Relevant bibliographies by topics / Fleet of UAVs

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Academic literature on the topic 'Fleet of UAVs'

Author: Grafiati
Published: 14 December 2024

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Journal articles on the topic "Fleet of UAVs"

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Фесенко, Герман Вікторович, and Вячеслав Сергійович Харченко. "МОДЕЛІ НАДІЙНОСТІ УГРУПОВАНЬ ФЛОТІВ БПЛА З КОВЗНИМ РЕЗЕРВУВАННЯМ ДЛЯ МОНІТОРИНГУ ПОТЕНЦІЙНО НЕБЕЗПЕЧНИХ ОБ’ЄКТІВ." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 2 (June 21, 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 fleet group with k-out-of-n redundancy. In order to take into account the reliability of the control station for various variants of the organization of UAV fleet groups, it is advisable to formulate the following tasks: to analyze the different structures of UAV fleet groups taking into account the scheme of activation of redundant UAVs; to develop and investigate models of reliability of UAV fleet groups with a centralized, decentralized and partially decentralized schemes of activation of redundant UAVs with the possibility of control station redundancy; to formulate recommendations for choosing a scheme for activation of redundant UAVs. Research results: the structure of the UAV fleet group with two-level k-out-of-n redundancy (at fleet levels and their groups using the reserve fleet) and different variants of organization of control stations were proposed. The centralized, decentralized and partially decentralized structures of activation of the redundant UAVs for a fleet group with reserve (reserve fleets) are investigated, namely: the reliability block diagrams of these systems are constructed; analytical expressions for calculating the probability of failure-free operation of the UAV fleet group based on each of these schemes are obtained; the following dependencies are obtained and investigated: probability of failure-free operation of a UAV fleet group with different probabilities of UAV failure-free operation from the number of the main fleets in case of use of reserve fleet with three UAVs; the probability of UAV fleet group failure-free operation with different schemes of activation of redundant UAVs from the number of main fleets in the case of using a reserve fleet with three UAVs. The development and research of reliability models have made it possible to formulate tasks regarding the choice of schemes for activation of redundant UAVs and the corresponding recommendations for the organization of groups. Further research is appropriate to focus on developing software to support decision-making on choosing the options for structures and taking into account possible schemes to get areas of responsibility by UAVs.
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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 (September 22, 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 manner. However, at present, there is a lack of decision support tools and methods for mission planners that consider all these influencing aspects together. To bridge this gap, this paper presents a decomposed solution approach, which provides decision support for UAVs’ fleet mission planning. The proposed approach assists flight mission planners in aerospace companies to select and evaluate different mission scenarios, for which flight-mission plans are obtained for a given fleet of UAVs, while guaranteeing delivery according to customer requirements in a given time horizon. Mission plans are analyzed from multiple perspectives including different weather conditions (wind speed and direction), payload capacities of UAVs, energy capacities of UAVs, fleet sizes, the number of customers visited by a UAV on a mission and delivery performance. The proposed decision support-driven declarative model supports the selection of the UAV mission planning scenarios subject to variations on all these configurations of the UAV system and variations in the weather conditions. The computer simulation based experimental results, provides evidence of the applicability and relevance of the proposed method. This ultimately contributes as a prototype of a decision support system of UAVs fleet-mission planning, able to determine whether is it possible to find a flight-mission plan for a given fleet of UAVs guaranteeing customer satisfaction under the given conditions. The mission plans are created in such a manner that they are suitable to be sent to Air Traffic Control for flight approval.
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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 (January 25, 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 is adopted. Specifically, the neural network consists of a grid of neurons, where each neuron has a local cost and has a local connection only with neighbor neurons. The cost of each neuron influences the cost of its neighbors, generating an attractive contribution to unvisited neurons. We introduce several controls and precautions to minimize the risk of collisions and optimize coverage planning. Then, preliminary simulations are performed in different scenarios by testing the algorithm in four maps and with fleets consisting of 3 to 10 vehicles. Results confirm the ability of the proposed approach to manage and coordinate the fleet providing the full coverage of the map in every tested scenario, avoiding collisions between vehicles, and uniformly distributing the fleet on the map.
