Group Of Unmanned Aerial Vehicles Research Articles

Utilizing Deep Reinforcement Learning to Control UAV Movement for Environmental Monitoring

Unmanned aerial vehicles (UAVs) are increasingly used in various applications, including infrastructure inspection, traffic monitoring, remote sensing, mapping, and rescue. However, many applications have required UAVs to function autonomously, without human intervention to improve system performance. In this study, we propose a new approach to environmental monitoring using a group of UAVs equipped with sensors under the support of reinforcement learning. Regarding the communication system model, we assume that UAVs can cooperate with each other to learn and share information about the environment, and then relocate to an optimal position while managing connectivity and coverage. After that, we exploit reinforcement learning with a deep deterministic policy gradient (DDPG) algorithm to optimize environmental monitoring with the proposed algorithm. Specifically, the proposed algorithm aims to simulate an environmental monitoring system using UAVs with basic parameters. We further apply the proposed algorithm to evaluate network performance under different parameter settings. Numerical results validate the effectiveness of the proposed learning-based framework in monitoring and sensing data.

PROSPECTS FOR THE JOINT USE OF UNMANNED AERIAL VEHICLES

In the article, the development of methods of combat use of unmanned aerial vehicles (UAVs) from the point of view of cause and effect processes in the system "UAVs - means of enemy air defense - purposes of combat mission" is considered by the authors. The main factors of development and changes in the methods of combat use of UAVs are analyzed. The basic patterns and connections of the process of development of methods of joint use of UAVs, the area of conditions for the expedient use of groups of UAVs are highlighted. The scientific problem of the stochastic synthesis of the automated control system for the group application of UAVs, as one of the problems of modern control theory and technical cybernetics, is substantiated and formalized. The question of the possibility of reducing the task of stochastic synthesis of the automated control system for the group application of UAVs to the set of control optimization tasks in linear (linearized) systems according to quadratic criteria is considered.

АППАРАТНО-АЛГОРИТМИЧЕСКОЕ ОБЕСПЕЧЕНИЕ ПЕРСПЕКТИВНОЙ СИСТЕМЫ ЭНЕРГОСБЕРЕЖЕНИЯ АВТОНОМНОЙ ГРУППЫ БПЛА

Due to the accelerated growth in the use of groups of autonomously functioning unmanned aerialvehicles (UAVs) in various environments, solving the problem of optimizing the functioning of groups ofsuch vehicles according to the criterion of the minimum energy consumed is an urgent scientific task. Inthis article, a new approach is being developed to ensure energy saving of a group of unmanned aerialvehicles (UAVs) by using UAV charging stations that provide the necessary versatility in servicing differenttypes of vehicles. The most effective variants of landing systems, precise positioning, power generationat the station are considered, and a hybrid charging system combining contact and contactless methods is also justified. A generalized scheme of multi-stage interaction of the UAV with the chargingstation is given, which provides for the possibility of repeating one of the stages if it is not fully completedwithin certain time intervals, and also takes into account the peculiarities of interaction betweenagents through the communication channels available to them. The problem of optimal distributionaccording to the energy criterion of the group's UAVs between destinations (PN) combined with chargingstations (SP) has been set and solved. Both the cruising mode and the maneuvering of the vehicleduring takeoff and landing, when the power consumption increases, are taken into account. The conceptof the effective distance to the destination is introduced, taking into account the estimated energy costsof each UAV to reach this destination, taking into account its arbitrary current position and the availablequeue of tasks at the moment. To study the developed approaches and algorithms for targeting andplanning the movement of the group's UAVs, software based on the Unity environment was created andtested. The flexibility of the latter allows modeling various algorithms of information interactionsof elements within a group of UAVs, a group of charging stations, as well as cross-interactionsbetween UAVs and charging stations. In particular, the software allows you to determine at eachdiscrete moment the degree of charge of each UAV, the queues of destinations for each UAV, itshistory of recharge at stations.

