Gotowe bibliografie tematyczne / Fleet of UAVs

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Gotowa bibliografia na temat „Fleet of UAVs”

Autor: Grafiati

Data publikacji: 14 grudnia 2024

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Fleet of UAVs"

Фесенко, Герман Вікторович, та Вячеслав Сергійович Харченко. "МОДЕЛІ НАДІЙНОСТІ УГРУПОВАНЬ ФЛОТІВ БПЛА З КОВЗНИМ РЕЗЕРВУВАННЯМ ДЛЯ МОНІТОРИНГУ ПОТЕНЦІЙНО НЕБЕЗПЕЧНИХ ОБ’ЄКТІВ". RADIOELECTRONIC AND COMPUTER SYSTEMS, № 2 (21 червня 2019): 147–56. http://dx.doi.org/10.32620/reks.2019.2.14.

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Over the past years, unmanned aerial vehicles have been used to solve the problems of pre-and post-accident monitoring of nuclear power plants and other potentially dangerous objects. The use of a fleet (fleet grouping) of the unmanned aerial vehicle (UAV) for monitoring missions is due to the special conditions of the aggressive environment, which causes the failures of certain UAVs, and therefore needs to ensure a high level of reliability of such a fleet (the group of fleets). The most effective way to solve this problem is to use k-out-of-n redundancy. The subject of the study is a UAV fle

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Thibbotuwawa, Bocewicz, Zbigniew, and Nielsen. "A Solution Approach for UAV Fleet Mission Planning in Changing Weather Conditions." Applied Sciences 9, no. 19 (2019): 3972. http://dx.doi.org/10.3390/app9193972.

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With a rising demand for utilizing unmanned aerial vehicles (UAVs) to deliver materials in outdoor environments, particular attention must be given to all the different aspects influencing the deployment of UAVs for such purposes. These aspects include the characteristics of the UAV fleet (e.g. size of fleet, UAV specifications and capabilities), the energy consumption (highly affected by weather conditions and payload) and the characteristics of the network and customer locations. All these aspects must be taken into account when aiming to achieve deliveries to customers in a safe and timely

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Godio, Simone, Stefano Primatesta, Giorgio Guglieri, and Fabio Dovis. "A Bioinspired Neural Network-Based Approach for Cooperative Coverage Planning of UAVs." Information 12, no. 2 (2021): 51. http://dx.doi.org/10.3390/info12020051.

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This paper describes a bioinspired neural-network-based approach to solve a coverage planning problem for a fleet of unmanned aerial vehicles exploring critical areas. The main goal is to fully cover the map, maintaining a uniform distribution of the fleet on the map, and avoiding collisions between vehicles and other obstacles. This specific task is suitable for surveillance applications, where the uniform distribution of the fleet in the map permits them to reach any position on the map as fast as possible in emergency scenarios. To solve this problem, a bioinspired neural network structure

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Larson, Jonathan, Paul Isihara, Gabriel Flores, et al. "A priori assessment of a smart-navigated unmanned aerial vehicle disaster cargo fleet." SIMULATION 96, no. 8 (2020): 641–53. http://dx.doi.org/10.1177/0037549720921447.

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The United Nations Office for the Coordination of Humanitarian Affairs has asserted that risks in deployment of unmanned aerial vehicles (UAVs) within disaster response must be reduced by careful development of best-practice standards before implementing such systems. With recent humanitarian field tests of cargo UAVs as indication that implementation may soon become reality, a priori assessment of a smart-navigated (autonomous) UAV disaster cargo fleet via simulation modeling and analysis is vital to the best-practice development process. Logistical problems with ground transport of relief su

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Thibbotuwawa, Amila, Grzegorz Bocewicz, Grzegorz Radzki, Peter Nielsen, and Zbigniew Banaszak. "UAV Mission Planning Resistant to Weather Uncertainty." Sensors 20, no. 2 (2020): 515. http://dx.doi.org/10.3390/s20020515.

