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1
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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Slim, M., M. Saied, H. Mazeh, H. Shraim, and C. Francis. "Fault-Tolerant Control Design for Multirotor UAVs Formation Flight." Giroskopiya i Navigatsiya 29, no. 2 (2021): 78–96. http://dx.doi.org/10.17285/0869-7035.0064.
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This paper proposes the use of the Gbest-Guided Artificial Bee Colony (GABC) algorithm to solve an online optimization problem for the leaderless distributed formation control of hexarotor Unmanned Aerial Vehicles (UAVs). The GABC is employed to optimize a cost function for each agent while ensuring the convergence of the fleet to the target position and averting both obstacles and collisions with other UAVs. The GABC algorithm has been shown to be competitive with some other conventional biological-inspired algorithms such as the Particle Swarm Optimization (PSO). Fault-Tolerant Control (FTC) methods are presented and tested on several scenarios, particularly we considered the cases of loss of agents and actuator faults in the fleet. Results show the success of the proposed FTC methods to minimize the faults effect on the formation final goal.
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Grasso, Christian, and Giovanni Schembra. "A Fleet of MEC UAVs to Extend a 5G Network Slice for Video Monitoring with Low-Latency Constraints." Journal of Sensor and Actuator Networks 8, no. 1 (January 1, 2019): 3. http://dx.doi.org/10.3390/jsan8010003.
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In the last decade, video surveillance systems have become more and more popular. Thanks to a decrease in price of video camera devices and the diffusion of cheap small unmanned aerial vehicles (UAVs), video monitoring is today adopted in a wide range of application cases, from road traffic control to precision agriculture. This leads to capture a great amount of visual material to be monitored and screened for event detection. However, information that is gathered from a platform of video monitoring UAVs may produce high-volume data, whose processing is unfeasible to be done locally by the same UAVs that perform monitoring. Moreover, because of the limited bandwidth of wireless links connecting UAVs to computing infrastructures that are installed on ground, offloading these data to edge clouds renders these platforms infeasible for video analysis applications with low-latency requirements. The target of this paper is to extend a 5G network slice for video monitoring with a Flying Ad-hoc NETwork (FANET) constituted by UAVs with multi-access edge computing (MEC) facilities (MEC UAVs), flying very close to the layer of UAVs monitoring the area of interest. A policy for mutual help among MEC UAVS is defined in order to increase the performance of the whole aerial MEC platform, so further reducing end-to-end latency between sources and actuators, and increasing system reliability. A use case is considered for a numerical analysis of the proposed platform.
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Zaitseva, Elena, Vitaly Levashenko, Ravil Mukhamediev, Nicolae Brinzei, Andriy Kovalenko, and Adilkhan Symagulov. "Review of Reliability Assessment Methods of Drone Swarm (Fleet) and a New Importance Evaluation Based Method of Drone Swarm Structure Analysis." Mathematics 11, no. 11 (June 1, 2023): 2551. http://dx.doi.org/10.3390/math11112551.
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Drones, or UAVs, are developed very intensively. There are many effective applications of drones for problems of monitoring, searching, detection, communication, delivery, and transportation of cargo in various sectors of the economy. The reliability of drones in the resolution of these problems should play a principal role. Therefore, studies encompassing reliability analysis of drones and swarms (fleets) of drones are important. As shown in this paper, the analysis of drone reliability and its components is considered in studies often. Reliability analysis of drone swarms is investigated less often, despite the fact that many applications cannot be performed by a single drone and require the involvement of several drones. In this paper, a systematic review of the reliability analysis of drone swarms is proposed. Based on this review, a new method for the analysis and quantification of the topological aspects of drone swarms is considered. In particular, this method allows for the computing of swarm availability and importance measures. Importance measures in reliability analysis are used for system maintenance and to indicate the components (drones) whose fault has the most impact on the system failure. Structural and Birnbaum importance measures are introduced for drone swarms’ components. These indices are defined for the following topologies: a homogenous irredundant drone fleet, a homogenous hot stable redundant drone fleet, a heterogeneous irredundant drone fleet, and a heterogeneous hot stable redundant drone fleet.
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Rinaldi, Marco, and Stefano Primatesta. "Comprehensive Task Optimization Architecture for Urban UAV-Based Intelligent Transportation System." Drones 8, no. 9 (September 10, 2024): 473. http://dx.doi.org/10.3390/drones8090473.
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This paper tackles the problem of resource sharing and dynamic task assignment in a task scheduling architecture designed to enable a persistent, safe, and energy-efficient Intelligent Transportation System (ITS) based on multi-rotor Unmanned Aerial Vehicles (UAVs). The addressed task allocation problem consists of heterogenous pick-up and delivery tasks with time deadline constraints to be allocated to a heterogenous fleet of UAVs in an urban operational area. The proposed architecture is distributed among the UAVs and inspired by market-based allocation algorithms. By exploiting a multi-auctioneer behavior for allocating both delivery tasks and re-charge tasks, the fleet of UAVs is able to (i) self-balance the utilization of each drone, (ii) assign dynamic tasks with high priority within each round of the allocation process, (iii) minimize the estimated energy consumption related to the completion of the task set, and (iv) minimize the impact of re-charge tasks on the delivery process. A risk-aware path planner sampling a 2D risk map of the operational area is included in the allocation architecture to demonstrate the feasibility of deployment in urban environments. Thanks to the message exchange redundancy, the proposed multi-auctioneer architecture features improved robustness with respect to lossy communication scenarios. Simulation results based on Monte Carlo campaigns corroborate the validity of the approach.