Collision‐free formation generation and tracking of unmanned aerial vehicles based on physicomimetics approach

AbstractThis paper investigates the problem of collision‐free leader–follower formation generation and tracking of multiple fixed‐wing unmanned aerial vehicles (UAVs). A group of UAVs, described by unicycle‐type models subject to velocity constraints, are required to form a desired formation, while tracking a virtual leader and achieving collision‐free flight. To handle this problem, a novel control law based on physicomimetics approach is proposed, which integrates the formation generation, formation tracking, and collision avoidance together. Physical forces are imitated to design artificial forces used in control laws that drive multiple UAVs to accomplish desired collaborative behaviors. Further, the virtual repulsion is embedded in the physicomimetics‐based control scheme to achieve obstacle avoidance naturally. The artificial forces have similar meaning to the physical forces because of similar forms, which facilitates the design and adjustment of the control strategy. Specially, to deal with the speed constraints of fixed‐wing UAVs, a saturation function is applied to modify the control laws and the stability is proved theoretically. Finally, numerical simulations and hardware‐in‐the‐loop experiments are provided to verify the effectiveness of the proposed control scheme.

Evolutionary Task Allocation and Cooperative Control of Unmanned Aerial Vehicles in Air Combat Applications

Autonomous air combat of unmanned aerial vehicles (UAVs) is a typical and difficult problem that must be studied in the field of military intelligence. The UAVs are required to work together to accomplish different tasks in the air combat. In this note, both task allocation and cooperative control of UAVs are studied for air combat applications. A two-layer framework of the air combat is established containing a task allocation upper layer and a cooperative control lower layer. In the task allocation upper layer, an evolutionary task allocation strategy is proposed for UAVs to allocate tasks by considering their cooperative behavior. After the task allocation, the UAVs, which are allocated for the same task, form the same group. In the cooperative control lower layer, a cooperative control approach is proposed for groups of UAVs to maintain a desired formation to accomplish the corresponding task. The evolutionary stable state and stabilization of the UAVs are achieved. Finally, both Software-in-the-Loop and Model-in-the-Loop simulations are conducted based on the RflySim platform to demonstrate the effectiveness of the proposed approach and further display the multi-UAV air combat vision realistically.

Hybrid swarm intelligent algorithm for multi-UAV formation reconfiguration

Formation flight of unmanned aerial vehicles (UAVs) utilizes reconfiguration procedures to handle a variety of emergencies, such as collision avoidance, malfunctions, fuel savings, and member replacement. As UAVs have limited computing power and energy resources, it is necessary to optimize the control inputs to reduce the distance travelled by UAVs while reducing the computing costs during formation reconfiguration. In this paper, the problem of multi-UAV reconfiguration is decoupled into two stages: task assignment and control input optimization of UAVs. For a solution to the above problem, we propose an adaptive hybrid particle swarm optimization and differential evolution algorithm (AHPSODE) to optimize minimize the distance of the total movement and reduce the computing cost of formation reconfiguration. Based on the idea of receding horizon control (RHC) and the nonlinear model of multi-UAV formation reconfiguration, an RHC controller using AHPSODE is designed to optimize the control input of the UAV group to obtain the shortest movement distance, and this method can reduce the computation time. We use the CEC 2017 test suit to test the performance of our proposed AHPSODE algorithm, and simulate the AHPSODE-based RHC controller to manage formation reconfiguration. The results show that our proposed AHPSODE performed well in convergence and accuracy and the RHC controller is effective.

Cooperative Position-based Formation-pursuit of Moving Targets by Multi-UAVs with Collision Avoidance

The paper focuses on the issue of capturing a moving target for multiple unmanned aerial vehicles (UAVs). The problem involves a group of UAVs to create a formation-pursuit in the encirclement of a moving target. Dynamic task allocation algorithm is used in 3D dynamic environments to efficiently allocate the target to several existing UAVs. Target information is disseminated to neighbor UAVs by the temporary leader of UAVs. For the formation-pursuit using a position-based strategy, destination points to create formation are made at the sphere coordinates around a moving target. Then the destination points are tracked using a fuzzy state feedback controller. Optimized artificial potential field (APF) algorithm is used to avoid collisions with targets, other UAVs, and static obstacles. Each UAV can choose the optimal trajectory to avoid obstacles and reset the formation after passing them. The simulation results show that multi-UAVs successfully surrounded and formed formation-pursuit of a moving target without colliding with the closest Euclidean distance between UAVs of 1.32957 m. UAVs with a target is 1.94359 m, and UAVs with static obstacles within a range of 1.60632 m.Keywords—formation-pursuit, multi-UAVs, obstacle avoidance, task allocation, tracking control.