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Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to conduct mission planning addressing changing weather conditions, collision avoidance, and energy constraints specific to these types of UAVs. This paper presents a declarative approach for solving the complex mission planning resistant to weather uncertainty. The approach has been tested on several examples, analyzing how customer satisfaction is influen

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Gadiraju, Divija Swetha, Prasenjit Karmakar, Vijay K. Shah, and Vaneet Aggarwal. "GLIDE: Multi-Agent Deep Reinforcement Learning for Coordinated UAV Control in Dynamic Military Environments." Information 15, no. 8 (2024): 477. http://dx.doi.org/10.3390/info15080477.

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Unmanned aerial vehicles (UAVs) are widely used for missions in dynamic environments. Deep Reinforcement Learning (DRL) can find effective strategies for multiple agents that need to cooperate to complete the task. In this article, the challenge of controlling the movement of a fleet of UAVs is addressed by Multi-Agent Deep Reinforcement Learning (MARL). The collaborative movement of the UAV fleet can be controlled centrally and also in a decentralized fashion, which is studied in this work. We consider a dynamic military environment with a fleet of UAVs, whose task is to destroy enemy targets

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Kats, Vladimir, and Eugene Levner. "Maximizing the Average Environmental Benefit of a Fleet of Drones under a Periodic Schedule of Tasks." Algorithms 17, no. 7 (2024): 283. http://dx.doi.org/10.3390/a17070283.

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Unmanned aerial vehicles (UAVs, drones) are not just a technological achievement based on modern ideas of artificial intelligence; they also provide a sustainable solution for green technologies in logistics, transport, and material handling. In particular, using battery-powered UAVs to transport products can significantly decrease energy and fuel expenses, reduce environmental pollution, and improve the efficiency of clean technologies through improved energy-saving efficiency. We consider the problem of maximizing the average environmental benefit of a fleet of drones given a periodic schedu

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Bit-Monnot, Arthur, Rafael Bailon-Ruiz, and Simon Lacroix. "A Local Search Approach to Observation Planning with Multiple UAVs." Proceedings of the International Conference on Automated Planning and Scheduling 28 (June 15, 2018): 437–45. http://dx.doi.org/10.1609/icaps.v28i1.13924.

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Observation planning for Unmanned Aerial Vehicles (UAVs) is a challenging task as it requires planning trajectories over a large continuous space and with motion models that can not be directly encoded into current planners. Furthermore, realistic problems often require complex objective functions that complicate problem decomposition. In this paper, we propose a local search approach to plan the trajectories of a fleet of UAVs on an observation mission. The strength of the approach lies in its loose coupling with domain specific requirements such as the UAV model or the objective function tha

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José-Torra, Ferran, Antonio Pascual-Iserte, and Josep Vidal. "A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations." Sensors 18, no. 10 (2018): 3411. http://dx.doi.org/10.3390/s18103411.

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This paper proposes a positioning strategy for a fleet of unmanned aerial vehicles (UAVs) airlifting wireless base stations driven by communication constraints. First, two schedulers that model the distribution of resources among users within a single cell are analyzed. Then, an UAV autonomous positioning strategy is developed, based on a fair distribution of the radio resources among all the users of all the cells in a given scenario, in such a way that the user bitrate is the same regardless the users’ distribution and spatial density. Moreover, two realistic constraints are added related to

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He, Ping Chuan, and Shu Ling Dai. "Parallel Niche Genetic Algorithm for UAV Fleet Stealth Coverage 3D Corridors Real-Time Planning." Advanced Materials Research 846-847 (November 2013): 1189–96. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1189.

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This paper presents a parallel improved niche genetic algorithm (PINGA) for 3D stealth coverage corridors real-time planning of unmanned aerial vehicles (UAVs) operating in a threat rich environment. 3D corridor was suggested to meet the diversity kinematics constraints of UAVs. Niche genetic algorithm (NGA) was improved by merging neighborhood mutation operator and hill climbing algorithm, and performed in parallel. Additionally, the crowding strategy based on high value targets was used to generate coverage trajectories in the area of interest (AOI). Preliminary results in virtual environmen

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Rozprawy doktorskie na temat "Fleet of UAVs"

Zagar, Maxime. "Set-membership estimation and distributed control for a fleet of UAVs for target search and tracking." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST164.