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Larson, Jonathan, Paul Isihara, Gabriel Flores, Edwin Townsend, Danilo R. Diedrichs, Christy Baars, Steven Kwon, et al. "A priori assessment of a smart-navigated unmanned aerial vehicle disaster cargo fleet." SIMULATION 96, no. 8 (June 7, 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 supplies in Puerto Rico after Hurricane Maria (2017) pose a compelling use scenario for UAV disaster cargo delivery. In this context, we introduce a General Purpose Assessment Model (GPAM) that can estimate the potential effectiveness of a cargo UAV fleet for any given response region. We evaluate this model using the following standards: (i) realistic specifications; (ii) stable output for various realistic specifications; and (iii) support of humanitarian goals. To this end, we discuss data from humanitarian cargo delivery field tests and feedback from practitioners, perform sensitivity analyses, and demonstrate the advantage of using humanitarian rather than geographic distance in making fleet delivery assignments. We conclude with several major challenges faced by those who wish to implement smart-navigated UAV cargo fleets in disaster response, and the need for further GPAM development. This paper proposes the GPAM as a useful simulation tool to encourage and guide steps toward humanitarian use of UAVs for cargo delivery. The model’s flexibility can allow organizations to quickly and effectively determine how best to respond to disasters.
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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 (January 16, 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 influenced by different values of the mission parameters, such as the fleet size, travel distance, wind direction, and wind speed. Computational experiments show the results that allow assessing alternative strategies of UAV mission planning.
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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 (August 11, 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 while avoiding obstacles like mines. The UAVs inherently come with a limited battery capacity directing our research to focus on the minimum task completion time. We propose a continuous-time-based Proximal Policy Optimization (PPO) algorithm for multi-aGent Learning In Dynamic Environments (GLIDE). In GLIDE, the UAVs coordinate among themselves and communicate with the central base to choose the best possible action. The action control in GLIDE can be controlled in a centralized and decentralized way, and two algorithms called Centralized-GLIDE (C-GLIDE), and Decentralized-GLIDE (D-GLIDE) are proposed on this basis. We developed a simulator called UAV SIM, in which the mines are placed at randomly generated 2D locations unknown to the UAVs at the beginning of each episode. The performance of both the proposed schemes is evaluated through extensive simulations. Both C-GLIDE and D-GLIDE converge and have comparable performance in target destruction rate for the same number of targets and mines. We observe that D-GLIDE is up to 68% faster in task completion time compared to C-GLIDE and could keep more UAVs alive at the end of the task.
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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 (June 28, 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 schedule of tasks performed by the fleet of vehicles. To solve the problem efficiently, we formulate it as an optimization problem on an infinite periodic graph and reduce it to a special type of parametric assignment problem. We exactly solve the problem under consideration in O(n3) time, where n is the number of flights performed by UAVs.
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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 that are both used as black boxes. Furthermore, the Variable Neighborhood Search (VNS) procedure considered facilitates the adaptation of the algorithm to specific requirements through the addition of new neighborhoods. We demonstrate the feasibility and convenience of the method on a large joint observation task in which a fleet of fixed-wing UAVs maps wildfires over areas of a hundred square kilometers. The approach allows generating plans over tens of minutes for a handful of UAVs in matter of seconds, even when considering very short primitive maneuvers.
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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 (October 11, 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 capacity of the backhaul link among the UAVs and the ground station: the bitrate delivered per UAV and the total backhaul bandwidth shared among all the UAVs. Additionally, an energy consumption model is considered to evaluate the efficiency and viability of the proposed strategy. Finally, numerical results in different scenarios are provided to assess both the schedulers performance and the proposed coordinated positioning strategy for the UAVs.
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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 environments show that the approach for UAVs high quality flight corridors planning is real-time and effective.
