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1
Noizet, Thomas. "Les essaims de drones, graal ou chimère ?" Revue Défense Nationale N° Hors-série, HS13 (September 20, 2023): 239–56. http://dx.doi.org/10.3917/rdna.hs13.0239.
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Dronisation et intelligence artificielle convergent aujourd’hui, donnant progressivement naissance à ce qu’il est convenu d’appeler des « essaims ». Les récents conflits renforcent l’idée que ces systèmes – y compris les essaims létaux – présentent un intérêt militaire. Ils feront partie demain des capacités amies et ennemies. Celui qui en sera doté disposera d’un avantage certain. Cependant, leur développement implique de consentir à une forme d’autonomie, ce qui suscite de nombreuses questions. Notre pays sera guidé par sa vision de l’éthique d’emploi des essaims et des armes dites « autonomes ». Pour autant, il convient d’avancer de manière résolue dans la voie des essaims, y compris vers les essaims armés.
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Abergel, Violette, Renato Saleri, and Hervé Lequay. "Vecteurs aériens téléopérés pour l'acquisition de données spatiales d'objets patrimoniaux, Retour d'expérience." Revue Française de Photogrammétrie et de Télédétection, no. 213 (April 27, 2017): 73–79. http://dx.doi.org/10.52638/rfpt.2017.363.
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Le relevé aérien constitue, dans le domaine de l'archéologie et de l'architecture, un vaste champ d'applications et d'intérêt. Au-delà des capacités croissantes des drones existants, le potentiel opérationnel de tels outils dépasse aujourd'hui l'imagination de leur propres créateurs : initialement conçus dans le secteur de la robotique civile et militaire, de récentes expérimentations ont pu tester le vol autonome, la prise de décision automatique, ainsi que l'intelligence distribuée.Avec près de dix ans d'expérience dans le domaine du relevé aérien, le laboratoire MAP a expérimenté un large panel de vecteurs aériens, pilotés ou semi-autonomes, équipés de capteurs de différentes natures, à des fns de télédétection et d'acquisition de données spatiales, ce travail étant principalement dévolu à la modélisation et à la simulation d'environnements 3D à haute valeur patrimoniale. Les compétences du MAP concernent donc la mobilisation de vecteurs aériens sans équipage, mais aussi le développement d'outils experts pour la modélisation et représentation 3D ou encore les protocoles d'analyse d'image dans le domaine de l'architecture, de l'urbanisme et du paysage.
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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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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.
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Martin, Olivier, Christophe Meynard, Marc Pierrot-Deseilligny, Jean-Philippe Souchon, and Christian Thom. "Réalisation d'une caméra photogrammétrique ultralégère et de haute résolution." Revue Française de Photogrammétrie et de Télédétection, no. 213 (April 26, 2017): 3–9. http://dx.doi.org/10.52638/rfpt.2017.200.
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Ces dernières années, l'IGN a participé à des expériences de photogrammétrie très haute résolution au coursdesquelles des appareils photo numérique (APN) ont été utilisés sur différents types de drones. Cela nous a permisd'affiner les caractéristiques techniques importantes que devrait proposer une nouvelle caméra photogrammétrique ultralégèreet de très haute résolution dédiée à ce type d'application. Le LOEMI, laboratoire en instrumentation de l’IGN, fortde l'expérience qu'il a acquise en concevant et réalisant les caméras aériennes numériques de l'IGN, s'est lancé dans laconception de ce nouvel imageur en 2012 après avoir étudié les possibilités offertes par le marché en termes de capteuret de composant "cerveau" de la caméra. La caméra sera basée sur un capteur CMOS et son électronique d'acquisitionet de traitements sur un SoC+FPGA de la société Xilinx. Grâce à la conception modulaire du dispositif, on pourradisposer, en fonction du porteur utilisé, de configurations plus ou moins autonomes et donc plus ou moins lourdes, ouavec plus ou moins de connectivité ou de capteurs annexes. La configuration la plus légère sera constituée du seul"étage imageur" dont une version est, en ce début 2014, en cours de test. Les premiers prototypes fonctionnelsdevraient être réalisés d'ici la fin de l'année 2014.
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Rojas-Perez, Leticia Oyuki, and Jose Martinez-Carranza. "DeepPilot: A CNN for Autonomous Drone Racing." Sensors 20, no. 16 (August 13, 2020): 4524. http://dx.doi.org/10.3390/s20164524.
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Autonomous Drone Racing (ADR) was first proposed in IROS 2016. It called for the development of an autonomous drone capable of beating a human in a drone race. After almost five years, several teams have proposed different solutions with a common pipeline: gate detection; drone localization; and stable flight control. Recently, Deep Learning (DL) has been used for gate detection and localization of the drone regarding the gate. However, recent competitions such as the Game of Drones, held at NeurIPS 2019, called for solutions where DL played a more significant role. Motivated by the latter, in this work, we propose a CNN approach called DeepPilot that takes camera images as input and predicts flight commands as output. These flight commands represent: the angular position of the drone’s body frame in the roll and pitch angles, thus producing translation motion in those angles; rotational speed in the yaw angle; and vertical speed referred as altitude h. Values for these 4 flight commands, predicted by DeepPilot, are passed to the drone’s inner controller, thus enabling the drone to navigate autonomously through the gates in the racetrack. For this, we assume that the next gate becomes visible immediately after the current gate has been crossed. We present evaluations in simulated racetrack environments where DeepPilot is run several times successfully to prove repeatability. In average, DeepPilot runs at 25 frames per second (fps). We also present a thorough evaluation of what we called a temporal approach, which consists of creating a mosaic image, with consecutive camera frames, that is passed as input to the DeepPilot. We argue that this helps to learn the drone’s motion trend regarding the gate, thus acting as a local memory that leverages the prediction of the flight commands. Our results indicate that this purely DL-based artificial pilot is feasible to be used for the ADR challenge.
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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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Muñoz-Gómez, Antonio-Miguel, Juan-Manuel Marredo-Píriz, Javier Ballestín-Fuertes, and José-Francisco Sanz-Osorio. "A Novel Charging Station on Overhead Power Lines for Autonomous Unmanned Drones." Applied Sciences 13, no. 18 (September 10, 2023): 10175. http://dx.doi.org/10.3390/app131810175.
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Innovative drone-based technologies provide novel techniques to guarantee the safety and quality of power supply and to perform these tasks more efficiently. Electric multirotor drones, which are at the forefront of technology, face significant flight time limitations due to battery capacity and weight constraints that limit their autonomous operation. This paper presents a novel drone charging station that harvests energy from the magnetic field present in power lines to charge the drone’s battery. This approach relies on a charging station that is easy to install by the drone on an overhead AC power line without modifying the electrical infrastructure. This paper analyses the inductive coupling between the energy harvester and the power line, electrical protection, the power electronics required for maximum power point tracking and the mechanical design of the charging station. A drone that perches on a cable, an end effector for installation procedures and the charging maneuver are described, along with discussion of the robotic and electrical tests performed in a relevant environment. Finally, a lightweight drone charging station capable of harvesting 145 W of power from a 600 A line current is reported.