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Cette thèse aborde le problème de la recherche et du suivi d'un nombre inconnu de cibles mobiles réparties dans une région d'intérêt (RdI) inconnue à l'aide d'une flotte coopérative de robots aériens sans pilote (UAVs). Ce problème de recherche, d'acquisition et de suivi coopératif (CSAT) apparaît dans des contextes militaires, e.g., pour trouver et suivre des véhicules ennemis, ou dans des applications civiles, e.g., pour rechercher des personnes perdues à la suite d'une catastrophe. Pour résoudre ce problème, chaque drone embarque un système de vision par ordinateur (CVS) composé d'une camér

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Bardin, Jeremy, Dan Covelli, James Malvasio, Matt Olson, Daniel Speer, and Orion Team Consulting. "BAMS UAS Manning and Fleet Integration Strategy." Thesis, Monterey, California. Naval Postgraduate School, 2010. http://hdl.handle.net/10945/7059.

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EMBA Project Report<br>EXECUTIVE SUMMARY: The Persistent Maritime Unmanned Aircraft Systems Program Office (PMA-262) has an expected initial operational capability (IOC) in FY2014 for the Broad Area Maritime Surveillance Unmanned Aerial System (BAMS UAS). Prior to IOC and in anticipation of operational testing with Air Test and Evaluation Squadron-One (VX-1), Orion Team Consulting (OTC) has been tasked to provide CAPT Kenney, Commanding Officer VX-1, a best-value course of action (COA) for manning and Fleet integration of the BAMS UAS. OTC used the following question to drive research and data

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Abdulazeez, Abdulhakeem. "Development of an autonomous control protocol for a UAV fleet network using machine learning." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG107.

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Dans le contexte de la robotique en réseau sans fil, des missions complexes, telles que la recherche et le sauvetage autonomes, peuvent nécessiter l’utilisation de plusieurs véhicules aériens sans pilote (UAV) déployés en formation afin d’augmenter l’efficacité et la qualité de service (QoS) tout en garantissant des impératifs spécifiques à la mission, tels que la maximisation de la couverture d’une zone en un seul passage de la flotte sur des sous-sections de celle-ci. Pour relever ces défis, un protocole efficace de localisation et de contrôle de formation est nécessaire.Les solutions exista

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Bellingham, John Saunders 1976. "Coordination and control of UAV fleets using mixed-integer linear programming." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/82252.

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Saif, Osamah. "Reactive navigation of a fleet of drones in interaction." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2269/document.

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De nos jours, les applications utilisant des quadrirotors autonomes sont en plein essor. La surveillance et la sécurité de sites industriels ou sensibles, de zones géographiques pour l’agriculture par exemple sont quelques-unes des applications les plus célèbres des véhicules aériens sans pilote (UAV). Actuellement, certains chercheurs et scientifiques se concentrent sur le déploiement multi-drones pour l’inspection et la surveillance de vastes zones. L’objectif de cette thèse est de concevoir des algorithmes afin de réaliser une commande de vol en formation distribuée/décentralisée de multi-U

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Bailon-Ruiz, Rafael. "Design of a wildfire monitoring system using fleets of Unmanned Aerial Vehicles." Thesis, Toulouse, INSA, 2020. http://www.theses.fr/2020ISAT0011.

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Les feux de forêt sont des incendies de végétation incontrôlés qui causent des dégâts importants à l’environnement, aux biens et aux personnes. Les actions de lutte contre de tels feux sont risqués et peuvent par conséquent bénéficier de techniques d'automatisation pour réduire l’exposition humaine. La télédétection aérienne est une technique qui permet d’obtenir des informations précises sur l’état d'un feu de forêt, afin que les équipes d’intervention puissent préparer des contre-mesures. Avec des véhicules aériens habités, elle expose les opérateurs à des risques élevés, qui peuvent être év

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Dietrich, Thomas [Verfasser], Armin [Akademischer Betreuer] Zimmermann, Andreas [Gutachter] Mitschele-Thiel, and Jean-Yves [Gutachter] Choley. "Energy-efficient resource management for continuous scenario fulfillment by UAV fleets / Thomas Dietrich ; Gutachter: Andreas Mitschele-Thiel, Jean-Yves Choley ; Betreuer: Armin Zimmermann." Ilmenau : TU Ilmenau, 2019. http://d-nb.info/1200353536/34.