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Dissertations / Theses on the topic "Fleet of UAVs"

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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 caméra et d'algorithmes de traitement d'images qui fournissent des mesures liées à la RdI. Les mesures fournies par le CVS consistent en des images avec des pixels labellisés, des cartes de profondeur et des boîtes contenant des pixels associés aux cibles qui ont été identifiées. Cette thèse considère une approche ensembliste pour aborder le problème CSAT. Par rapport à d'autres approches reposant sur des hypothèses stochastiques sur le bruit de mesure, les approches ensemblistes supposent un bruit de mesure borné avec des bornes connues. Ces approches peuvent alors caractériser des ensembles qui contiennent nécessairement la position des cibles, à condition que les hypothèses sur les modèles de mesure et les bornes du bruit soient respectées. Très peu de travaux antérieurs exploitent directement les mesures CVS dans une approche ensembliste. Cela s'explique principalement par le fait qu'il est difficile d'obtenir des modèles de mesure pour un CVS lorsqu'il est composé d'algorithmes d'apprentissage profond. Pour résoudre ces problèmes, nous introduisons plusieurs hypothèses pour relier les mesures CVS aux cibles et aux obstacles présents dans la RdI en utilisant une approche géométrique. A partir de ces hypothèses, nous proposons un nouvel estimateur ensembliste qui exploite directement les mesures CVS pour caractériser les ensembles qui sont garantis de contenir la position de chaque cible identifiée. Les mesures CVS sont également exploitées pour évaluer les ensembles qui sont garantis ne contenir aucune position de cible. Un processus de prédiction-correction similaire au filtre de Kalman a été implémenté pour tenir compte de l'échange d'informations entre les drones. La correction se fait via les mesures CVS acquises par chaque drone et des estimées transmises par les drones voisins. Plusieurs incertitudes supplémentaires peuvent être prises en compte dans l'approche proposée. Nous nous sommes concentrés sur la manière de prendre en compte l'incertitude de l'état des drones. Comme la RdI est encombrée d'obstacles inconnus, chaque drone établit une carte d'occupation et d'élévation pendant la recherche et le suivi des cibles à l'aide des mesures CVS. Cette carte fournit une description approximative de l'emplacement, de la hauteur, et de la forme des obstacles. La carte peut être exploitée pour éviter les obstacles. Dans cette thèse, elle est utilisée pour prédire la portion du champ de vue de la caméra de chaque drone qui est masquée par un obstacle. Cette information est déterminante dans la conception d'un algorithme par commande prédictive (MPC) pour déterminer la trajectoire de chaque drone, en minimisant l'incertitude de localisation des cibles identifiées et en réduisant la taille de l'ensemble contenant des cibles potentiellement non détectées. Si les cibles sont plus nombreuses que les UAVs, un compromis entre la recherche de nouvelles cibles et le suivi des cibles déjà identifiées afin de réduire l'incertitude de la localisation doit être trouvé. Des simulations réalisées avec Webots illustrent les performances de l'estimateur et de la loi de guidage en évaluant l'efficacité des drones à trouver et identifier les cibles, et maintenir une estimation précise de leur position
This thesis addresses the problem of searching and tracking an unknown number of mobile targets spread within an unknown Region of Interest (RoI) using a fleet of cooperating Unmanned Aerial Vehicles (UAVs). Such Cooperative Search, Acquisition, and Tracking (CSAT) problem appears in military contexts, e.g., when enemy vehicles have to be found and tracked, or in civilian applications, e.g., when searching for lost people after a disaster. To solve the CSAT problem, each UAV embeds a Computer Vision System (CVS) consisting of a camera and image processing algorithms that provide measurements related to the RoI. The CVS measurements consist of images with labeled pixels, depth maps, and boxes in the images containing pixels related to the detected and identified targets. This thesis considers a set-membership approach to address the CSAT problem. Compared to alternative approaches relying on stochastic assumptions on the measurement noise, set-membership approaches assume bounded measurement noise with known bounds. Set-membership approaches can then characterize sets that are guaranteed to contain the location of the targets, provided that the hypotheses on the measurement models and noise bounds are satisfied. Very few previous works directly exploit CVS measurements in a set-membership approach. This is mainly because obtaining measurement models for a CVS involving deep learning algorithms is difficult. To address these issues, we introduce several assumptions to relate the CVS measurements with the targets and obstacles present in the RoI using a geometric approach. With these assumptions, we propose a new set-membership estimator that directly exploits the CVS measurements to characterize sets that are guaranteed to contain the location of each identified target. The CVS measurements are also exploited to evaluate sets that are guaranteed to contain no target location. A prediction-correction scheme similar to the Kalman filter has been considered to account for the exchange of information between UAVs. The correction involves CVS measurements acquired by each UAV and estimates shared by neighboring UAVs. Several additional sources of uncertainty may be considered in the proposed approach. We have focused on the state uncertainty of UAVs. As the RoI is cluttered with unknown obstacles, each UAV builds an occupancy-elevation map during the search and tracking of targets using CVS measurements. The map provides an approximate description of the location, height, and shape of the obstacles. The map may be exploited for obstacle avoidance. In this thesis, it is used to predict the occlusion by obstacles in the field of view of each UAV. This information is instrumental in the design of a Model Predictive Control (MPC) algorithm to determine the trajectory of each UAV, minimizing the localization uncertainty of identified targets and reducing the size of the set containing potentially undetected targets. If the targets outnumber the UAVs, a trade-off has to be found between searching for new targets and tracking those already identified to reduce the localization uncertainty. Simulations performed with Webots illustrate the performance of the target location estimator and the MPC design by evaluating the efficiency of the cooperating UAVs to explore the environment, find and identify the targets, and maintain an accurate estimation of their location