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Sondors, Marks, Ilmārs Apeināns, and Sergejs Kodors. "AUTONOMOUS UNMANNED DRONES FLIGHT PLANNING, USING A MODIFIED SHORTEST PATH ALGORITHM WITH A LIMITED TIME FRAME." HUMAN. ENVIRONMENT. TECHNOLOGIES. Proceedings of the Students International Scientific and Practical Conference, no. 27 (October 30, 2023): 14–18. http://dx.doi.org/10.17770/het2023.27.7375.
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The aim of this work is to develop an algorithm to find the shortest path for drone flight planning with a limited time frame. Author used the local search shortest path algorithm to find the most efficient algorithm to use for further modification to apply to a drones flight calculation. The algorithm was modified to use the distance between points as a unit of time to limit the flight path length depending on the drone's maximum flight time. As a result of the work, an algorithm was created which, upon receiving an array of points, finds the shortest distance between the points, but when it reaches the limit of the flight duration, it returns to the drone station to charge, and resumes flight once it’s done charging.
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Gupta, Myra. "Reinforcement Learning for Autonomous Drone Navigation." Innovative Research Thoughts 9, no. 5 (2023): 11–20. http://dx.doi.org/10.36676/irt.2023-v9i5-002.
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Drone navigation involves the process of controlling the movement and flight path of unmanned aerial vehicles (UAVs). It encompasses both the hardware and software systems that enable drones to navigate and maneuver autonomously or under the guidance of a human operator. The utility of drone navigation is vast and varied, making it a critical component in numerous industries and applications. Firstly, drone navigation plays a crucial role in aerial surveillance and reconnaissance. Drones equipped with advanced navigation systems can efficiently patrol large areas, monitor activities, and gather real-time data from various perspectives. This capability is particularly valuable in security and law enforcement operations, disaster response, and environmental monitoring, where access and visibility might be limited.
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Anderson, Kara. "Autonomous Archaeological Authority: The Future of Drone Use and Privacy Laws in Cultural Heritage Preservation." Journal of Air Law and Commerce 88, no. 3 (2023): 635. http://dx.doi.org/10.25172/jalc.88.3.4.
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Since ancient times, humanity has placed high value on natural and cultural phenomena, with Philo of Byzantium recording the first list of the “Seven Wonders of the Ancient World” as early as 225 B.C.E. Similarly, modern world leaders continue to recognize the value of these and more sites through preserving them as United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage sites. With the advancement of drone technology, researchers now employ drones to aid preservation efforts since drones can enter dangerous and humanly-inaccessible spaces, provide detailed images of sites the human eye cannot see, and assist governments in identifying illegal looting. However, while many countries have developed drone use regulations, the challenging ethical questions drones pose regarding privacy rights have resulted in a lack of drone-specific privacy regulations. As countries create new legislation to fill the policy gaps, the tension between protecting privacy rights and preserving cultural heritage results in an unclear future for the use of drones for site preservation. Section II of this Comment analyzes the history of World Heritage sites, drone development, and their intersection to understand the vital role drones play in site preservation. Subsequently, Section III conducts a comparative analysis of drone-use and privacy regulations in four countries with the greatest amount of UNESCO sites to identify the current status of global drone laws. Finally, Section IV addresses the lack of drone-specific privacy regulation and asserts potential implications new drone legislation could have on preservation efforts while postulating methods to protect preservation drone use.
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Gajana, Kanade Dnyaneshwar. "Medical Supplies Delivery Autonomous Drone with Security." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 6022–30. http://dx.doi.org/10.22214/ijraset.2024.61335.
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Abstract: Drones and UAVs have gained a lot of attention in the recent years. Being fast and efficient, they have the capability to carry out a multitude of tasks efficiently and quickly. One of the critical applications of drones is in the field of healthcare is the delivery of medical supplies. In case of severe natural calamities, only a drone is suitable for providing the basic essential supplies that can save the lives of constrained survivors. In rural and underdeveloped areas, a drone is ideal for delivery. The usage of a medical drone is not restricted as it can easily be used in day-to-day urban and sub-urban areas. It is an efficient method to deliver medicines and aid to patients and those in need.
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Haque, Ahshanul, and Md Naseef-Ur-Rahman Chowdhury. "Exploring the Benefits of Reinforcement Learning for Autonomous Drone Navigation and Control." International Journal of Advanced Networking and Applications 15, no. 01 (2023): 5808–14. http://dx.doi.org/10.35444/ijana.2023.15110.
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Drones are now used in a wide range of industries, including delivery services and agriculture. Notwithstanding, controlling robots in powerful conditions can be testing, particularly while performing complex assignments. Conventional strategies for drone mechanization depend on pre-customized directions, restricting their adaptability and versatility. Drones can learn from their interactions with their environment and improve their performance over time with the help of reinforcement learning (RL), which has emerged as a promising method for drone automation in recent years. This paper looks at how RL can be used to automate drones and how it can be used in different industries. In addition, the difficulties of RL-based drone automation and potential directions for future research are discussed in the paper.
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Vogel, Olga, and Annette Kluge. "Humanzentrierte Implementierung von (teil-)autonomen Drohnen." Zeitschrift für wirtschaftlichen Fabrikbetrieb 119, no. 5 (April 30, 2024): 324–30. http://dx.doi.org/10.1515/zwf-2024-1063.
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Abstract The industrial use of drones is constantly increasing due to the transition from Industry 4.0 to Industry 5.0. A prerequisite for the concrete implementation is the legal and organizational risk assessment of flight robotics. The core of the article is a systematic overview of relevant human-centered risk factors for the adaptation of drones in organizations. Based on the proposed risk taxonomy, design options for human-drone interaction and an overview of key questions for risk assessment are presented.
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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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Kashkarov, Anton, Volodymyr Diordiiev, Andrii Sabo, and Gennadii Novikov. "Semi-Autonomous Drone for Agriculture on the Tractor Base." Acta Technologica Agriculturae 21, no. 4 (December 1, 2018): 149–52. http://dx.doi.org/10.2478/ata-2018-0027.
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Abstract This paper deals with the prospects of using a drone for spraying the gardens and vineyards. Relevance of this process is substantiated with the help of statistical data on the industry in Ukraine. To increase the efficiency of drones during the plant treatment, the concept of a semi-autonomous drone is proposed with connection to a communication line with a tractor – a “tractor-drone” complex. A spraying solution and commands for the drone are transmitted via the communication line. Basic physical formulas for appropriate selection of technical means for the lifting of sprayer frame are presented. Environmental parameters for the flight control system were estimated: temperature fluctuation at 20 K requires screw speed increase by 1.5%; an increase in atmospheric pressure by 5% allows reduction of screw speed by 2%. Tasks of the control system for the concept of semi-autonomous drones are defined in the paper.