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CHIEN, WEI-CHEN, and 簡偉臣. "A Study of Using A Fleet of UAV to Spray Pesticides in The Rice Field." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/nbvae8.

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碩士<br>國立雲林科技大學<br>工業工程與管理系<br>107<br>Rice is a staple Chinese food which is the largest crop in Taiwan. It is necessary to spray pesticides immediately for preventing the spread of pests and diseases. Nowadays, the unmanned aerial vehicles (UAVs) are invented to carry out pesticide spraying operations. It is also necessary to consider that the UAVs need to refill the pesticide and change the battery in a large field. This operation can be investigated as an agricultural field operations problem (AFOP) which is the major topic in this study. This study transforms the AFOP into a capacitated veh

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HSU, HUNG-MING, and 許宏銘. "A Study of Multi-Objective Genetic Algorithm Applying on Routing Problem of Heterogeneous UAV Fleet with Adjustable View Angle." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/vw6fkt.

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碩士<br>中華大學<br>資訊工程學系<br>105<br>Unmanned Aerial Vehicle (UAV) with the characteristic of low cost and high mobility. Compared to flying traditional aircraft, the requirements of landing UAV site is low, the skill of UAV operation is ease, and UAV is capable to avoid the risk of human accident. Therefore, it is very suitable for alternative personnel to carry out a variety kinds of high-risk missions. Among these missions, aerial photography is one of the most popular application of unmanned aerial vehicles. In this paper, we investigated the uses of heterogeneous UAVs in aerial photography mis

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Książki na temat "Fleet of UAVs"

Gilmore, Christopher, Michael Chaykowsky, and Brent Thomas. Autonomous Unmanned Aerial Vehicles for Blood Delivery: A UAV Fleet Design Tool and Case Study. RAND Corporation, 2019. http://dx.doi.org/10.7249/rr3047.

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Thomas, Brent, Christopher K. Gilmore, and Michael Chaykowsky. Autonomous Unmanned Aerial Vehicles for Blood Delivery: A UAV Fleet Design Tool and Case Study. RAND Corporation, The, 2020.

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Części książek na temat "Fleet of UAVs"

Radzki, Grzeogorz, Peter Nielsen, Amila Thibbotuwawa, Grzegorz Bocewicz, and Zbigniew Banaszak. "Declarative UAVs Fleet Mission Planning: A Dynamic VRP Approach." In Computational Collective Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63007-2_15.

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Guo, Qing, Zhengzhong Xiang, Jian Peng, and WenZheng Xu. "Data Collection of IoT Devices with Different Priorities Using a Fleet of UAVs." In Wireless Algorithms, Systems, and Applications. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19214-2_18.

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Thibbotuwawa, Amila, Peter Nielsen, Grzegorz Bocewicz, and Zbigniew Banaszak. "UAVs Fleet Mission Planning Subject to Weather Fore-Cast and Energy Consumption Constraints." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13273-6_11.

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Zaitseva, Elena, Vitaly Levashenko, Nicolae Brinzei, et al. "Reliability Assessment of UAV Fleets." In Emerging Networking in the Digital Transformation Age. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-24963-1_19.

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Akram, Raja Naeem, Konstantinos Markantonakis, Keith Mayes, et al. "A Secure and Trusted Channel Protocol for UAVs Fleets." In Information Security Theory and Practice. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93524-9_1.

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Radzki, G., P. Nielsen, G. Bocewicz, and Z. Banaszak. "UAV Fleet Mission Planning Subject to Robustness Constraints." In Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53829-3_4.

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Zhou, Zhenyu, and Yanchao Liu. "A Scalable Cloud-Based UAV Fleet Management System." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17629-6_22.

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Tožička, Jan, Benedek Szulyovszky, Guillaume de Chambrier, Varun Sarwal, Umar Wani, and Mantas Gribulis. "Application of Deep Reinforcement Learning to UAV Fleet Control." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01057-7_85.