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Reineman, Benjamin D., Luc Lenain, and W. Kendall Melville. "The Use of Ship-Launched Fixed-Wing UAVs for Measuring the Marine Atmospheric Boundary Layer and Ocean Surface Processes." Journal of Atmospheric and Oceanic Technology 33, no. 9 (September 2016): 2029–52. http://dx.doi.org/10.1175/jtech-d-15-0019.1.
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AbstractThe deployment and recovery of autonomous or remotely piloted platforms from research vessels have become a way of significantly extending the capabilities and reach of the research fleet. This paper describes the use of ship-launched and ship-recovered Boeing–Insitu ScanEagle unmanned aerial vehicles (UAVs). The UAVs were instrumented to characterize the marine atmospheric boundary layer (MABL) structure and dynamics, and to measure ocean surface processes during the October 2012 Equatorial Mixing (EquatorMix) experiment in the central Pacific and during the July 2013 Trident Warrior experiment off the Virginia coast. The UAV measurements, including atmospheric momentum and radiative, sensible, and latent heat fluxes, are complemented by measurements from ship-based instrumentation, including a foremast MABL eddy-covariance system, lidar altimeters, and a digitized X-band radar system. During EquatorMix, UAV measurements reveal longitudinal atmospheric roll structures not sampled by ship measurements, which contribute significantly to vertical fluxes of heat and momentum. With the nadir-looking UAV lidar, surface signatures of internal waves are observed, consistent and coherent with measurements from ship-based X-band radar, a Hydrographic Doppler Sonar System, and a theoretical model. In the Trident Warrior experiment, the instrumented UAVs were used to demonstrate real-time data assimilation of meteorological data from UAVs into regional coupled ocean–atmosphere models. The instrumented UAVs have provided unprecedented spatiotemporal resolution in atmospheric and oceanographic measurements in remote ocean locations, demonstrating the capabilities of these platforms to extend the range and capabilities of the research fleet for oceanographic and atmospheric studies.
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Bocewicz, Grzegorz, Grzegorz Radzki, Izabela Nielsen, Marcin Witczak, and Banaszak Zbigniew. "UAVs fleet mission planning robust to changing weather conditions." IFAC-PapersOnLine 53, no. 2 (2020): 10518–24. http://dx.doi.org/10.1016/j.ifacol.2020.12.2798.
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Bailon-Ruiz, Rafael, Arthur Bit-Monnot, and Simon Lacroix. "Real-time wildfire monitoring with a fleet of UAVs." Robotics and Autonomous Systems 152 (June 2022): 104071. http://dx.doi.org/10.1016/j.robot.2022.104071.
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Lagkas, Thomas, Vasileios Argyriou, Stamatia Bibi, and Panagiotis Sarigiannidis. "UAV IoT Framework Views and Challenges: Towards Protecting Drones as “Things”." Sensors 18, no. 11 (November 17, 2018): 4015. http://dx.doi.org/10.3390/s18114015.
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Unmanned aerial vehicles (UAVs) have enormous potential in enabling new applications in various areas, ranging from military, security, medicine, and surveillance to traffic-monitoring applications. Lately, there has been heavy investment in the development of UAVs and multi-UAVs systems that can collaborate and complete missions more efficiently and economically. Emerging technologies such as 4G/5G networks have significant potential on UAVs equipped with cameras, sensors, and GPS receivers in delivering Internet of Things (IoT) services from great heights, creating an airborne domain of the IoT. However, there are many issues to be resolved before the effective use of UAVs can be made, including security, privacy, and management. As such, in this paper we review new UAV application areas enabled by the IoT and 5G technologies, analyze the sensor requirements, and overview solutions for fleet management over aerial-networking, privacy, and security challenges. Finally, we propose a framework that supports and enables these technologies on UAVs. The introduced framework provisions a holistic IoT architecture that enables the protection of UAVs as “flying” things in a collaborative networked environment.
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Tipantuña, Christian, Xavier Hesselbach, Victor Sánchez-Aguero, Francisco Valera, Ivan Vidal, and Borja Nogales. "An NFV-Based Energy Scheduling Algorithm for a 5G Enabled Fleet of Programmable Unmanned Aerial Vehicles." Wireless Communications and Mobile Computing 2019 (February 20, 2019): 1–20. http://dx.doi.org/10.1155/2019/4734821.
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The fifth generation of mobile networks (5G) is expected to provide diverse and stringent improvements such as greater connectivity, bandwidth, throughput, availability, improved coverage, and lower latency. Considering this, drones or Unmanned Aerial Vehicles (UAVs) and Internet of Things (IoT) devices are perfect examples of existing technology that can take advantage of the capabilities provided by 5G technology. In particular, UAVs are expected to be an important component of 5G networks implementations and support different communication requirements and applications. UAVs working together with 5G can potentially facilitate the deployment of standalone or complementary communications infrastructures, and, due to its rapid deployment, these solutions are suitable candidates to provide network services in emergency scenarios, natural disasters, and search and rescue missions. An important consideration in the deployment of a programmable drone fleet is to guarantee the reliability and performance of the services through consistent monitoring, control, and management scheme. In this regard, the Network Functions Virtualization (NFV) paradigm, a key technology within the 5G ecosystem, can be used to perform automation, management, and orchestration tasks. In addition, to ensure the coordination and reliability in the communications systems, considering that the UAVs have a finite lifetime and that eventually they must be replaced, a scheduling scheme is needed to guarantee the availability of services and efficient resource utilization. To this end, in this paper is presented an UAV scheduling scheme which leverages the potential offered by NFV. The proposed strategy, based on a brute-force search combinatorial algorithm, allows obtaining the optimal scheduling of UAVs in time, in order to efficiently deploy network services. Simulation results validate the performance of the proposed strategy, by providing the number of drones needed to meet certain levels of service availability. Furthermore, the strategy allows knowing the sequence of replacement of UAVs to ensure the optimal resource utilization.