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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
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 analysis to provide a cogent plan for the future manning of the UAS: Considering both manning cost and capability, which of the three COAs listed below is the best manning strategy for Fleet-wide employment of the BAMS UAS? 1. BAMS UAS Standalone Squadron 2. Joint BAMS / Global Hawk UAS Squadron 3. Combined BAMS UAS / P-8A Squadron OTC employed current and available manpower data, baseline BAMS UAS operational data, and existing Global Hawk (GH) UAS manpower and operational data to analyze the three courses of action (COAs). Additionally, OTC utilized the current BAMS UAS Manpower Business Case Analysisi We recommend that BAMS UAS Fleet integration be accomplished via a combined BAMS UAS / P-8A squadron. This recommendation has been determined by accounting for manning cost and capabilities. The preponderance of reasons for this recommendation is centered on the manning cost saving over the other two COAs coupled with an increase in organic capabilities and ability to leverage intelligence sharing/support. This would effectively augment the maritime ISR capabilities of the P-8A Poseidon, provide best-value accounting for capabilities, and allow for career (MBCA) as the benchmark for determining the best-value COA for manning and Fleet integration. This report will provide insight into the essential data utilized, key assumptions made, and the final recommendations derived through extensive research and coordination with the USAF 303rd Aeronautical Systems Group (AESG) – the Global Hawk UAS program office, and PMA-262. We recommend that BAMS UAS Fleet integration be accomplished via a combined BAMS UAS / P-8A squadron. This recommendation has been determined by accounting for manning cost and capabilities. The preponderance of reasons for this recommendation is centered on the manning cost saving over the other two COAs coupled with an increase in organic capabilities and ability to leverage intelligence sharing/support. This would effectively augment the maritime ISR capabilities of the P-8A Poseidon, provide best-value accounting for capabilities, and allow for career progression for the BAMS UAS air vehicle operators (AVOs), mission coordinators (MSN COORD), sensor operators, and maintainers.
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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-UAVs en temps réel dans une perspective de systèmes de systèmes. Tout d’abord, nous avons passé en revue certains travaux récents de la littérature sur la commande de vol en formation et la commande de quadrirotors. Nous avons présenté une brève introduction sur les systèmes de systèmes, leur définition et leurs caractéristiques. Ensuite, nous avons introduit la commande de vol en formation avec ses structures les plus utilisées dans la littérature. Nous avons alors présenté quelques travaux existants traitant du flocking (comportement de regroupement en flotte), les méthodes de modélisation les plus utilisés pour les quadrirotors et quelques approches de commande les plus utilisées pour stabiliser des quadrirotors. Deuxièmement, nous avons utilisé la structure de la commande comportementale pour réaliser un vol en formation de plusieurs UAVs. Nous avons conçu un comportement pour réaliser le vol en formation de multi-UAVs sans fragmentation. Le comportement proposé traite le problème flocking dans une perspective globale, c’est-à-dire, nous avons inclus une tendance dans chaque drone pour former une formation. Les défis des Systèmes de systèmes nous a motivés à chercher des algorithmes de flocking et de consensus introduits dans la littérature qui peuvent être utiles pour répondre à ces défis. Cela nous a amenés à proposer quatre lois de commande en visant à être compatibles avec le modèle non linéaire des quadrirotors et pouvant être expérimentés sur des plates-formes réelles. Les lois de commande ont été exécutées à bord de chaque quadrirotor dans la formation et chaque quadrirotor interagit avec ses voisins pour assurer un vol en formation sans collision. Enfin, nous avons validé nos lois de commande par des simulations et des expériences en temps réel. Pour la simulation, nous avons utilisé un simulateur de multi quadrirotors développé au laboratoire Heudiasyc. Pour les expériences, nous avons mis en œuvre nos lois de contrôle sur des quadrirotors ArDrone2 évolués dans un environnement intérieur équipé d’un système de capture de mouvement (Optitrack)