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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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Ngoua Ndong Avele, J. B., and V. S. Goryainov. "UAV Docking Station: Study on Building an Autonomous Takeoff and Landing Platform for Unmanned Aerial Vehicles." LETI Transactions on Electrical Engineering & Computer Science 16, no. 9 (2023): 38–48. http://dx.doi.org/10.32603/2071-8985-2023-16-9-38-48.
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Studies how to increase the efficiency of autonomous operation of drones using an intelligent docking station for unmanned aerial vehicles (UAVs), which improves charging and maintenance, reducing the need for human intervention in these processes. Known examples of designs for an automatic drone recharging system have been considered. The results present a system developed for automatic landing of drones on the platform and concepts for systems for automatically positioning the drone after landing and for wireless charging or replacing the drone battery.
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Lee, Alvin, Suet-Peng Yong, Witold Pedrycz, and Junzo Watada. "Testing a Vision-Based Autonomous Drone Navigation Model in a Forest Environment." Algorithms 17, no. 4 (March 27, 2024): 139. http://dx.doi.org/10.3390/a17040139.
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Drones play a pivotal role in various industries of Industry 4.0. For achieving the application of drones in a dynamic environment, finding a clear path for their autonomous flight requires more research. This paper addresses the problem of finding a navigation path for an autonomous drone based on visual scene information. A deep learning-based object detection approach can localize obstacles detected in a scene. Considering this approach, we propose a solution framework that includes masking with a color-based segmentation method to identify an empty area where the drone can fly. The scene is described using segmented regions and localization points. The proposed approach can be used to remotely guide drones in dynamic environments that have poor coverage from global positioning systems. The simulation results show that the proposed framework with object detection and the proposed masking technique support drone navigation in a dynamic environment based only on the visual input from the front field of view.
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Chronis, Christos, Georgios Anagnostopoulos, Elena Politi, Antonios Garyfallou, Iraklis Varlamis, and George Dimitrakopoulos. "Path planning of autonomous UAVs using reinforcement learning." Journal of Physics: Conference Series 2526, no. 1 (June 1, 2023): 012088. http://dx.doi.org/10.1088/1742-6596/2526/1/012088.
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Abstract Autonomous BVLOS Unmanned Aerial Vehicles (UAVs) are gradually gaining their share in the drone market. Together with the demand for extended levels of autonomy comes the necessity for high-performance obstacle avoidance and navigation algorithms that will allow autonomous drones to operate with minimum or no human intervention. Traditional AI algorithms have been extensively used in the literature for finding the shortest path in 2-D or 3-D environments and navigating the drones successfully through a known and stable environment. However, the situation can become much more complicated when the environment is changing or not known in advance. In this work, we explore the use of advanced artificial intelligence techniques, such as reinforcement learning, to successfully navigate a drone within unspecified environments. We compare our approach against traditional AI algoriths in a set of validation experiments on a simulation environment, and the results show that using only a couple of low-cost distance sensors it is possible to successfully navigate the drone beyond the obstacles.
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Langåker, Helge-André, Håkon Kjerkreit, Christoffer L. Syversen, Richard JD Moore, Øystein H. Holhjem, Irene Jensen, Aiden Morrison, et al. "An autonomous drone-based system for inspection of electrical substations." International Journal of Advanced Robotic Systems 18, no. 2 (March 1, 2021): 172988142110029. http://dx.doi.org/10.1177/17298814211002973.
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In the years to come, large power grid operators will operate and maintain an ever-increasing asset base. New innovative solutions are needed to increase the quality and efficiency of asset management to avoid corresponding growth in resources and cost. To this end, autonomous unmanned aerial vehicles (UAVs) provide a range of possibilities. Here, we present a novel prototype solution for autonomous and remotely operated inspection missions with resident drones on electrical substations, comprising: (1) an autonomous drone with sense and avoid and robustness to harsh weather capability; (2) a drone hangar for remote operations; and (3) drone operations and data acquisition management software. Further, we discuss the possibilities and challenges that such a system offers and give an overview of requirements that are key to realizing the potential of drones for improved asset management. These requirements are based on years of operational experience with electrical substations combined with the lessons learned during the development and testing of our drone system. We also experimentally investigate safety distances between the drone and high-voltage infrastructure. We demonstrate the usefulness of our autonomous inspection solution through extensive field testing at one of Statnett’s fully operational electrical substations.
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Edelman, Harry, Joel Stenroos, Jorge Peña Queralta, David Hästbacka, Jani Oksanen, Tomi Westerlund, and Juha Röning. "Analysis of airport design for introducing infrastructure for autonomous drones." Facilities 41, no. 15/16 (July 21, 2023): 85–100. http://dx.doi.org/10.1108/f-11-2022-0146.
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Purpose Connecting autonomous drones to ground operations and services is a prerequisite for the adoption of scalable and sustainable drone services in the built environment. Despite the rapid advance in the field of autonomous drones, the development of ground infrastructure has received less attention. Contemporary airport design offers potential solutions for the infrastructure serving autonomous drone services. To that end, this paper aims to construct a framework for connecting air and ground operations for autonomous drone services. Furthermore, the paper defines the minimum facilities needed to support unmanned aerial vehicles for autonomous logistics and the collection of aerial data. Design/methodology/approach The paper reviews the state-of-the-art in airport design literature as the basis for analysing the guidelines of manned aviation applicable to the development of ground infrastructure for autonomous drone services. Socio-technical system analysis was used for identifying the service needs of drones. Findings The key findings are functional modularity based on the principles of airport design applies to micro-airports and modular service functions can be connected efficiently with an autonomous ground handling system in a sustainable manner addressing the concerns on maintenance, reliability and lifecycle. Research limitations/implications As the study was limited to the airport design literature findings, the evolution of solutions may provide features supporting deviating approaches. The role of autonomy and cloud-based service processes are quintessentially different from the conventional airport design and are likely to impact real-life solutions as the area of future research. Practical implications The findings of this study provided a framework for establishing the connection between the airside and the landside for the operations of autonomous aerial services. The lack of such framework and ground infrastructure has hindered the large-scale adoption and easy-to-use solutions for sustainable logistics and aerial data collection for decision-making in the built environment. Social implications The evolution of future autonomous aerial services should be accessible to all users, “democratising” the use of drones. The data collected by drones should comply with the privacy-preserving use of the data. The proposed ground infrastructure can contribute to offloading, storing and handling aerial data to support drone services’ acceptability. Originality/value To the best of the authors’ knowledge, the paper describes the first design framework for creating a design concept for a modular and autonomous micro-airport system for unmanned aviation based on the applied functions of full-size conventional airports.