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Su, Wenjia, Min Gao, Xinbao Gao, and Zhaolong Xuan. "Enhanced Multi-UAV Path Planning in Complex Environments With Voronoi-Based Obstacle Modelling and Q-Learning." International Journal of Aerospace Engineering 2024 (May 27, 2024): 1–14. http://dx.doi.org/10.1155/2024/5114696.
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To tackle the challenge of obstacle avoidance path planning for multiple unmanned aerial vehicles (UAVs) in intricate environments, this study introduces a Voronoi graph–based model to represent the obstacle-laden environment and employs a Markov decision process (MDP) for single UAV path planning. The traditional Q-learning algorithm is enhanced by adjusting the initial state of the Q-table and fine-tuning the reward and penalty values, enabling the acquisition of efficient obstacle avoidance paths for individual UAVs in complex settings. Leveraging the improved Q-learning algorithm for single UAVs, the Q-table is iteratively refined for a fleet of UAVs, with dynamic modifications based on the waypoints chosen by each UAV. This approach ensures the generation of collision-free paths for multiple UAVs, as validated by simulation results that showcase the algorithm’s effectiveness in learning from past training data. The proposed method offers a robust framework for practical UAV trajectory generation in complex environments.
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Lissandrini, Nicola, Giulia Michieletto, Riccardo Antonello, Marta Galvan, Alberto Franco, and Angelo Cenedese. "Cooperative Optimization of UAVs Formation Visual Tracking." Robotics 8, no. 3 (July 7, 2019): 52. http://dx.doi.org/10.3390/robotics8030052.
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The use of unmanned vehicles to perform tiring, hazardous, repetitive tasks, is becoming a reality out of the academy laboratories, getting more and more interest for several application fields from the industrial, to the civil, to the military contexts. In particular, these technologies appear quite promising when they employ several low-cost resource-constrained vehicles leveraging their coordination to perform complex tasks with efficiency, flexibility, and adaptation that are superior to those of a single agent (even if more instrumented). In this work, we study one of said applications, namely the visual tracking of an evader (target) by means of a fleet of autonomous aerial vehicles, with the specific aim of focusing on the target so as to perform an accurate position estimation while concurrently allowing a wide coverage over the monitored area so as to limit the probability of losing the target itself. These clearly conflicting objectives call for an optimization approach that is here developed: by considering both aforementioned aspects and the cooperative capabilities of the fleet, the designed algorithm allows controling in real time the single fields of view so as to counteract evasion maneuvers and maximize an overall performance index. The proposed strategy is discussed and finally assessed through the realistic Gazebo-ROS simulation framework.
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Felli, Lorenzo, Romeo Giuliano, Andrea De Negri, Francesco Terlizzi, Franco Mazzenga, and Alessandro Vizzarri. "Maximal LoRa Range for Unmanned Aerial Vehicle Fleet Service in Different Environmental Conditions." IoT 5, no. 3 (July 31, 2024): 509–23. http://dx.doi.org/10.3390/iot5030023.
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This study investigates communication between UAVs using long range (LoRa) devices, focusing on the interaction between a LoRa gateway UAV and other UAVs equipped with LoRa transmitters. By conducting experiments across various geographical regions, this study aims to delineate the fundamental boundary conditions for the efficient control of a UAV fleet. The parameters under analysis encompass inter-device spacing, radio interference effects, and terrain topography. This research yields pivotal insights into communication network design and optimization, thereby enhancing operational efficiency and safety within diverse geographical contexts for UAV operations. Further research insights could involve a weather analysis and implementation of improved solutions in terms of communication systems.
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Almeida, M., H. Hildmann, and G. Solmaz. "DISTRIBUTED UAV-SWARM-BASED REAL-TIME GEOMATIC DATA COLLECTION UNDER DYNAMICALLY CHANGING RESOLUTION REQUIREMENTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6 (August 23, 2017): 5–12. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w6-5-2017.
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Unmanned Aerial Vehicles (UAVs) have been used for reconnaissance and surveillance missions as far back as the Vietnam War, but with the recent rapid increase in autonomy, precision and performance capabilities – and due to the massive reduction in cost and size – UAVs have become pervasive products, available and affordable for the general public. The use cases for UAVs are in the areas of disaster recovery, environmental mapping & protection and increasingly also as extended eyes and ears of civil security forces such as fire-fighters and emergency response units. In this paper we present a swarm algorithm that enables a fleet of autonomous UAVs to collectively perform sensing tasks related to environmental and rescue operations and to dynamically adapt to e.g. changing resolution requirements. We discuss the hardware used to build our own drones and the settings under which we validate the proposed approach.
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Zemlianko, H., and V. Kharchenko. "Cyber Security Systems of Highly Functional Uav Fleets for Monitoring Critical Infrastructure: Analysis of Disruptions, Attacks and Counterapproaches." Èlektronnoe modelirovanie 46, no. 1 (February 10, 2024): 41–54. http://dx.doi.org/10.15407/emodel.46.01.041.
