technologies a permis le développement de drones de taille et de poids réduit aptes à évoluer dans des environnements intérieurs ou urbains. Pour exécuter les missions qui leur sont attribuées, les drones doivent posséder un système de navigation robuste, comprenant, notamment, une fonctionnalité temps réel d'ego-localisation précise dans un repère absolu. Nous proposons de résoudre cette problématique par la mise en correspondance des dernières images acquises avec des images géoréférencées de type Google Streetview.Dans l'hypothèse où il serait possible pour une image requête de retrouver l'image géo-référencée représentant la même scène, nous avons tout d'abord étudié une solution permettant d'affiner la localisation grâce à l'estimation de la pose relative entre ces deux images. Pour retrouver l'image de la base correspondant à une image requête, nous avons ensuite étudié et proposé une méthode hybride exploitant à la fois les informations visuelles et odométriques mettant en oeuvre une chaîne de Markov à états cachés. Les performances obtenues, dépendant de la qualité de la mesure de similarité visuelle, nous avons enfin proposé une solution originale basée sur l'apprentissage supervisé de distances permettant de mesurer les similarités entre les images requête et les images géoréférencées proches de la position supposéeIn this last decade, technology evolution has enabled the development of small and light UAV able to evolve in indoor and urban environments. In order to execute missions assigned to them, UAV must have a robust navigation system, including a precise egolocalization functionality within an absolute reference. We propose to solve this problem by mapping the latest images acquired with geo-referenced images, i.e. Google Streetview images.In a first step, assuming that it is possible for a given query image to retrieve the geo-referenced image depicting the same scene, we study a solution, based on relative pose estimation between images, to refine the location. Then, to retrieve geo-referenced images corresponding to acquired images, we studied and proposed an hybrid method exploiting both visual and odometric information by defining an appropriate Hidden Markov Model (HMM), where states are geographical locations. The quality of achieved performances depending of visual similarities, we finally proposed an original solution based on a supervised metric learning solution. The solution measures similarities between the query images and geo-referenced images close to the putative position, thanks to distances learnt during a preliminary step
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Le, Barz Cédric. "Navigation visuelle pour les missions autonomes des petits drones." Electronic Thesis or Diss., Paris 6, 2015. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2015PA066424.pdf.
Full textAbstract:
Lors de dette dernière décennie, l'évolution des technologies a permis le développement de drones de taille et de poids réduit aptes à évoluer dans des environnements intérieurs ou urbains. Pour exécuter les missions qui leur sont attribuées, les drones doivent posséder un système de navigation robuste, comprenant, notamment, une fonctionnalité temps réel d'ego-localisation précise dans un repère absolu. Nous proposons de résoudre cette problématique par la mise en correspondance des dernières images acquises avec des images géoréférencées de type Google Streetview.Dans l'hypothèse où il serait possible pour une image requête de retrouver l'image géo-référencée représentant la même scène, nous avons tout d'abord étudié une solution permettant d'affiner la localisation grâce à l'estimation de la pose relative entre ces deux images. Pour retrouver l'image de la base correspondant à une image requête, nous avons ensuite étudié et proposé une méthode hybride exploitant à la fois les informations visuelles et odométriques mettant en oeuvre une chaîne de Markov à états cachés. Les performances obtenues, dépendant de la qualité de la mesure de similarité visuelle, nous avons enfin proposé une solution originale basée sur l'apprentissage supervisé de distances permettant de mesurer les similarités entre les images requête et les images géoréférencées proches de la position supposéeIn this last decade, technology evolution has enabled the development of small and light UAV able to evolve in indoor and urban environments. In order to execute missions assigned to them, UAV must have a robust navigation system, including a precise egolocalization functionality within an absolute reference. We propose to solve this problem by mapping the latest images acquired with geo-referenced images, i.e. Google Streetview images.In a first step, assuming that it is possible for a given query image to retrieve the geo-referenced image depicting the same scene, we study a solution, based on relative pose estimation between images, to refine the location. Then, to retrieve geo-referenced images corresponding to acquired images, we studied and proposed an hybrid method exploiting both visual and odometric information by defining an appropriate Hidden Markov Model (HMM), where states are geographical locations. The quality of achieved performances depending of visual similarities, we finally proposed an original solution based on a supervised metric learning solution. The solution measures similarities between the query images and geo-referenced images close to the putative position, thanks to distances learnt during a preliminary step
Books on the topic "Autonomous drones"
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Blubaugh, David Allen, Steven D. Harbour, Benjamin Sears, and Michael J. Findler. Intelligent Autonomous Drones with Cognitive Deep Learning. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-6803-2.