Security Energy Efficiency Analysis of Analog Collaborative Beamforming With Stochastic Virtual Antenna Array of UAV Swarm

To extend the transmission range governed by limited transmit power, collaborative beamforming (CB) can be employed with a virtual antenna array (VAA) constructed by an unmanned aerial vehicle (UAV) swarm. CB techniques combined with physical layer security (PLS) algorithms are known to better secure transmissions of sensitive information in the presence of eavesdropping attacks, compared to co-located antenna-based schemes. In particular, analog collaborative beamforming (ACB) with random VAA topologies, which is suitable for UAV networks due to its lower signaling/channel estimation overhead compared to the digital CB, can provide a significantly higher secrecy rate by creating noise-like signals at eavesdroppers. However, the increase in energy consumption to improve secrecy rate had been ignored in the existing ACB-based PLS schemes, which is of paramount importance considering the limited onboard energy of UAVs. Thus, motivated by such limitation, in this paper, we examine the security energy efficiency of the ACB-based PLS technique, where a group of UAVs (or VAA elements) randomly change their locations in a distributed manner to randomize the array factor of the VAA, i.e., a stochastic VAA. The proposed stochastic VAA outperforms the existing static VAA in terms of secrecy energy efficiency as verified by analytic and numerical results. Further, the impact of phase errors of the proposed scheme is also presented, which indicates the significance of the accurate phase estimation.

Reconnaissance and Confirmation Task Planning of Multiple Fixed-Wing UAVs with Specific Payloads: A Comparison Study

In this study, the reconnaissance and confirmation task planning of multiple fixed-wing unmanned aerial vehicles (UAV) with specific payloads, which is an NP-hard problem with strong constraints and mixed variables, is decomposed into two subproblems, task allocation with “payload-target” matching constraints, and fast path planning of the UAV group, for which two mathematical models are respectively established. A bi-layer collaborative solution framework is also proposed. The outer layer optimizes the allocation scheme between the UAVs and targets, whereas the inner layer generates the UAV path and evaluates the outer scheme. In the outer layer, a unified encoding based on the grouping and pairing relationship between UAVs and targets is proposed. The corresponding combinatorial mutation operators are then designed for the representative NSGA-II, MOEA/D-AWA, and DMOEA-ϵC algorithms. In the inner layer, an efficient heuristic algorithm is used to solve the path planning of each UAV group. The simulation results verify the effectiveness of the cooperative bi-layer solution scheme and the combined mutation operators. At the same time, compared with the NSGA-II and MOEA/D-AWA, DMOEA-ϵC can obtain a significantly better Pareto front and can weigh the assigned number of UAVs and the total task completion time to generate more diversified reconnaissance confirmation execution schemes.

Path planning of multi-UAVs based on deep Q-network for energy-efficient data collection in UAVs-assisted IoT

In recent years, Unmanned Aerial Vehicles (UAVs) can effectively alleviate the problems of unstable links and low transmission efficiency, which have been applied for Wireless Sensor Networks (WSNs) to speed up data collection and transmission. However, when the multiple UAVs collect data onto the same area, there is a problem of overlapping coverage areas, which will result in low energy efficiency. Therefore, this paper studies the energy-efficient collaborative path planning problem to maximize data collection of UAVs from distributed sensors. Based on built multi-UAVs assisted system for collecting sensors data, we formulate the optimization objective to maximize the data collected by the UAV group within the limits of energy and the total covered area. To solve the problem of UAVs' collaborative path planning, we propose a Hexagonal Area Search (HAS) algorithm, which is combined with multi-agents Deep Q-Network(DQN), called HAS-DQN. By limiting the total coverage of UAVs, HAS-DQN can effectively avoid collision problems with UAVs. Experiments show that HAS-DQN can effectively solve the path overlap problem of multiple UAVs moving at the same cost in an unknown environment.

Collision-free formation-containment control for a group of UAVs with unknown disturbances

This paper focuses on the formation-containment control problem for a group of fixed-wing unmanned aerial vehicles (UAVs) subjected to unknown disturbances. To solve this problem, a two-layer control framework is developed by decoupling the problem into a trajectory generation problem at the group level and a trajectory tracking problem at the individual level. Firstly, based on the directed interaction topology and the required configuration of the UAVs, two types of finite-time estimators are constructed to generate the desired trajectories of the leading UAVs and the following UAVs, respectively. Meanwhile, to guarantee the safety of each UAV, the smooth collision avoidance potential function and obstacle avoidance potential function are designed. After that, using the dynamic surface control (DSC) technique and combined with the established potential functions, a distributed control law with the ability of collision avoidance is presented for each UAV to track the generated trajectory without any collision, where the homogeneous extended state observer (HESO) is developed to compensate for the mismatched and matched disturbances. The stability of the whole closed-loop system is provided using the Lyapunov theorem. Finally, simulation results demonstrate the effectiveness of the proposed control algorithm.