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Rathod, Pornima D., and Gopal U. Shinde. "Autonomous Aerial System (UAV) for Sustainable Agriculture: A Review." International Journal of Environment and Climate Change 13, no. 8 (June 10, 2023): 1343–55. http://dx.doi.org/10.9734/ijecc/2023/v13i82080.
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Drones are now a days emerging as a component of precision agriculture along with contributing to sustainable agriculture. The use of advanced technologies such as drone in agriculture offer potential for facing several major or minor challenges. The major applications of drone in agriculture are spraying, irrigation, crop monitoring, soil and field analysis and bird control. The objective of this paper is to review the latest trends and applications of leading technologies related to agricultural UAVs equipment, and sensors development. And also, the use of UAVs in real agricultural environments. Based on the literature, found that a lots of agriculture applications can be done by using Drone. In the methodology, we used a comprehensive review from other researches in this world. Furthermore, the future development of agricultural UAVs and their challenges are considered. In this review paper, summarizes the available agricultural drones and applications of UAVs for Precision Agriculture using different sensors to evaluated agricultural parameters such as NDVI, vegetation index, NIR, nutrient disorder using sensors like RGB, digital camera, multispectral and hyperspectral sensors and to reduce the wasting of water and chemicals quadcopter, hexacopter UAVs could be used.
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V.J, Rehna, and Mohammad Nizamuddin Inamdar. "Impact of Autonomous Drone Pollination in Date Palms." International Journal of Innovative Research and Scientific Studies 5, no. 4 (October 12, 2022): 297–305. http://dx.doi.org/10.53894/ijirss.v5i4.732.
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Artificial pollination of date palms has been practiced over thousands of years to improve the fruiting traits in date palms. Due to the changes in agricultural practices in the modern period, mechanical pollination techniques were tried in some parts of the world. But machine pollination of date palms has not gained popularity worldwide owing to economic, environmental, or technical challenges. Of late, agricultural drones were introduced to pollinate date palms in significantly less time and reduce the risk of injury, manpower, and cost. Modern drones can have integrated, built-in smart data-collecting devices to provide the farmers with all relevant information. Although this autonomous method provides a number of benefits in terms of labor and cost, pollination time, ease of use, etc, studies have not yet entirely evaluated the efficacy of drone pollination on date palms. This paper summarizes the outcomes of an autonomous drone pollination study performed during the 2022 season in the orchards of Oman. The pros and cons of this artificial aerial pollination method are examined in the paper. The impact of this method of pollination on crop yield, fruit quality, and fruit set percentage are analyzed. This study also explores the limitations of the autonomous drone pollination system and throws light on ways to improve its efficiency.
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Amicone, Donatello, Andrea Cannas, Alberto Marci, and Giuseppe Tortora. "A Smart Capsule Equipped with Artificial Intelligence for Autonomous Delivery of Medical Material through Drones." Applied Sciences 11, no. 17 (August 28, 2021): 7976. http://dx.doi.org/10.3390/app11177976.
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In the last few years, many examples of blood and medicine delivery drones have been demonstrated worldwide, which mainly rely on aeronautical experience that is not common in the medical world. Speaking about drone delivery, attention should focus on the most important thing: the transported lifesaving good. Traditional boxes that monitor temperature are not usually in real time, and are not suitable for drone transportation because they are heavy and bulky. This means that the biomedical characteristics of delivery are of primary importance. A Smart Capsule, equipped with artificial intelligence (AI), is the first system ever proposed to provide a fully autonomous drone delivery service for perishable and high-value medical products, integrating real-time quality monitoring and control. It consists in a smart casing that is able to guide any autonomous aerial vehicle attached to it, specifically designed for transporting blood, organs, tissues, test samples and drugs, among others. The system monitors the conditions of the product (e.g., temperature, agitation and humidity) and adjusts them when needed by exploiting, for instance, vibrations to maintain the required agitation, ensuring that goods are ready to be used as soon as they are delivered. The Smart Capsule also leverages external temperature to reduce energy uptake from the drone, thus improving the drone’s battery life and flight range. The system replaces the need for specialized drivers and traditional road-bound transportation means, while guaranteeing compliance with all applicable safety regulations. A series of 16 experimental tests was performed to demonstrate the possibility of using the smart capsule to manage the flight and internal good delivery. Eighty-one missions were carried out for a total of 364 min of flight. The Smart Capsule greatly improves emergency response and efficiency of healthcare systems by reducing delivery times by up to 80% and costs by at least 28%. The Smart Capsule and its enabling technology based on AI for drone deliveries are discussed in this paper. The aim of this work is to show the possibility of managing drone delivery with an AI-based device.
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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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Bezas, Konstantinos, Georgios Tsoumanis, Constantinos T. Angelis, and Konstantinos Oikonomou. "Coverage Path Planning and Point-of-Interest Detection Using Autonomous Drone Swarms." Sensors 22, no. 19 (October 5, 2022): 7551. http://dx.doi.org/10.3390/s22197551.
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Unmanned Aerial Vehicles (UAVs) or drones presently are enhanced with miniature sensors that can provide information relative to their environment. As such, they can detect changes in temperature, orientation, altitude, geographical location, electromagnetic fluctuations, lighting conditions, and more. Combining this information properly can help produce advanced environmental awareness; thus, the drone can navigate its environment autonomously. Wireless communications can also aid in the creation of drone swarms that, combined with the proper algorithm, can be coordinated towards area coverage for various missions, such as search and rescue. Coverage Path Planning (CPP) is the field that studies how drones, independently or in swarms, can cover an area of interest efficiently. In the current work, a CPP algorithm is proposed for a swarm of drones to detect points of interest and collect information from them. The algorithm’s effectiveness is evaluated under simulation results. A set of characteristics is defined to describe the coverage radius of each drone, the speed of the swarm, and the coverage path followed by it. The results show that, for larger swarm sizes, the missions require less time while more points of interest can be detected within the area. Two coverage paths are examined here—parallel lines and spiral coverage. The results depict that the parallel lines coverage is more time-efficient since the spiral increases the required time by an average of 5% in all cases for the same number of detected points of interest.
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Sanchez-Rodriguez, Jose-Pablo, and Alejandro Aceves-Lopez. "A survey on stereo vision-based autonomous navigation for multi-rotor MUAVs." Robotica 36, no. 8 (May 6, 2018): 1225–43. http://dx.doi.org/10.1017/s0263574718000358.