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The modern world is becoming increasingly dependent on the security of critical infrastructure facilities (CIF), which is monitored by UAVs, their fleets and multifunctional fleet systems (MFS UAVs). The UAV MFS have a complex digital infrastructure (DIS). The DIS is based on new information technologies that have certain security deficiencies and create new cyber threats, in particular, due to specific vulnerabilities that can be exploited externally. The provi-sion of cyber security of the CIS of the MBF of UAVs has been studied thanks to the develop-ment of a sequence of analysis of cyber threats using the IMECA procedure. An overview of existing cyber security assessment methods and their limitations was conducted; developed models of the OKI monitoring system based on the UAV MBF; analyzed cyber threats to its CIS; the criticality of cyber attacks and the impact of countermeasures; formulated recommen-dations for ensuring cyber security and general conclusions based on research results. A method of ensuring cyber security of the CIS of the MBF UAV was created, which consists of determin-ing its specific features as an object of cyber threats, analyzing violators, vulnerabilities, risks of critical violations and choosing countermeasures, the use of which allows you to increase the level of cyber security and reliability of the monitoring system and ensure a temporary response to cyber threats.
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Bousbaa, Fatima Zohra, Chaker Abdelaziz Kerrache, Zohra Mahi, Abdou El Karim Tahari, Nasreddine Lagraa, and Mohamed Bachir Yagoubi. "GeoUAVs: A new geocast routing protocol for fleet of UAVs." Computer Communications 149 (January 2020): 259–69. http://dx.doi.org/10.1016/j.comcom.2019.10.026.
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Calamoneri, Tiziana, Federico Corò, and Simona Mancini. "Autonomous data detection and inspection with a fleet of UAVs." Computers & Operations Research 168 (August 2024): 106678. http://dx.doi.org/10.1016/j.cor.2024.106678.
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Kabashkin, Igor. "Availability of Services in Wireless Sensor Network with Aerial Base Station Placement." Journal of Sensor and Actuator Networks 12, no. 3 (May 8, 2023): 39. http://dx.doi.org/10.3390/jsan12030039.
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Internet of Things technologies use many sensors combined with wireless networks for cyber-physical systems in various applications. Mobility is an essential characteristic for many objects that use sensors. In mobile sensor networks, the availability of communication channels is crucial, especially for mission-critical applications. This article presents models for analyzing the availability of sensor services in a wireless network with aerial base station placement (ABSP), considering the real conditions for using unmanned aerial vehicles (UAVs). The studied system uses a UAV-assisted mobile edge computing architecture, including ABSP and a ground station for restoring the energy capacity of the UAVs, to maintain the availability of interaction with the sensors. The architecture includes a fleet of additional replacement UAVs to ensure continuous communication coverage for the sensor network during the charging period of the air-based station UAVs. Analytical expressions were obtained to determine the availability of sensor services in the system studied.
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Bemposta Rosende, Sergio, Javier Sánchez-Soriano, Carlos Quiterio Gómez Muñoz, and Javier Fernández Andrés. "Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants." Energies 13, no. 21 (November 1, 2020): 5712. http://dx.doi.org/10.3390/en13215712.
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This article presents a remote management architecture of an unmanned aerial vehicles (UAVs) fleet to aid in the management of solar power plants and object tracking. The proposed system is a competitive advantage for sola r energy production plants, due to the reduction in costs for maintenance, surveillance, and security tasks, especially in large solar farms. This new approach consists of creating a hardware and software architecture that allows for performing different tasks automatically, as well as remotely using fleets of UAVs. The entire system, composed of the aircraft, the servers, communication networks, and the processing center, as well as the interfaces for accessing the services via the web, has been designed for this specific purpose. Image processing and automated remote control of the UAV allow generating autonomous missions for the inspection of defects in solar panels, saving costs compared to traditional manual inspection. Another application of this architecture related to security is the detection and tracking of pedestrians and vehicles, both for road safety and for surveillance and security issues of solar plants. The novelty of this system with respect to current systems is summarized in that all the software and hardware elements that allow the inspection of solar panels, surveillance, and people counting, as well as traffic management tasks, have been defined and detailed. The modular system presented allows the exchange of different specific vision modules for each task to be carried out. Finally, unlike other systems, calibrated fixed cameras are used in addition to the cameras embedded in the drones of the fleet, which complement the system with vision algorithms based on deep learning for identification, surveillance, and inspection.
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Alhaqbani, Amjaad, Heba Kurdi, and Kamal Youcef-Toumi. "Fish-Inspired Task Allocation Algorithm for Multiple Unmanned Aerial Vehicles in Search and Rescue Missions." Remote Sensing 13, no. 1 (December 23, 2020): 27. http://dx.doi.org/10.3390/rs13010027.
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The challenge concerning the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) has significantly spurred research interest due to its contribution to the success of various fleet missions. This challenge becomes more complex in time-constrained missions, particularly if they are conducted in hostile environments, such as search and rescue (SAR) missions. In this study, a novel fish-inspired algorithm for multi-UAV missions (FIAM) for task allocation is proposed, which was inspired by the adaptive schooling and foraging behaviors of fish. FIAM shows that UAVs in an SAR mission can be similarly programmed to aggregate in groups to swiftly survey disaster areas and rescue-discovered survivors. FIAM’s performance was compared with three long-standing multi-UAV task allocation (MUTA) paradigms, namely, opportunistic task allocation scheme (OTA), auction-based scheme, and ant-colony optimization (ACO). Furthermore, the proposed algorithm was also compared with the recently proposed locust-inspired algorithm for MUTA problem (LIAM). The experimental results demonstrated FIAM’s abilities to maintain a steady running time and a decreasing mean rescue time with a substantially increasing percentage of rescued survivors. For instance, FIAM successfully rescued 100% of the survivors with merely 16 UAVs, for scenarios of no more than eight survivors, whereas LIAM, Auction, ACO and OTA rescued a maximum of 75%, 50%, 35% and 35%, respectively, for the same scenarios. This superiority of FIAM performance was maintained under a different fleet size and number of survivors, demonstrating the approach’s flexibility and scalability.