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Facility, Dryden Flight Research, ed. Autonomous RPRV navigation guidance and control. Hawthorne, Calif: Systems Technology, Inc., 1989.
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United States. National Aeronautics and Space Administration., ed. Search problems in mission planning and navigation of autonomous aircraft. West Lafayette, Ind: Purdue University, School of Aeronautics and Astronautics, 1988.
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United States. National Aeronautics and Space Administration. Aeronautics Research Mission Directorate, ed. Crash course: Lessons learned from accidents involving remotely piloted and autonomous aircraft. Washington, DC: National Aeronautics and Space Administration, 2013.
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Musial, Marek. System architecture of small autonomous UAVs: Requirements and design approaches in control, communication, and data processing. Saarbrücken: VDM Verlag Dr. Müller, 2008.
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Kastanas, Elias. Unité et diversité: Notions autonomes et marge d'appréciation des Etats dans la jurisprudence de la Cour européenne des droits de l'homme. Bruxelles: E. Bruylant, 1996.
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Sebbane, Yasmina Bestaoui. Smart Autonomous Aircraft. Taylor & Francis Group, 2020.
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Statman, Daniel. Drones and Robots. Edited by Seth Lazar and Helen Frowe. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199943418.013.9.
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The recent development of unmanned technology—drones and robots of various types—is transforming the nature of warfare. Instead of fighting against other human beings, combatants will soon be fighting against machines. At present, these machines are operated by human beings, but they are becoming increasingly autonomous. Some people believe that, from a moral point of view, this development is worrisome, especially insofar as fully autonomous offensive systems (‘killer robots’) are concerned. I claim that the arguments that support this belief are pretty weak. Compared with the grand battles of the past, with their shockingly high toll of casualties, drone-centered campaigns seem much more humane. They also enable a better fit between moral responsibility and vulnerability to defensive action. Drones and robots may well be recorded in the annals of warfare as offering real promise for moral progress.
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Smart Autonomous Aircraft: Flight Control and Planning for UAV. Taylor & Francis Group, 2015.
Find full textBook chapters on the topic "Autonomous drones"
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Schulzke, Marcus. "Evaluating Autonomous Drones." In The Morality of Drone Warfare and the Politics of Regulation, 149–71. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/978-1-137-53380-7_6.
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Mukerji, Nikil. "Autonomous Killer Drones." In Drones and Responsibility, 197–214. Farnham, Surrey, UK England ; Burlington, VT : Ashgate, 2016. | Series: Emerging technologies, ethics and international affairs: Routledge, 2016. http://dx.doi.org/10.4324/9781315578187-12.
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Hoek, Marga. "Autonomous Vehicles and Drones." In Tech For Good, 217–52. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003392064-7.
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Saxon, Dan. "Autonomous Drones and Individual Criminal Responsibility." In Drones and Responsibility, 17–46. Farnham, Surrey, UK England ; Burlington, VT : Ashgate, 2016. | Series: Emerging technologies, ethics and international affairs: Routledge, 2016. http://dx.doi.org/10.4324/9781315578187-2.
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Kumar, Kirshna, Sushil Kumar, and Rupak Kharel. "Autonomous and Connected UAVs/Drones." In Secure and Digitalized Future Mobility, 17–31. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/b22998-2.
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Müller, Vincent C. "Autonomous Killer Robots are Probably Good News 1." In Drones and Responsibility, 67–81. Farnham, Surrey, UK England ; Burlington, VT : Ashgate, 2016. | Series: Emerging technologies, ethics and international affairs: Routledge, 2016. http://dx.doi.org/10.4324/9781315578187-4.
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López, Luis Bausá, Niels van Manen, Erik van der Zee, and Steven Bos. "DroneAlert: Autonomous Drones for Emergency Response." In Multi-Technology Positioning, 303–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50427-8_15.
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Dahlmann, Anja. "Drones and Lethal Autonomous Weapon Systems." In Armament, Arms Control and Artificial Intelligence, 159–73. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11043-6_12.
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Santamaria-Navarro, Angel, Rohan Thakker, David D. Fan, Benjamin Morrell, and Ali-akbar Agha-mohammadi. "Towards Resilient Autonomous Navigation of Drones." In Springer Proceedings in Advanced Robotics, 922–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95459-8_57.