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SUMMARYThis paper presents an overview of the most recent vision-based multi-rotor micro unmanned aerial vehicles (MUAVs) intended for autonomous navigation using a stereoscopic camera. Drone operation is difficult because pilots need the expertise to fly the drones. Pilots have a limited field of view, and unfortunate situations, such as loss of line of sight or collision with objects such as wires and branches, can happen. Autonomous navigation is an even more difficult challenge than remote control navigation because the drones must make decisions on their own in real time and simultaneously build maps of their surroundings if none is available. Moreover, MUAVs are limited in terms of useful payload capability and energy consumption. Therefore, a drone must be equipped with small sensors, and it must carry low weight. In addition, a drone requires a sufficiently powerful onboard computer so that it can understand its surroundings and navigate accordingly to achieve its goal safely. A stereoscopic camera is considered a suitable sensor because of its three-dimensional (3D) capabilities. Hence, a drone can perform vision-based navigation through object recognition and self-localise inside a map if one is available; otherwise, its autonomous navigation creates a simultaneous localisation and mapping problem.
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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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Alsawy, Assem, Dan Moss, Alan Hicks, and Susan McKeever. "An Image Processing Approach for Real-Time Safety Assessment of Autonomous Drone Delivery." Drones 8, no. 1 (January 15, 2024): 21. http://dx.doi.org/10.3390/drones8010021.
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The aim of producing self-driving drones has driven many researchers to automate various drone driving functions, such as take-off, navigation, and landing. However, despite the emergence of delivery as one of the most important uses of autonomous drones, there is still no automatic way to verify the safety of the delivery stage. One of the primary steps in the delivery operation is to ensure that the dropping zone is a safe area on arrival and during the dropping process. This paper proposes an image-processing-based classification approach for the delivery drone dropping process at a predefined destination. It employs live streaming via a single onboard camera and Global Positioning System (GPS) information. A two-stage processing procedure is proposed based on image segmentation and classification. Relevant parameters such as camera parameters, light parameters, dropping zone dimensions, and drone height from the ground are taken into account in the classification. The experimental results indicate that the proposed approach provides a fast method with reliable accuracy based on low-order calculations.
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Nguyen, Dinh Dung, Jozsef Rohacs, and Daniel Rohacs. "Autonomous Flight Trajectory Control System for Drones in Smart City Traffic Management." ISPRS International Journal of Geo-Information 10, no. 5 (May 17, 2021): 338. http://dx.doi.org/10.3390/ijgi10050338.
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With the exponential growth of numerous drone operations ranging from infrastructure monitoring to even package delivery services, the integration of UAS in the smart city transportation systems is an actual task that requires radically new, sustainable (safe, secure, with minimum environmental impact and life cycle cost) solutions. The primary objective of this proposed option is the definition of routes as desired and commanded trajectories and their autonomous execution. The airspace structure and fixed routes are given in the global GPS reference system with supporting GIS mapping. The concept application requires a series of further studies and solutions as drone trajectory (or corridor) following by an autonomous trajectory tracking control system, coupled with autonomous conflict detection, resolution, safe drone following, and formation flight options. The second part of the paper introduces such possible models and shows some results of their verification tests. Drones will be connected with the agency, designed trajectories to support them with factual information on trajectories and corridors. While the agency will use trajectory elements to design fixed or desired trajectories, drones may use the conventional GPS, infrared, acoustic, and visual sensors for positioning and advanced navigation. The accuracy can be improved by unique markers integrated into the infrastructure.
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Bhardwaj, Swarnika. "Real-time operating system for autonomous drone control." Innovative Research Thoughts 9, no. 4 (2023): 134–43. http://dx.doi.org/10.36676/irt.2023-v9i4-019.
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Drones have become a transformative force across industries in the quickly changing world of autonomous technologies. Their applications are expanding, from precision agriculture to emergency response, thanks to developments in real-time operating systems. A key development is a "Real-time operating system for autonomous drone control" that reshapes the potential of unmanned aerial vehicles through the convergence of high-speed data processing, low-latency responsiveness, and complex control algorithms.
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Yildiz, Melih, Burcu Bilgiç, Utku Kale, and Dániel Rohács. "Experimental Investigation of Communication Performance of Drones Used for Autonomous Car Track Tests." Sustainability 13, no. 10 (May 17, 2021): 5602. http://dx.doi.org/10.3390/su13105602.
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Autonomous Vehicles (AVs) represent an emerging and disruptive technology that provides a great opportunity for future transport not only to have a positive social and environmental impact but also traffic safety. AV use in daily life has been extensively studied in the literature in various dimensions, however; it is time for AVs to go further which is another technological aspect of communication. Vehicle-to-Vehicle (V2V) technology is an emerging issue that is expected to be a mutual part of AVs and transportation safety in the near future. V2V is widely discussed by its deployment possibilities not only by means of communication, even to be used as an energy transfer medium. ZalaZONE Proving Ground is a 265-hectare high-tech test track for conventional, electric as well as connected, assisted, and automated vehicles. This paper investigates the use of drones for tracking the cars on the test track. The drones are planned to work as an uplink for the data collected by the onboard sensors of the car. The car is expected to communicate with the drone which is flying in coordination. For the communication 868 MHz is selected to be used between the car and the drone. The test is performed to simulate different flight altitudes of drones. The signal strength of the communication is analyzed, and a model is developed which can be used for the future planning of the test track applications.
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Sirmacek, B., R. Rashad, and P. Radl. "AUTONOMOUS UAV-BASED 3D-RECONSTRUCTION OF STRUCTURES FOR AERIAL PHYSICAL INTERACTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 601–5. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-601-2019.
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<p><strong>Abstract.</strong> We introduce a fully automated only path planning approach especially for drones. This novel method relies on usage of a stereo camera mounted at the bottom of a hexagonal drone for real-time point cloud reconstruction and localization. The real-time point cloud is analyzed in a software loop where the entropy of the point cloud and the surface normals are calculated. The low entropy positions (which indicate the 3D areas with less point density and less information) and the surface normals are used for calculating the next inspection point which can be targeted by the drone in order to enhance the point cloud best. Path planning to these automatically selected target points is done during the flight (quite real-time) and automatically. The initial experiments are performed on Gazebo simulation environment within the ROS system using realistic parameters of our real drone and real stereo camera.</p>
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Zhang, Chi, Zhong Yang, Haoze Zhuo, Luwei Liao, Xin Yang, Tang Zhu, and Guotao Li. "A Lightweight and Drift-Free Fusion Strategy for Drone Autonomous and Safe Navigation." Drones 7, no. 1 (January 2, 2023): 34. http://dx.doi.org/10.3390/drones7010034.