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DEWMINI, Janani, W. Madushan FERNANDO, Izabela Iwa NIELSEN, Grzegorz BOCEWICZ, Amila THIBBOTUWAWA, and Zbigniew BANASZAK. "IDENTIFYING THE POTENTIAL OF UNMANNED AERIAL VEHICLE ROUTING FOR BLOOD DISTRIBUTION IN EMERGENCY REQUESTS." Applied Computer Science 19, no. 4 (December 31, 2023): 68–87. http://dx.doi.org/10.35784/acs-2023-36.
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This study is focusing on identifying the potential of Unmanned Aerial Vehicle (UAV) routing for blood distribution in emergency requests in Sri Lanka compared to existing transportation modes. Capacitated Unmanned Aerial Vehicle Routing Problem was used as the methodology to find the optimal distribution plan between blood banks directing emergency requests. The developed UAV routing model was tested for different instances to compare the results. Finally, the proposed distribution process via UAVs was compared with the current distribution process for the objective function set up in the model and other Key Performance Indicators (KPIs) including energy consumption savings and operational cost savings. The average percentage of distribution time re-duction, energy consumption cost reduction, and operational cost per day reduction utilizing UAVs were determined to be 58.57%, 96.35%, and 61.20%, respectively, for the instances tested using the model highlighting the potential of UAVs. Therefore, the deficiencies in Sri Lanka's present blood delivery system can be addressed using UAVs' potential for time, cost, and energy savings. The ability to save time through the deployment of UAVs to the fleet during emergency situations plays a crucial role in preventing the loss of human lives.
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Bono, Antonio, Luigi D’Alfonso, Giuseppe Fedele, Anselmo Filice, and Enrico Natalizio. "Path Planning and Control of a UAV Fleet in Bridge Management Systems." Remote Sensing 14, no. 8 (April 12, 2022): 1858. http://dx.doi.org/10.3390/rs14081858.
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Traditional methodologies for precise inspection of bridges (pavement, beams, column cap, column, joints and inside box girder, etc.) with By-bridge equipment, Aerial Work Platform (AWP) or via ropes have several limits that can be overcome by using Unmanned Aerial Vehicles (UAVs). The constant development in this field allows us to go beyond the manual control and the use of a single UAV. In the context of inspection rules, this research provides new inputs to the multilevel approach used today and to the methods of structural inspection with drones. Today, UAV-based inspections are limited by manual and/or semi-automatic control with many restrictions on trajectory settings, especially for areas of difficult access with Global Navigation Satellite Systems (GNSS) denied that still require the intervention of a human operator. This work proposes the use of autonomous navigation with a fleet of UAVs for infrastructural inspections. Starting from a digital twin, a solution is provided to problems such as the definition of a set of reference trajectories and the design of a position controller. A workflow to integrate a generic Bridge Management System (BMS) with this type of approach is provided.
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Ibenthal, Julius, Michel Kieffer, Luc Meyer, Hélène Piet-Lahanier, and Sébastien Reynaud. "Bounded-error target localization and tracking using a fleet of UAVs." Automatica 132 (October 2021): 109809. http://dx.doi.org/10.1016/j.automatica.2021.109809.
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Feng, Yi, Cong Zhang, Stanley Baek, Samir Rawashdeh, and Alireza Mohammadi. "Autonomous Landing of a UAV on a Moving Platform Using Model Predictive Control." Drones 2, no. 4 (October 12, 2018): 34. http://dx.doi.org/10.3390/drones2040034.
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Developing methods for autonomous landing of an unmanned aerial vehicle (UAV) on a mobile platform has been an active area of research over the past decade, as it offers an attractive solution for cases where rapid deployment and recovery of a fleet of UAVs, continuous flight tasks, extended operational ranges, and mobile recharging stations are desired. In this work, we present a new autonomous landing method that can be implemented on micro UAVs that require high-bandwidth feedback control loops for safe landing under various uncertainties and wind disturbances. We present our system architecture, including dynamic modeling of the UAV with a gimbaled camera, implementation of a Kalman filter for optimal localization of the mobile platform, and development of model predictive control (MPC), for guidance of UAVs. We demonstrate autonomous landing with an error of less than 37 cm from the center of a mobile platform traveling at a speed of up to 12 m/s under the condition of noisy measurements and wind disturbances.
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Pham, Thiem V., and Thanh Dong Nguyen. "Path-Following Formation of Fixed-Wing UAVs under Communication Delay: A Vector Field Approach." Drones 8, no. 6 (June 2, 2024): 237. http://dx.doi.org/10.3390/drones8060237.