Cluster selection for load balancing in flying ad hoc networks using an optimal low-energy adaptive clustering hierarchy based on optimization approach

PurposeFlying ad hoc networks (FANETs) have a major effect in various areas such as civil projects and smart cities. The facilities of installation and low cost of unmanned aerial vehicles (UAVs) have created a new challenge for researchers. Cluster head (CH) selection and load balancing between the CH are the most critical issues in the FANETs. For CH selection and load balancing in FANETs, this study used efficient clustering to address both problems and overcome these challenges. This paper aims to propose a novel CH selection and load balancing scheme to solve the low energy consumption and low latency in the FANET system.Design/methodology/approachThis paper tried to select the CH and load balancing with the help of low-energy adaptive clustering hierarchy (LEACH) algorithm and bat algorithm (BA). Load balancing and CH selection are NP-hard problems, so the metaheuristic algorithms can be the best answer for these issues. In the LEACH algorithm, UAVs randomly generate numerical, and these numbers are sorted according to those values. To use the load balancing, the threshold of CH has to be considered; if the threshold is less than 0.7, the BA starts working and begins to find new CH according to the emitted pulses.FindingsThe proposed method compares with three algorithms, called bio-inspired clustering scheme FANETs, Grey wolf optimization and ant colony optimization in the NS3 simulator. The proposed algorithm has a good efficiency with respect to the network lifetime, energy consumption and cluster building time.Originality/valueThis study aims to extend the UAV group control concepts to include CH selection and load balancing to improve UAV energy consumption and low latency.

Adaptive hybrid formation-search and track controller of UAVs

In this paper, a novel search and track scheme is developed through adaptive hybrid formation- search and track controller of networked unmanned aerial vehicles (UAVs). The search and the track algorithm is carried out with antennas placed on at least four UAVs that kept the ‘+’ formation on the x-y plane. The x-axis is assumed to lie in the east–west direction and the y-axis in the north–south direction. UAVs flying in the ‘+’ formation are divided into 4 main groups: east, west, north, and south. UAVs on the x-axis are driven from the difference in signal strengths of the east group and the west group, and on the y-axis from the difference in signal strengths of the north and south groups respectively. The hybrid controller consists of two discrete modes: (1) formation mode (2) search and track mode. The back-stepping formation controller is derived through Lyapunov stability criteria to make the UAVs fly in ‘+’shape and at a fixed altitude. Then, received signal power consensus-based search and track controller is developed for networked UAV groups flying in a desired ‘+’ formation. Simulation results verify our theoretical claims.

Group movement of UAVs in environment with dynamic obstacles: a survey

PurposeThe successful application of the group of unmanned aerial vehicles (UAVs) in the tasks of monitoring large areas is becoming a promising direction in modern robotics. This paper aims to study the tasks related to the control of the UAV group while performing a common mission.Design/methodology/approachThis paper discusses the main tasks solved in the process of developing an autonomous UAV group. During the survey, five key tasks of group robotics were investigated, namely, UAV group control, path planning, reconfiguration, task assignment and conflict resolution. Effective methods for solving each problem are presented, and an analysis and comparison of these methods are carried out. Several specifics of various types of UAVs are also described.FindingsThe analysis of a number of modern and effective methods showed that decentralized methods have clear advantages over centralized ones, since decentralized methods effectively perform the assigned mission regardless of on the amount of resources used. As for the method of planning the group movement of UAVs, it is worth choosing methods that combine the algorithms of global and local planning. This combination eliminates the possibility of collisions not only with static and dynamic obstacles, but also with other agents of the group.Originality/valueThe results of scientific research progress in the tasks of UAV group control have been summed up.

Energy-Efficient UAV Movement Control for Fair Communication Coverage: A Deep Reinforcement Learning Approach.