Nowadays, applications of autonomous quadrotors are increasing rapidly. Surveillance and security of industrial sites, geographical zones for agriculture for example are some popular applications of Unmanned Aerial Vehicles (UAVs). Nowadays, researchers and scientists focus on the deployment of multi-UAVs for the inspection and the surveillance of large areas. The objective of this thesis is to design algorithms and techniques to perform a real-time distributed/decentralized multi-UAVs flight formation control, from a system of systems perspective. Firstly, we reviewed recent works of the literature about flight formation control and the control of quadrotors. We presented a brief introduction about systems of systems, their definition and characteristics. Then, we introduced the flight formation control with its most used structures in the literature, some existing works dealing with flocking. Finally, we presented the most used modeling methodologies for quadrotors and some control approaches that are used to stabilize quadrotors. Secondly, we used the behavioral-based control structure to achieve a multiple UAV flocking. We conceived a behavior intending to address the control design towards a successful achievement of the flocking task without fragmentation. The proposed behavior treats the flocking problem from a global perspective, that is, we included a tendency of separated UAVs to form a flock.System of systems challenges motivated us to look for flocking and consensus algorithms introduced in the literature that could be helpful to answer to these challenges. This led us to propose four flocking control laws aiming at being compatible with the nonlinear model of quadrotors and at being implemented on experimental platforms. The control laws were run aboard each quadrotor in the flock. By running the control law, each quadrotor interacts with its neighbors to ensure a collision-free flocking. Finally, we validated our proposed control laws by simulations and real-time experiments. For the simulation, we used a PC-based simulator of flock of multiple quadrotors which was developed at Heudiasyc laboratory. For experiments, we implemented our control laws on ArDrone2 quadrotors evolved in an indoor environment equipped with an Optitrack motion capture system
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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 évités par l’utilisation de véhicules autonomes. Cette thèse présente un système de surveillance de feux de forêt basé sur des flottes de véhicules aériens sans pilote (UAV) afin de fournir aux pompiers des renseignements précis et à jour sur un feu de forêt. Nous présentons une approche pour planifier les trajectoires d’une flotte de drones à voilure fixe afin d’observer un feu de forêt évoluant au fil du temps. Des modèles réalistes du terrain, du processus de propagation du feu et des drones sont exploités, ainsi qu’un modèle du vent, pour prédire la propagation des feux de forêt et planifier le mouvement des drones. L’approche présentée adapte une méthode générique de recherche à voisinage variable (VNS) à ces modèles et les contraintes associées. L’exécution de la mission d’observation planifiée fournit des cartes des feux de forêt qui sont transmises à l’équipe d’intervention et exploitées par l’algorithme de planification pour déterminer de nouvelles trajectoires d’observation. Les algorithmes et les modèles sont intégrés dans une architecture logicielle permettant l’exécution dans des scénarios avec différents niveaux de réalisme, avec des drones réels et simulés survolant un feu de forêt réel ou synthétique. Les résultats de simulation mixte montrent la capacité de planifier les trajectoires d’observation d’une petite flotte de drones et de mettre à jour les plans lorsque de nouvelles informations sur l’incendie sont incorporées dans le modèle de propagation de feu
Wildfires, also known as forest or wildland fires, are uncontrolled vegetation fires occurring in rural areas that cause tremendous damage to the society, harming environment, property and people. The firefighting endeavor is a dull, dirty and dangerous job and as such, can greatly benefit from automation to reduce human exposure to hazards. Aerial remote sensing is a common technique to obtain precise information about a wildfire state so fire response teams can prepare countermeasures. This task, when performed with manned aerial vehicles, expose operators to high risks that can be eliminated by the use of autonomous vehicles. This thesis introduces a wildfire monitoring system based on fleets of unmanned aerial vehicles (UAVs) to provide firefighters with timely updated information about a wildland fire. We present an approach to plan trajectories for a fleet of fixed-wing UAVs to observe a wildfire evolving over time. Realistic models of the terrain, of the fire propagation process, and of the UAVs are exploited, together with a model of the wind, to predict wildfire spread and plan UAV motion. The approach tailors a generic Variable Neighborhood Search method to these models and the associated constraints. The execution of the planned monitoring mission provides wildfire maps that are transmitted to the fire response team and exploited by the planning algorithm to plan new observation trajectories. Algorithms and models are integrated within a software architecture allowing for execution under scenarios with different levels of realism, with real and simulated UAVs flying over a real or synthetic wildfire. Mixed-reality simulation results show the ability to plan observation trajectories for a small fleet of UAVs, and to update the plans when new information on the fire are incorporated in the fire model
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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
國立雲林科技大學
工業工程與管理系
107
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 vehicle routing problem (CVRP). The goal of this study is to find an efficient method for arranging UAVs routing, refilling pesticides operation, and schedule of changing the battery. A mixed integer linear program mathematical model is developed to minimize the maximum operating time of UAVs. The overall operation time includes: (1) travel time, (2) operation time, (3) refilling time, and (4) changing battery time. A mathematical model is developed first. The solution algorithm is then developed by using the logic of Mix-opt simulated annealing algorithm. The solution program using Python language is also developed to solve a real-world problem. The Taguchi method is used to find better parameters used in the solution algorithm. Finally, a sensitivity analysis is conducted including: (1) adjust the UAVs spraying direction, (2) adjust the number of spraying UAVs, and (3) adjust the UAVs spraying rate. Based on the results of sensitivity analysis, suggestions and conclusions are made to provide the management level of agricultural operations.