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In many applications, such as atmospheric observation or disaster monitoring, cooperative control of a fleet of UAVs is crucial because it is effective in repeated tasks. In this work, we provide a workable and useful cooperative guiding algorithm for several fixed-wing UAVs to construct a path-following formation with communication delays. The two primary components of our concept are path-following (lateral guidance) and path formation (longitudinal guidance). The former is in charge of ensuring that, in the presence of wind disturbance, the lateral distance between the UAV and its targeted path converges using a well-known vector field technique. In the event of a communication delay, the latter ensures that several fixed-wing UAVs will create a predetermined formation shape. Furthermore, we provide a maximum delay bound that is dependent on the topology and a controller’s gain. Lastly, in order to confirm the viability and advantages of our suggested approach, we construct an effective platform for a hardware-in-the-loop (HIL) test.
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Alvear, Oscar, Nicola Roberto Zema, Enrico Natalizio, and Carlos T. Calafate. "Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility." Journal of Advanced Transportation 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/8204353.
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Air pollution monitoring has recently become an issue of utmost importance in our society. Despite the fact that crowdsensing approaches could be an adequate solution for urban areas, they cannot be implemented in rural environments. Instead, deploying a fleet of UAVs could be considered an acceptable alternative. Embracing this approach, this paper proposes the use of UAVs equipped with off-the-shelf sensors to perform air pollution monitoring tasks. These UAVs are guided by our proposed Pollution-driven UAV Control (PdUC) algorithm, which is based on a chemotaxis metaheuristic and a local particle swarm optimization strategy. Together, they allow automatically performing the monitoring of a specified area using UAVs. Experimental results show that, when using PdUC, an implicit priority guides the construction of pollution maps by focusing on areas where the pollutants’ concentration is higher. This way, accurate maps can be constructed in a faster manner when compared to other strategies. The PdUC scheme is compared against various standard mobility models through simulation, showing that it achieves better performance. In particular, it is able to find the most polluted areas with more accuracy and provides a higher coverage within the time bounds defined by the UAV flight time.
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Radzki, Grzegorz, Izabela Nielsen, Paulina Golińska-Dawson, Grzegorz Bocewicz, and Zbigniew Banaszak. "Reactive UAV Fleet’s Mission Planning in Highly Dynamic and Unpredictable Environments." Sustainability 13, no. 9 (May 7, 2021): 5228. http://dx.doi.org/10.3390/su13095228.
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Unmanned aerial vehicles (UAVs) create an interesting alternative for establishing more sustainable urban freight deliveries. The substitution of traditional trucks in the last-mile distribution by a UAV fleet can contribute to urban sustainability by reducing air pollution and increasing urban freight efficiency. This paper presents a novel approach to the joint proactive and reactive planning of deliveries by a UAV fleet. We develop a receding horizon-based approach to reactive, online planning for the UAV fleet’s mission. We considered the delivery of goods to spatially dispersed customers over an assumed time horizon. Forecasted weather changes affect the energy consumption of UAVs and limit their range. Therefore, consideration should be given to plans for follow-up tasks, previously unmet needs, and predictions of disturbances over a moving time horizon. We propose a set of reaction rules that can be encountered during delivery in a highly dynamic and unpredictable environment. We implement a constraint programming paradigm, which is well suited to cope with the nonlinearity of the system’s characteristics. The proposed approach to online reactive UAV routing is evaluated in several instances. The computational experiments have shown that the developed model is capable of providing feasible plans for a UAV fleet’s mission that are robust to changes in weather and customer’s orders.
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Sun, Xiaolei, Naiming Qi, and Weiran Yao. "Boolean Networks-Based Auction Algorithm for Task Assignment of Multiple UAVs." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/425356.
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This paper presents an application of Boolean networks-based auction algorithm (BNAA) for task assignment in unmanned aerial vehicles (UAVs) systems. Under reasonable assumptions, the assignment framework consists of mission control system, communication network, and ground control station. As the improved algorithm of consensus-based bundle algorithm (CBBA), the BNAA utilizes a cluster-based combinatorial auction policy to handle multiple tasks. Instead of empirical method based on look-up table about conditional variables, Boolean network is introduced into consensus routine of BNAA for solving the conflict of assignment across the fleet of UAVs. As a new mathematic theory, semitensor product provides the implementation and theoretical proof of Boolean networks. Numerical results demonstrate the effectiveness and efficiency of proposed BNAA method.
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Hassan, Zohaib, Syed Irtiza Ali Shah, and Ahsan Sarwar Rana. "Charging Station Distribution Optimization Using Drone Fleet in a Disaster." Journal of Robotics 2022 (July 31, 2022): 1–13. http://dx.doi.org/10.1155/2022/7329346.
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A disaster is an unforeseen calamity that can cause damage to properties and can bring about a loss of human lives. Usually, many relief supplies, such as clean water, food, and medical supplies, are required by disaster victims. Quick response and rapid distribution of essential relief items into the affected region can save countless lives and prevent or slow down the effects of disasters. In this regard, disaster management comes into play, which is highly dependent on the topography and access of the disaster-hit area. If the disaster-hit site has little or no road connectivity, the use of UAVs/drones becomes essential in delivering health packages to the affected areas to assist with humanitarian aid. Since the battery capacity of the drone is limited, UAVs/drones require charging stations located at various places to carry out the necessary relief work. These charging stations should be transported using road infrastructure and preinstalled in disaster-prone areas, as access to these areas may be denied once the disaster hits. This article presents a novel optimization model to distribute relief items to disaster-hit areas. The objective of this model is to optimize the location and the number of the charging stations. We consider the relative priority of locations where a preference is given to locations with higher priority levels. The optimal number of charging stations and optimal routes has also been determined by using our optimization model. To illustrate the use of our model, numerical examples have been simulated for a different number of targets. Through our numerical simulation, it was observed that the drone’s maximum distance capacity is the key factor in determining the optimal grid size, which directly correlates to the number of charging stations.