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Self-localization and state estimation are crucial capabilities for agile drone autonomous navigation. This article presents a lightweight and drift-free vision-IMU-GNSS tightly coupled multisensor fusion (LDMF) strategy for drones’ autonomous and safe navigation. The drone is carried out with a front-facing camera to create visual geometric constraints and generate a 3D environmental map. Ulteriorly, a GNSS receiver with multiple constellations support is used to continuously provide pseudo-range, Doppler frequency shift and UTC time pulse signals to the drone navigation system. The proposed multisensor fusion strategy leverages the Kanade–Lucas algorithm to track multiple visual features in each input image. The local graph solution is bounded in a restricted sliding window, which can immensely predigest the computational complexity in factor graph optimization procedures. The drone navigation system can achieve camera-rate performance on a small companion computer. We thoroughly experimented with the LDMF system in both simulated and real-world environments, and the results demonstrate dramatic advantages over the state-of-the-art sensor fusion strategies.
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Pfeiffer, Christian, Simon Wengeler, Antonio Loquercio, and Davide Scaramuzza. "Visual attention prediction improves performance of autonomous drone racing agents." PLOS ONE 17, no. 3 (March 1, 2022): e0264471. http://dx.doi.org/10.1371/journal.pone.0264471.
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Humans race drones faster than neural networks trained for end-to-end autonomous flight. This may be related to the ability of human pilots to select task-relevant visual information effectively. This work investigates whether neural networks capable of imitating human eye gaze behavior and attention can improve neural networks’ performance for the challenging task of vision-based autonomous drone racing. We hypothesize that gaze-based attention prediction can be an efficient mechanism for visual information selection and decision making in a simulator-based drone racing task. We test this hypothesis using eye gaze and flight trajectory data from 18 human drone pilots to train a visual attention prediction model. We then use this visual attention prediction model to train an end-to-end controller for vision-based autonomous drone racing using imitation learning. We compare the drone racing performance of the attention-prediction controller to those using raw image inputs and image-based abstractions (i.e., feature tracks). Comparing success rates for completing a challenging race track by autonomous flight, our results show that the attention-prediction based controller (88% success rate) outperforms the RGB-image (61% success rate) and feature-tracks (55% success rate) controller baselines. Furthermore, visual attention-prediction and feature-track based models showed better generalization performance than image-based models when evaluated on hold-out reference trajectories. Our results demonstrate that human visual attention prediction improves the performance of autonomous vision-based drone racing agents and provides an essential step towards vision-based, fast, and agile autonomous flight that eventually can reach and even exceed human performances.
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Jacobsen, Rune Hylsberg, Lea Matlekovic, Liping Shi, Nicolaj Malle, Naeem Ayoub, Kaspar Hageman, Simon Hansen, Frederik Falk Nyboe, and Emad Ebeid. "Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure." Applied Sciences 13, no. 3 (January 17, 2023): 1256. http://dx.doi.org/10.3390/app13031256.
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Inspection of critical infrastructure with drones is experiencing an increasing uptake in the industry driven by a demand for reduced cost, time, and risk for inspectors. Early deployments of drone inspection services involve manual drone operations with a pilot and do not obtain the technological benefits concerning autonomy, coordination, and cooperation. In this paper, we study the design needed to handle the complexity of an Unmanned Aerial System (UAS) to support autonomous inspection of safety-critical infrastructure. We apply a constructive research approach to link innovation needs with concepts, designs, and validations that include simulation and demonstration of key design parts. Our design approach addresses the complexity of the UAS and provides a selection of technology components for drone and ground control hardware and software including algorithms for autonomous operation and interaction with cloud services. The paper presents a drone perception system with accelerated onboard computing, communication technologies of the UAS, as well as algorithms for swarm membership, formation flying, object detection, and fault detection with artificial intelligence. We find that the design of a cooperative drone swarm and its integration into a custom-built UAS for infrastructure inspection is highly feasible given the current state of the art in electronic components, software, and communication technology.
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Nurgaliev, Ian, Youssef Eskander, and Karolina Lis. "The Use of Drones and Autonomous Vehicles in Logistics and Delivery." Logistics and Transport 57, no. 1 (2023): 62. http://dx.doi.org/10.26411/83-1734-2015-2-55-6-23.
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The logistics and delivery industry is facing challenges such as high transportation costs, difficulty in meeting customer demands, and environmental concerns. However, the integration of drone and autonomous vehicle technology can address these challenges by reducing transportation costs, increasing speed and reliability of delivery, and improving efficiency. The use of drones and autonomous vehicles can bring significant benefits such as increased efficiency, cost savings, improved safety, increased accessibility, and real-time tracking. Despite the potential benefits, there are still regulatory, technical, and financial barriers to overcome before a widespread adoption of these technologies. The use of drones and autonomous vehicles in the logistics and delivery industry is rapidly growing, with companies like Amazon, UPS, DHL actively experimenting with the use of these technologies. However, there are several challenges and limitations that must be overcome before they can be widely adopted, such as safety and regulatory requirements, weather and environmental conditions, battery life and range, navigation, and public perception.
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Shimada, Tomoyasu, Hiroki Nishikawa, Xiangbo Kong, and Hiroyuki Tomiyama. "Fast and High-Quality Monocular Depth Estimation with Optical Flow for Autonomous Drones." Drones 7, no. 2 (February 14, 2023): 134. http://dx.doi.org/10.3390/drones7020134.
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Recent years, autonomous drones have attracted attention in many fields due to their convenience. Autonomous drones require precise depth information so as to avoid collision to fly fast and both of RGB image and LiDAR point cloud are often employed in applications based on Convolutional Neural Networks (CNNs) to estimate the distance to obstacles. Such applications are implemented onboard embedded systems. In order to precisely estimate the depth, such CNN models are in general so complex to extract many features that the computational complexity increases, requiring long inference time. In order to solve the issue, we employ optical flow to aid in-depth estimation. In addition, we propose a new attention structure that makes maximum use of optical flow without complicating the network. Furthermore, we achieve improved performance without modifying the depth estimator by adding a perceptual discriminator in training. The proposed model is evaluated through accuracy, error, and inference time on the KITTI dataset. In the experiments, we have demonstrated the proposed method achieves better performance by up to 34% accuracy, 55% error reduction and 66% faster inference time on Jetson nano compared to previous methods. The proposed method is also evaluated through a collision avoidance in simulated drone flight and achieves the lowest collision rate of all estimation methods. These experimental results show the potential of proposed method to be used in real-world autonomous drone flight applications.
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Patel, Shreepali. "Drone Filming: Creativity versus Regulations in Autonomous Art Systems. A Case Study." Media-N 15, no. 1 (January 27, 2019): 17–23. http://dx.doi.org/10.21900/j.median.v15i1.50.