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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
中華大學
資訊工程學系
105
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 mission within a limited area. In the literature, numerous approaches have been proposed in UAV path planning, such as adding UAV flight parameter control, single or multiple UAVs flight path planning ... and so on. However, none of them discussed heterogeneous UAVs. In this paper, the concept of flying path is not equivalent to the cruise path is proposed. The objective of UAV path planning should meet the requirement of aerial photography mission while optimizes the mission costs. Therefore, we investigated the heterogeneous UAVs with adjustable image angle in aerial photography mission. A multiobjective optimization problem is formulated, considering the coverage of UAV camera, flight distance of UAVs, energy consumption, and the configuration of heterogeneous UAVs. In this paper, we proposed a multi-objective evolutionary approach to solve the investigated problem. From the experimental results, the proposed approach can effectively obtain multiple non-dominated solutions. The solutions also shows that the decoded routes of UAVs path is very similar to the appearance of the demand model.
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Books on the topic "Fleet of UAVs"

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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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Book chapters on the topic "Fleet of UAVs"

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Radzki, Grzeogorz, Peter Nielsen, Amila Thibbotuwawa, Grzegorz Bocewicz, and Zbigniew Banaszak. "Declarative UAVs Fleet Mission Planning: A Dynamic VRP Approach." In Computational Collective Intelligence, 188–202. Cham: 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, 218–30. Cham: 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, 104–14. Cham: 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, Andriy Kovalenko, Marina Yelis, Viktors Gopejenko, and Ravil Mukhamediev. "Reliability Assessment of UAV Fleets." In Emerging Networking in the Digital Transformation Age, 335–57. Cham: 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, Pierre-François Bonnefoi, Amina Cherif, Damien Sauveron, and Serge Chaumette. "A Secure and Trusted Channel Protocol for UAVs Fleets." In Information Security Theory and Practice, 3–24. Cham: 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, 35–47. Cham: 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, 203–18. Cham: 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, 1169–77. Cham: 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, 207–38. Cham: 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, 442–54. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14841-5_29.

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Conference papers on the topic "Fleet of UAVs"

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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, 1–16. 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 utilization of 66% and 33%, respectively. The study also calculates annual network costs of EUR 2.23Mn, with a majority of it being direct costs (54.5%). Further sensitivity analysis identifies the biggest influences of battery capacity, C-Rate, and operator-to-UAV ratio on network performance and costs, highlighting these factors as critical for future optimization. Additionally, the benefit of incorporating various different UAV types into the network is only given if each UAV provides a unique value proposition to enhance the network performance.
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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), 12041–47. 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. 400 Commonwealth Drive, Warrendale, PA, United States: 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 deployed in the area where the UAVs and UGVs operate. However, due to uncertainties, the state of charge of UAV and UGV batteries, their arrival time at the charging location and the charging duration cannot be predicted accurately. We propose a stochastic modeling approach to deal with these uncertainties based on certain physical assumptions such as the flight time for a UAV, distance travelled for a UGV, and the final state of charge of the battery before they leave the host charging vehicle. Based on the stochastic model, an aggregated charging power demand is forecasted. A model predictive control-based operation is then used for the operation of the distributed sources on the host vehicle to meet the forecasted charging power demand. The host vehicle battery works as a buffer during abrupt changes in the charging power demand. The operational scenario is simulated with ten UAVs, ten UGVs and a host vehicle carrying a diesel generator, a battery pack and a PV system. The result of this work is applicable to energy-aware charging management for a fleet of vehicles.</div></div>
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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. Reston, Virginia: 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. Reston, Virginia: 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}. California: 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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Belkadi, A., L. Ciarletta, and D. Theilliol. "UAVs fleet control design using distributed particle swarm optimization: A leaderless approach." In 2016 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2016. http://dx.doi.org/10.1109/icuas.2016.7502679.

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