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Loroch, Leszek, and Andrzej Żyluk. "New Technologies for Air Traffic Security." Journal of Konbin 7, no. 4 (January 1, 2008): 95–112. http://dx.doi.org/10.2478/v10040-008-0081-z.
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New Technologies for Air Traffic Security Security of increasing intensity of air traffic requires significant technological support. In Europe, the dynamics of this phenomenon will be additionally stimulated by implementation of unmanned aerial vehicles (UAVs) into the air traffic. For effective operation of aircraft fleet it is necessary to employ new on-board diagnostic devices and flight recorders in order to evaluate technical condition of aircraft's instrumentation. Reducing the pilots' workload requires the development of new integrated digital avionics. On the other hand, in order to make the air traffic more secure, it is necessary to develop "sense and avoid" systems not only for UAVs, but for piloted aircraft, as well. On-ground support requires the effective airport/airfield protection and keeping airfield pavements well-maintained and safe.
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Perez-Montenegro, Carlos, Matteo Scanavino, Nicoletta Bloise, Elisa Capello, Giorgio Guglieri, and Alessandro Rizzo. "A Mission Coordinator Approach for a Fleet of UAVs in Urban Scenarios." Transportation Research Procedia 35 (2018): 110–19. http://dx.doi.org/10.1016/j.trpro.2018.12.018.
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El Ferik, Sami, and Olapido Raphael Thompson. "Biologically Inspired Control Of A Fleet Of Uavs With Threat Evasion Strategy." Asian Journal of Control 18, no. 6 (May 30, 2016): 2283–300. http://dx.doi.org/10.1002/asjc.1324.
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Zhou, Jinlun, Honghai Zhang, Mingzhuang Hua, Fei Wang, and Jia Yi. "P-DRL: A Framework for Multi-UAVs Dynamic Formation Control under Operational Uncertainty and Unknown Environment." Drones 8, no. 9 (September 10, 2024): 475. http://dx.doi.org/10.3390/drones8090475.
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Unmanned aerial vehicle (UAV) formation flying is an efficient and economical operation mode for air transportation systems. To improve the effectiveness of synergetic formation control for UAVs, this paper proposes a pairwise conflict resolution approach for UAV formation through mathematical analysis and designs a dynamic pairing and deep reinforcement learning framework (P-DRL formation control framework). Firstly, a new pairwise UAV formation control theorem is proposed, which breaks down the multi-UAVs formation control problem into multiple sequential control problems involving UAV pairs through a dynamic pairing algorithm. The training difficulty of Agents that only control each pair (two UAVs) is lower compared to controlling all UAVs directly, resulting in better and more stable formation control performance. Then, a deep reinforcement learning model for a UAV pair based on the Environment–Agent interaction is built, where segmented reward functions are designed to reduce the collision possibility of UAVs. Finally, P-DRL completes the formation control task of the UAV fleet through continuous pairing and Agent-based pairwise formation control. The simulations used the dynamic pairing algorithm combined with the DRL architectures of asynchronous advantage actor–critic (P-A3C), actor–critic (P-AC), and double deep q-value network (P-DDQN) to achieve synergetic formation control. This approach yielded effective control results with a strong generalization ability. The success rate of controlling dense, fast, and multi-UAV (10–20) formations reached 96.3%, with good real-time performance (17.14 Hz).
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Grzegorz, Radzki, Bocewicz Grzegorz, Dybala Bogdan, and Banaszak Zbigniew. "Reactive Planning-Driven Approach to Online UAVs Mission Rerouting and Rescheduling." Applied Sciences 11, no. 19 (September 24, 2021): 8898. http://dx.doi.org/10.3390/app11198898.
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The presented problem concerns the route planning of a UAV fleet carrying out deliveries to spatially dispersed customers in a highly dynamic and unpredictable environment within a specified timeframe. The developed model allows for predictive (i.e., taking into account forecasted changing weather conditions) and reactive (i.e., enabling contingency UAVs rerouting) delivery mission planning (i.e., NP-hard problem) in terms of the constraint satisfaction problem. Due to the need to implement an emergency return of the UAV to the base or handling ad hoc ordered deliveries, sufficient conditions have been developed. Checking that these conditions are met allows cases to be eliminated if they do not guarantee acceptable solutions, thereby allowing the calculations to be sped up. The experiments carried out showed the usefulness of the proposed approach in DSS-based contingency planning of the UAVs’ mission performed in a dynamic environment.
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Cordeiro, Thiago F. K., João Y. Ishihara, and Henrique C. Ferreira. "A Decentralized Low-Chattering Sliding Mode Formation Flight Controller for a Swarm of UAVs." Sensors 20, no. 11 (May 30, 2020): 3094. http://dx.doi.org/10.3390/s20113094.
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In this paper, a nonlinear robust formation flight controller for a swarm of unmanned aerial vehicles (UAVs) is presented. It is based on the virtual leader approach and is capable of achieving and maintaining a formation with time-varying shape. By using a decentralized architecture, the local controller in each UAV uses information only from the UAV itself, its neighbors, and from the virtual leader. Also, a synchronization control objective provides a mechanism to weight between the fleet achieving the desired formation shape, that is, achieving the desired relative position between the UAVs, and each UAV achieving its desired absolute position. The use of a combination of a sliding mode controller and a low pass filter reduces the usual chattering effect, providing a smooth control signal while maintaining robustness. Simulation results show the effectiveness of the proposed decentralized controller.