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This article explores the impact of drone regulations on the narrative potential of drone filming. The central focus of this exploration is a Case Study analysis of the production of a multi-screen audio-visual digital installation, The Crossing (Patel, 2016). The Crossing [1], filmed in central London, utilized the use of a heavy weight Unmanned Aerial System (UAS) also known as a drone with a 5-kilo weight load capacity with the Alexa Mini WCU-4. Combined with the CForce Mini lens control system, the UAS gave unparalleled camera and lens control at extended ranges, providing complete pan, tilt and lens control and allowing dynamic moves in the air. The result was the ability to navigate through spaces to give intimate and playful shots that give the viewer ‘alternate’ versions of reality that only a ‘machine’ can provide. Artists, performers and filmmakers are finding new kinds of beauty through automated programming where the drones are not just capturing the story but the machines themselves become the story. However, the operational scope of drones is limited by legal and health and safety regulations, particularly within built up urban environments. These regulations govern the vertical and horizontal distance from objects and people, line of sight, time constraints, weather conditions as well as security implications. Further restrictions include requiring a trained and fully licensed crew with permission from the relevant aviation bodies. This article seeks to answer whether these restrictions limit the creativity of the artist or challenge the creator to consider alternate ways of using these Autonomous Art Systems to inform the aesthetic scope of the captured image. This article will draw on a combination of original filming and broadcast examples to examine how legal and security restrictions on UAS inform the narrative and aesthetic realization of the final art form and subsequent emotional and physical response of the spectator.
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Hulens, Dries, Wiebe Van Ranst, Ying Cao, and Toon Goedemé. "Autonomous Visual Navigation for a Flower Pollination Drone." Machines 10, no. 5 (May 10, 2022): 364. http://dx.doi.org/10.3390/machines10050364.
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In this paper, we present the development of a visual navigation capability for a small drone enabling it to autonomously approach flowers. This is a very important step towards the development of a fully autonomous flower pollinating nanodrone. The drone we developed is totally autonomous and relies for its navigation on a small on-board color camera, complemented with one simple ToF distance sensor, to detect and approach the flower. The proposed solution uses a DJI Tello drone carrying a Maix Bit processing board capable of running all deep-learning-based image processing and navigation algorithms on-board. We developed a two-stage visual servoing algorithm that first uses a highly optimized object detection CNN to localize the flowers and fly towards it. The second phase, approaching the flower, is implemented by a direct visual steering CNN. This enables the drone to detect any flower in the neighborhood, steer the drone towards the flower and make the drone’s pollinating rod touch the flower. We trained all deep learning models based on an artificial dataset with a mix of images of real flowers, artificial (synthetic) flowers and virtually rendered flowers. Our experiments demonstrate that the approach is technically feasible. The drone is able to detect, approach and touch the flowers totally autonomously. Our 10 cm sized prototype is trained on sunflowers, but the methodology presented in this paper can be retrained for any flower type.
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Miera, Paweł, Hubert Szolc, and Tomasz Kryjak. "Control of an Autonomous Unmanned Aerial Vehicle Using Reinforcement Learning." Pomiary Automatyka Robotyka 27, no. 4 (December 20, 2023): 85–91. http://dx.doi.org/10.14313/par_250/85.
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Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatable avoid trees and fly through the forest.
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An, Weigang, Tianyu Lin, and Peng Zhang. "An Autonomous Soaring for Small Drones Using the Extended Kalman Filter Thermal Updraft Center Prediction Method Based on Ordinary Least Squares." Drones 7, no. 10 (September 26, 2023): 603. http://dx.doi.org/10.3390/drones7100603.
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Many birds in the natural world are capable of engaging in sustained soaring within thermal updrafts for extended periods without flapping their wings. Autonomous soaring has the potential to greatly improve both the range and endurance of small drones. In this paper, the extended Kalman filter (EKF) thermal updraft center prediction method based on ordinary least squares (OLS) is proposed to develop the autonomous soaring system for small drones, and an adaptive step size update strategy is incorporated into the EKF. The proposed method is compared with EKF thermal updraft prediction methods through simulated experiments. The results indicate that the proposed prediction method has low computational complexity and fast convergence speed and performs more stably in weak thermal updrafts. The above advantages stem from the OLS providing an approximate distribution of the thermal updraft around the drone for the EKF. This empowers the EKF algorithm with ample information to dynamically update the thermal updraft center in real time. The adaptive step size update strategy further accelerates the convergence speed of this process. In addition, flight experiments were conducted on the Talon fixed-wing drone platform to test the autonomous soaring system. During the flight experiment, the drone successfully engaged in static soaring within thermal updrafts, effectively hovering and gaining energy. Throughout the approximately 40 min flight duration, the drone only utilized its propulsion for about 8 min. This demonstrated the effectiveness of the autonomous soaring system using the EKF thermal updraft center prediction method based on OLS. Finally, by analyzing and discussing the differences between the simulation experiment results and the flight experiment results, some improvement strategies for the current work are proposed.
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Tao, Hong, Tao Song, Defu Lin, Ren Jin, and Bin Li. "Autonomous Navigation and Control System for Capturing A Moving Drone." Field Robotics 2, no. 1 (March 10, 2022): 34–54. http://dx.doi.org/10.55417/fr.2022002.
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This paper describes an autonomous navigation and control system for capturing the maneuvering drones. A vision-based navigation method seeks and detects the intruding drone, then, the target trajectory is predicted by fusing onboard vision and inertial-measurement resources. The target’s relative position, velocity and acceleration are also obtained at the same time. Then, we present a modified proportional-derivative (PD) algorithm based on the estimated target states. In addition, the boundary constraints of the protected area are considered to avoid a collision. The proposed capture navigation and control system has demonstrated its efficiency both in simulation, flight experiments, and MBZIRC 2020, where our team won the Challenge I competition.
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Istiqomah Arrahmah, Annisa, Rissa Rahmania, and Dany Eka Saputra. "Comparison between convolutional neural network and K-nearest neighbours object detection for autonomous drone." Bulletin of Electrical Engineering and Informatics 11, no. 4 (August 1, 2022): 2303–12. http://dx.doi.org/10.11591/eei.v11i4.3784.
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In autonomous drones, the drone’s ability to move depends on the drone’s capacity to know its position, either in relative or absolute position. The Pinhole model is one of the methods to calculate a drone’s relative position based on the triangle similarity concept using a single camera. This method utilizes bounding box information generated from an object detection algorithm. Thus, accuracy of the generated bounding box is crucial, and selection of object detection algorithm is necessary. This paper compares and evaluates machine learning and deep learning object detection methods to determine which method is suitable for distance measurement using a single camera for autonomous drone’s controller based on pinhole model. A novel K-nearest neighbours-based (KNN-based) object detection is constructed to represent the machine learning method while you only look once version 5 (YOLOv5) convolutional neural network (CNN) architecture is selected to represent the deep learning method. A dataset consists of two different classes, with a total of 1520 images, collected from the unmanned aerial vehicle (UAV) camera for training and evaluation purposes. Confusion matrix and intersection over union (IoU)/generalized intersection of union (GIoU) matrix are used to evaluate the performance of both methods. The result of this paper shows the performance of each system and concludes the suitable type of object detection algorithm for the autonomous UAV purpose.