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Altshuler, Yaniv, Alex Pentland, and Alfred M. Bruckstein. "Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of Reconnaissance UAVs." In Swarms and Network Intelligence in Search. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63604-7_8.

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Kliushnikov, Ihor, Vyacheslav Kharchenko, and Herman Fesenko. "UAV Fleet Routing with Battery Recharging for Nuclear Power Plant Monitoring Considering UAV Failures." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14841-5_29.

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Streszczenia konferencji na temat "Fleet of UAVs"

Karpstein, Robin, Victor Luis, and Florian Holzapfel. "Agent-based Simulation of UAV based Logistics Networks with Real World Data." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1407.

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Applications of Unmanned Aerial Vehicles (UAVs) are on the rise. Particularly within the healthcare sector the potential is huge as its cited as the most accepted application. This paper introduces an agent-based simulation to evaluate the network performance of UAV-based logistics networks in healthcare. The simulation is applied to a hypothetical real-world network. During a simulated day, the UAV fleet performs 212 flights, including 97 delivery flights, amounting to 4264 minutes enroute and covering a distance of 5941 kilometers. The analysis reveals average non-idle and mission utilizatio

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Zagar, Maxime, Luc Meyer, Michel Kieffer, and Héléne Piet-Lahanier. "Target Search and Tracking in an Unknown Environment by a Fleet of UAVs using a Set-Membership Approach." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886702.

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Ding, Jianing, Hai-Tao Zhang, and Bin-Bin Hu. "Coordinated Landing Control for Cross-Domain UAV-USV Fleets Using Heterogeneous-Feature Matching." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611392.

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Paudel, Saroj, Jiangfeng Zhang, Beshah Ayalew, and Annette Skowronska. "Charging Load Estimation for a Fleet of Autonomous Vehicles." In WCX SAE World Congress Experience. SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2025.

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<div class="section abstract"><div class="htmlview paragraph">In intelligent surveillance and reconnaissance (ISR) missions, multiple autonomous vehicles, such as unmanned ground vehicles (UGVs) or unmanned aerial vehicles (UAVs), coordinate with each other for efficient information gathering. These vehicles are usually battery-powered and require periodic charging when deployed for continuous monitoring that spans multiple hours or days. In this paper, we consider a mobile host charging vehicle that carries distributed sources, such as a generator, solar PV and battery, and is dep

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Kopyt, Antoni, Janusz P. Narkiewicz, Tomasz Malecki, and Pawel Radyisyewski. "Optimal Object Location by a Fleet of Various UAVs." In AIAA Modeling and Simulation Technologies Conference. American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-2332.

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Bailon-Ruiz, Rafael, Simon Lacroix, and Arthur Bit-Monnot. "Planning to Monitor Wildfires with a Fleet of UAVs." In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8593859.

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D'Amato, Egidio, Immacolata Notaro, Barbara Iodice, Giulia Panico, and Luciano Blasi. "Decentralized Moving Horizon Estimation for a Fleet of UAVs." In 2022 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2022. http://dx.doi.org/10.1109/icuas54217.2022.9836138.

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Kaneko, Shugo, and Joaquim R. Martins. "Fleet Design of Package Delivery UAVs Considering the Operations." In AIAA SCITECH 2022 Forum. American Institute of Aeronautics and Astronautics, 2022. http://dx.doi.org/10.2514/6.2022-1503.

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Bodin, François, Tristan Charrier, Arthur Queffelec, and François Schwarzentruber. "Generating Plans for Cooperative Connected UAVs." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/846.

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We present a tool for graph coverage with a fleet of UAVs. The UAVs must achieve the coverage of an area under the constraint of staying connected with the base, where the mission supervisor starts the plan. With an OpenStreetMap interface, the user is able to choose a specific location on which the mission needs to be generated and observes the resulting plan being executed.

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Verdu, Titouan, Gautier Hattenberger, and Simon Lacroix. "Flight patterns for clouds exploration with a fleet of UAVs." In 2019 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2019. http://dx.doi.org/10.1109/icuas.2019.8797953.

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