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Dwivedi, Kriti, Priyanka Govindarajan, Deepika Srinivasan, A. Keerthi Sanjana, Ramani Selvanambi, and Marimuthu Karuppiah. "Intelligent Autonomous Drones in Industry 4.0." In Artificial Intelligence and Cyber Security in Industry 4.0, 133–63. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2115-7_6.
Full textConference papers on the topic "Autonomous drones"
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Velazquez, Eric Marin, and Sudhanshu Kumar Semwal. "Using Autonomous Drones Interactions towards Mobile Personal Spaces for Indoor Environments." In WSCG'2021 - 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2021. Západočeská univerzita, 2021. http://dx.doi.org/10.24132/csrn.2021.3002.14.
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We propose an extension of a recent work using convo-lutional neural networks and drones, such as Learning tofly by using DroNet [8] that can possibly safely drive adrone autonomously. The combination of (i) the DroNetarchitecture and weights to apply to CNNs to avoid thecrashes; (ii) combining it with DLIB tracker, a corre-lation implemented tracker based on Danelljan et al.’spaper [3] work; (iii) the extraction of descriptors usingSpeeded Up Robust Features [1]; and (iv) Fast Libraryfor Approximate Nearest Neighbors [10] for the featurematching – leads a drone to track any object and avoidcrashes autonomously without any prior informationabout the object. The main goal is to create a partnershipbetween the drone(s) and the participant as the dronefollows the participant and avoids collisions. Our workextends existing methods to also included a way for adrone to follow a person even if the person is hiddenfor a few frames. Our algorithms also work in low/poorambient light satisfactorily. In future, our technique canbe used to provide novel indoor applications for drones.
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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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Iob, Pietro, Luca Frau, Piero Danieli, Lorenzo Olivieri, and Carlo Bettanini. "Avalanche Rescue with Autonomous Drones." In 2020 IEEE 7th International Workshop on Metrology for AeroSpace (MetroAeroSpace). IEEE, 2020. http://dx.doi.org/10.1109/metroaerospace48742.2020.9160116.
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Chow, Nicole, Gopi Gugan, and Anwar Haque. "RADR: Routing for Autonomous Drones." In 2019 15th International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE, 2019. http://dx.doi.org/10.1109/iwcmc.2019.8766530.
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Bregu, Endri, Nicola Casamassima, Daniel Cantoni, Luca Mottola, and Kamin Whitehouse. "Reactive Control of Autonomous Drones." In MobiSys'16: The 14th Annual International Conference on Mobile Systems, Applications, and Services. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2906388.2906410.
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Milhouse, Mark O. "Framework for Autonomous Delivery Drones." In the 4th Annual ACM Conference. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2808062.2808075.
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Wang, Qing, Chenren Xu, Supeng Leng, and Sofie Pollin. "When Autonomous Drones Meet Driverless Cars." In MobiSys '18: The 16th Annual International Conference on Mobile Systems, Applications, and Services. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3210240.3210806.
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Mitcheson, Paul D., David Boyle, George Kkelis, David Yates, Juan Arteaga Saenz, Samer Aldhaher, and Eric Yeatman. "Energy-autonomous sensing systems using drones." In 2017 IEEE SENSORS. IEEE, 2017. http://dx.doi.org/10.1109/icsens.2017.8234092.
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Godio, Simone, Francesco Marino, Alessandro Minervini, Stefano Primatesta, Marcello Chiaberge, and Giorgio Guglieri. "Autonomous Drones in GNSS-Denied Environments: Results from the Leonardo Drone Contest." In 2023 IEEE 10th International Workshop on Metrology for AeroSpace (MetroAeroSpace). IEEE, 2023. http://dx.doi.org/10.1109/metroaerospace57412.2023.10190003.
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Hutton, Courtney. "Augmented Reality Interfaces for Semi-Autonomous Drones." In 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). IEEE, 2019. http://dx.doi.org/10.1109/vr.2019.8797893.
Full textReports on the topic "Autonomous drones"
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Kulhandjian, Hovannes. AI-Based Bridge and Road Inspection Framework Using Drones. Mineta Transportation Institute, November 2023. http://dx.doi.org/10.31979/mti.2023.2226.