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Htiouech, Skander, Khalil Chebil, Mahdi Khemakhem, Fidaa Abed, and Monaji H. Alkiani. "An Extended Model for the UAVs-Assisted Multiperiodic Crowd Tracking Problem." Complexity 2023 (February 1, 2023): 1–14. http://dx.doi.org/10.1155/2023/3001812.
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The multiperiodic crowd tracking (MPCT) problem is an extension of the periodic crowd tracking (PCT) problem, recently addressed in the literature and solved using an iterative solver called PCTs solver. For a given crowded event, the MPCT consists of follow-up crowds, using unmanned aerial vehicles (UAVs) during different periods in a life-cycle of an open crowded area (OCA). Our main motivation is to remedy an important limitation of the PCTs solver called “PCTs solver myopia” which is, in certain cases, unable to manage the fleet of UAVs to cover all the periods of a given OCA life-cycle during a crowded event. The behavior of crowds can be predicted using machine learning techniques. Based on this assumption, we proposed a new mixed integer linear programming (MILP) model, called MILP-MPCT, to solve the MPCT. The MILP-MPCT was designed using linear programming technique to build two objective functions that minimize the total time and energy consumed by UAVs under a set of constraints related to the MPCT problem. In order to validate the MILP-MPCT, we simulated it using IBM-ILOG-CPLEX optimization framework. Thanks to the “clairvoyance” of the proposed MILP-MPCT model, experimental investigations show that the MILP-MPCT model provides strategic moves of UAVs between charging stations (CSs) and crowds to provide better solutions than those reported in the literature.
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Zhuo, Ran, Shiqian Song, and Yejun Xu. "UAV Communication Network Modeling and Energy Consumption Optimization Based on Routing Algorithm." Computational and Mathematical Methods in Medicine 2022 (June 28, 2022): 1–10. http://dx.doi.org/10.1155/2022/4782850.
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Topological information is provided, and research on the design of routing protocols for UAV self-assembling networks is conducted, in order to enable fleet communication transfer between UAVs and UAVs and enhance their communication transmission rate in the self-assembling network. A new routing protocol is proposed through greedy forwarding and peripheral forwarding of UAV self-assembling network communication data, UAV self-assembling network planarization processing, dynamic adjustment of routing mode based on topological information, and routing protocol decision content generation. The proposed network is described using stochastic geometry theory, with the UAV and building locations modeled as two independently distributed Poisson point processes and the building shape modeled as a rectangular body with height obeying the Rayleigh distribution. An estimated equation for typical user coverage is produced using this model. The simulation results show that the approximate expression matches with the simulation results with reduced computational complexity, which verifies the validity of the approximate analysis. By comparing it with the clustering-based routing protocol, it is concluded that the new routing protocol conditions for UAV self-assembly network can realize the communication transmission between UAVs and drones and further promote their communication transmission rate.
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Erdelj, Milan, Borey Uk, David Konam, and Enrico Natalizio. "From the Eye of the Storm: An IoT Ecosystem Made of Sensors, Smartphones and UAVs." Sensors 18, no. 11 (November 7, 2018): 3814. http://dx.doi.org/10.3390/s18113814.
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The development of Unmanned Aerial Vehicles (UAV) along with the ubiquity of Internet of Things (IoT) enables the creation of systems that, leveraging 5G enhancements, can provide real-time multimedia communications and data streaming. However, the usage of the UAVs introduces new constraints, such as unstable network communications and security pitfalls. In this work, the experience of implementing a system architecture for data and multimedia transmission using a multi-UAV system is presented. The system aims at creating an IoT ecosystem to bridge UAVs and other types of devices, such as smartphones and sensors, while coping with the fallback in an unstable communication environment. Furthermore, this work proposes a detailed description of a system architecture designed for remote drone fleet control. The proposed system provides an efficient, reliable and secure system for multi-UAV remote control that will offer the on-demand usage of available sensors, smartphones and unmanned vehicle infrastructure.
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Grünblatt, Rémy, Isabelle Guérin Lassous, and Olivier Simonin. "A distributed antenna orientation solution for optimizing communications in a fleet of UAVs." Computer Communications 181 (January 2022): 102–15. http://dx.doi.org/10.1016/j.comcom.2021.09.020.
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Belkadi, Adel, Hernan Abaunza, Laurent Ciarletta, Pedro Castillo, and Didier Theilliol. "Design and Implementation of Distributed Path Planning Algorithm for a Fleet of UAVs." IEEE Transactions on Aerospace and Electronic Systems 55, no. 6 (December 2019): 2647–57. http://dx.doi.org/10.1109/taes.2019.2906437.
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RADZKI, Grzegorz, Amila THIBBOTUWAWA, and Grzegorz BOCEWICZ. "UAVS FLIGHT ROUTES OPTIMIZATION IN CHANGING WEATHER CONDITIONS – CONSTRAINT PROGRAMMING APPROACH." Applied Computer Science 15, no. 3 (September 30, 2019): 5–20. http://dx.doi.org/10.35784/acs-2019-17.
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The problem of delivering goods in a distribution network is considered in which a fleet of Unmanned Aerial Vehicles (UAV) carries out transport operations. The changing weather conditions in which the transport operations take place and the UAVs energy capacity levels influenced by the weather conditions are taken into account as factors that affect the determination of a collision-free route. The goods must be delivered to the customers in a given time window. Establishing the routes are the focus of this study. Solutions maximizing the level of customer satisfaction are focused and the computational experiments presented in the study show the impact of weather conditions on route determination.