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Pascarella, D., V. U. Castrillo, I. Iudice, G. Pigliasco, and A. Vozella. "Game-theoretic learning for the coordination of drone teams in autonomous cooperative inspection." Journal of Physics: Conference Series 2716, no. 1 (March 1, 2024): 012058. http://dx.doi.org/10.1088/1742-6596/2716/1/012058.
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Abstract Without the need for an on-board pilot, drones are designed to accomplish dull, dangerous and dirty missions. However, if a mission exhibits a large operative area and/or several objectives, it may entail poor performance when executed by a single drone. Drone teams may overcome this issue by acting as mobile sensor networks for proximal sensing. In such networks, cooperative autonomy is a key enabling behaviour for achieving resilient and cost-efficient systems. This work implements cooperative autonomous behaviour in the form of a dynamic and decentralized mission planner for a multi-drone inspection mission. The proposed design exploits multi-agent task allocation, distributed route planning and game theory for the assignment of inspection tasks and for the processing of optimal routes in reasonable time frames and with limited communication. In detail, it applies the learning-in-games framework for the coordination within the inspection team, by studying some ad-hoc variants of best response and of log linear learning. Moreover, this work presents some numerical results of model-in-the-loop tests for a comparison between the learning-in-games approaches.
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Saffre, Fabrice, Hanno Hildmann, Hannu Karvonen, and Timo Lind. "Monitoring and Cordoning Wildfires with an Autonomous Swarm of Unmanned Aerial Vehicles." Drones 6, no. 10 (October 14, 2022): 301. http://dx.doi.org/10.3390/drones6100301.
Full textAbstract:
Unmanned aerial vehicles, or drones, are already an integral part of the equipment used by firefighters to monitor wildfires. They are, however, still typically used only as remotely operated, mobile sensing platforms under direct real-time control of a human pilot. Meanwhile, a substantial body of literature exists that emphasises the potential of autonomous drone swarms in various situational awareness missions, including in the context of environmental protection. In this paper, we present the results of a systematic investigation by means of numerical methods i.e., Monte Carlo simulation. We report our insights into the influence of key parameters such as fire propagation dynamics, surface area under observation and swarm size over the performance of an autonomous drone force operating without human supervision. We limit the use of drones to perform passive sensing operations with the goal to provide real-time situational awareness to the fire fighters on the ground. Therefore, the objective is defined as being able to locate, and then establish a continuous perimeter (cordon) around, a simulated fire event to provide live data feeds such as e.g., video or infra-red. Special emphasis was put on exclusively using simple, robust and realistically implementable distributed decision functions capable of supporting the self-organisation of the swarm in the pursuit of the collective goal. Our results confirm the presence of strong nonlinear effects in the interaction between the aforementioned parameters, which can be closely approximated using an empirical law. These findings could inform the mobilisation of adequate resources on a case-by-case basis, depending on known mission characteristics and acceptable odds (chances of success).
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Khelifi, Adel, Gabriele Ciccone, Mark Altaweel, Tasnim Basmaji, and Mohammed Ghazal. "Autonomous Service Drones for Multimodal Detection and Monitoring of Archaeological Sites." Applied Sciences 11, no. 21 (November 5, 2021): 10424. http://dx.doi.org/10.3390/app112110424.
Full textAbstract:
Constant detection and monitoring of archaeological sites and objects have always been an important national goal for many countries. The early identification of changes is crucial to preventive conservation. Archaeologists have always considered using service drones to automate collecting data on and below the ground surface of archaeological sites, with cost and technical barriers being the main hurdles against the wide-scale deployment. Advances in thermal imaging, depth imaging, drones, and artificial intelligence have driven the cost down and improved the quality and volume of data collected and processed. This paper proposes an end-to-end framework for archaeological sites detection and monitoring using autonomous service drones. We mount RGB, depth, and thermal cameras on an autonomous drone for low-altitude data acquisition. To align and aggregate collected images, we propose two-stage multimodal depth-to-RGB and thermal-to-RGB mosaicking algorithms. We then apply detection algorithms to the stitched images to identify change regions and design a user interface to monitor these regions over time. Our results show we can create overlays of aligned thermal and depth data on RGB mosaics of archaeological sites. We tested our change detection algorithm and found it has a root mean square error of 0.04. To validate the proposed framework, we tested our thermal image stitching pipeline against state-of-the-art commercial software. We cost-effectively replicated its functionality while adding a new depth-based modality and created a user interface for temporally monitoring changes in multimodal views of archaeological sites.
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Sarvi, Batoul. "Multimedia communications for autonomous drones." Boolean 2022 VI, no. 1 (December 6, 2022): 52–58. http://dx.doi.org/10.33178/boolean.2022.1.9.
Full textAbstract:
In recent years, there has been significant growth in multimedia communication on drones. The first thing that comes to every researcher’s mind is what requirements are for multimedia communication to be acceptable for existing scenarios on UAVs? Because of the noisy wireless channel and long distance between UAVs, providing reliable and real-time multimedia communications on UAVs stands at the top of the requirements list. To the best of our knowledge, mobile edge computing and cross-layer error control have significant possibilities to provide a better quality of multimedia communication on UAVs. Finally, utilizing the aforementioned edge network techniques can increase the efficiency of the overall system, enhance the video quality, maximize the usage of network resources, and save energy in multimedia communication on UAV networks.
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Rofi’i, Ahmad, Dafid Ari Prasetyo, and Maria Azizah. "Analisis Prediksi Sebaran Nilaparvata Lugens (Hama Wereng) Tanaman Padi menggunakan Teknologi Autonomous Drone Mapping dengan Ground Sampling Area." Jurnal Ilmiah Inovasi 21, no. 1 (April 30, 2021): 38–45. http://dx.doi.org/10.25047/jii.v21i1.2633.
Full textAbstract:
The purpose of this study was to help farmers predict the spread of rice planthopper pests by utilizing Autonomous Drone Mapping technology with Ground Sampling Area. The object under study was rice farming land with an area of 64.5 m2 which often experienced disturbances in productivity and quality of rice. Autonomous Drone Mapping Technology with Ground Sampling Area integration. This technology is used to detect the spread of leafhoppers on agricultural land through mapping of the affected land through a map of conditions resulting from shooting and imagery produced by drones flown over agricultural land. This technology can help rice farmers to predict pest attack from an early age by handling and preventive measures so that the level of productivity and quality of rice is not compromised. Prediction analysis can take advantage of remote imaging photos from the use of Autonomous Drone Mapping with Ground Sampling Area by analyzing the spread prediction data with Tren Forecasting Prediction. Based on the analysis of the predictions from the photo of the spread of planthopper pests, the distribution formula is y = 25.396 ln(x) -34.948 with the maximum spread of leafhoppers occurring on the 49th day so that serious handling is needed by farmers.