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There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge and road inspection using drones with multiple sensors collecting capabilities. It is not sufficient to conduct inspections of bridges and roads using cameras alone, so the research team utilized an infrared (IR) camera along with a high-resolution optical camera. In many instances, the IR camera can provide more details to the interior structural damages of a bridge or a road surface than an optical camera, which is more suitable for inspecting damages on the surface of a bridge or a road. In addition, the drone inspection system is equipped with a minicomputer that runs Machine Learning algorithms. These algorithms enable autonomous drone navigation, image capture of the bridge or road structure, and analysis of the images. Whenever any damage is detected, the location coordinates are saved. Thus, the drone can self-operate and carry out the inspection process using advanced AI algorithms developed by the research team. The experimental results reveal the system can detect potholes with an average accuracy of 84.62% using the visible light camera and 95.12% using a thermal camera. This developed bridge and road inspection framework can save time, money, and lives by automating and having drones conduct major inspection operations in place of humans.
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Alexander, Serena, Bo Yang, Owen Hussey, and Derek Hicks. Examining the Externalities of Highway Capacity Expansions in California: An Analysis of Land Use and Land Cover (LULC) Using Remote Sensing Technology. Mineta Transportation Institute, November 2023. http://dx.doi.org/10.31979/mti.2023.2251.
Full textAbstract:
There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge and road inspection using drones with multiple sensors collecting capabilities. It is not sufficient to conduct inspections of bridges and roads using cameras alone, so the research team utilized an infrared (IR) camera along with a high-resolution optical camera. In many instances, the IR camera can provide more details to the interior structural damages of a bridge or a road surface than an optical camera, which is more suitable for inspecting damages on the surface of a bridge or a road. In addition, the drone inspection system is equipped with a minicomputer that runs Machine Learning algorithms. These algorithms enable autonomous drone navigation, image capture of the bridge or road structure, and analysis of the images. Whenever any damage is detected, the location coordinates are saved. Thus, the drone can self-operate and carry out the inspection process using advanced AI algorithms developed by the research team. The experimental results reveal the system can detect potholes with an average accuracy of 84.62% using the visible light camera and 95.12% using a thermal camera. This developed bridge and road inspection framework can save time, money, and lives by automating and having drones conduct major inspection operations in place of humans.
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Christensen, Lance. PR-459-133750-R03 Fast Accurate Automated System To Find And Quantify Natural Gas Leaks. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), November 2019. http://dx.doi.org/10.55274/r0011633.
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Miniature natural gas sensors weighing a few hundred grams with 10 ppb s-1 sensitivity towards methane and ppb s-1 sensitivity towards methane and ethane present the energy industry with cost effective ways to improve safety, comply with State and Federal regulations, decrease natural gas emissions, and attribute natural gas indications to thermogenic or biogenic sources. One particularly promising implementation is on small unmanned aerial systems (sUASs) flown by service providers or in-house personnel or even more ambitiously as part of larger network conducting autonomous, continual monitoring. This report describes refinement of the OPLS measurement system to include all ancillary instruments needed to put OPLS methane and ethane measurements into context for leak surveillance, localization, and quantification. Flights were conducted on a variety of VTOLs and fixed wing drones as described below to ensure that the overall system development resulted in a system that was platform agnostic. This report describes: - The complete agnostic OPLS measurement system.The individual components are described and their performance investigated.Technical issues that arose during testing and field deployment are described. - Field experiments of the refined OPLS measurement system at a real-world oil and gas production site.These experiments exercise the OPLS system's ability to do leak surveillance, localization, and quantification. - Laboratory development of the OPLS instrument to improve its performance in terms of signal-to-noise and accuracy. - Field experiments demonstrating the successful application of OPLS on a fixed-wing hybrid flown at altitudes higher than 50 m. - Field experiments demonstrating the utility of source attribution using the ethane measurement capability. There is a related webinar.
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McQueen, Bob. Unsettled Issues in Advanced Air Mobility Certification. SAE International, June 2021. http://dx.doi.org/10.4271/epr2021014.
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Advanced air mobility (AAM) refers to urban transportation systems that move people and goods by air. This has significant implications for reducing traffic congestion in cities and for providing an integrated approach to urban mobility. With the emergence of drone technology and the possibility of more autonomous aircraft, interest has grown considerably in AAM. Unsettled Issues in Advanced Air Mobility Certification discusses the impact of AAM on private sector solution providers including aerospace and technology companies and goes into solutions for urban planners and transportation professionals for better integration across all AAM modes.