Unmanned Aerial Vehicles (UAVs) are considered an important element in wireless communication networks due to their agility, mobility, and ability to be deployed as mobile base stations (BSs) in the network to improve the communication quality and coverage area. UAVs can be used to provide communication services for ground users in different scenarios, such as transportation systems, disaster situations, emergency cases, and surveillance. However, covering a specific area under a dynamic environment for a long time using UAV technology is quite challenging due to its limited energy resources, short communication range, and flying regulations and rules. Hence, a distributed solution is needed to overcome these limitations and to handle the interactions among UAVs, which leads to a large state space. In this paper, we introduced a novel distributed control solution to place a group of UAVs in the candidate area in order to improve the coverage score with minimum energy consumption and a high fairness value. The new algorithm is called the state-based game with actor–critic (SBG-AC). To simplify the complex interactions in the problem, we model SBG-AC using a state-based potential game. Then, we merge SBG-AC with an actor–critic algorithm to assure the convergence of the model, to control each UAV in a distributed way, and to have learning capabilities in case of dynamic environments. Simulation results show that the SBG-AC outperforms the distributed DRL and the DRL-EC3 in terms of fairness, coverage score, and energy consumption.

New algorithm for calculating the required number of unmanned aerial vehicles and the duration of their stay in dangerous area

This paper proposes a new approach to calculate the time spent by unmanned aerial vehicle (UAV) in dangerous area with the consideration of the maximum allowed probability of losing UAV and the increasing rate of this probability in a given area. Unlike the known approaches, which are based on flying around the dangerous areas, it is proposed to cross the boundaries of the dangerous area for a defined time, which is calculated to allow the obtaining of the required data set about the interested area. Based on the UAV loss probabilities estimates, an approach to planning the number of UAVs in a group flight is substantiated, taking into account losing them. The formula for calculating the required number of UAVs, obtained by this approach, consists of three terms, which consider the requirements for pre-flight task accomplishment, high-quality in-flight service of new requests, as well as the necessary reserve in case of the decrease of UAV performance. To evaluate the quality of the proposed algorithm, various case with different initial conditions in determining the time of stay in dangerous zone are considered. The specified time minimizes the given indicator. In addition, the paper presents practical example where the algorithm is used to observe a territory by a group of UAVs. It is shown that the algorithm can determine the required number of UAVs to study an area with a given dimension, and it also can calculate the time of stay of each UAV in the dangerous area in order to reduce the loss probability of UAVs.

Analysis of the experience of the combat use of unmanned aerial vehicles against anti-aircraft missile systems in the military conflict in Nagorno-Karabakh.

An analysis of the latest military conflicts and local wars in Ukraine, Syria, Libya and other regions of the planet clearly demonstrates that practically in each of them there is a new format of warfare, traditional ideas about the forms and methods of armed struggle on the battlefield are being destroyed, and significant adjustments are being made. in the strategy and tactics of achieving victorious results for each side. One of them was the so-called "Karabakh conflict" between Armenia and Azerbaijan over control over the territory of Nagorno-Karabakh. The events of September-November 2020 in the scientific community have been called the “drone war”. It was thanks to modern drones that Azerbaijan gained air superiority and used it to achieve a strategic advantage in the war. Modern unmanned aerial vehicles (UAVs) are able not only to effectively identify the enemy day and night, direct their own firepower at him, but also independently destroy him at a considerable distance from the battlefield.The article discusses the use of a massive UAV raid against anti-aircraft missile systems (SAM) of air defense (air defense) in a military conflict on the territory of Nagorno-Karabakh. Such use of UAVs for air defense air defense systems led to the rapid depletion of their combat resource and, as a consequence, the further inability of these complexes to solve tasks according to their intended purpose. An analysis of the results of the combat use of air defense systems against modern UAVs showed that the detection range of the air defense missile system by the UAV equipment has become relatively the same, and sometimes even exceeds it. In the course of the military conflict in Nagorno-Karabakh, a new tactic for the use of UAVs was developed, which makes it possible to ensure the guaranteed defeat of the air defense missile system and thereby carry out the functional suppression of the air defense system and ensure the gains of air superiority. For a scientifically substantiated confirmation of the possibility of successful destruction of an anti-aircraft missile-gun complex (AMC) by a UAV group, analytical and simulation modeling was carried out and appropriate conclusions were drawn to resolve the military conflict in Donbass.

GREEN: A Global Energy Efficiency Maximization Strategy for Multi-UAV Enabled Communication Systems

In the scenario of limited energy supply, Unmanned Aerial Vehicles (UAVs) enabled communication systems must make efficient use of energy in order to provide long-term service. In this paper, we propose a global energy efficiency maximization (GREEN) strategy for multi-UAV enabled communication systems. In such systems, a group of UAVs communicates with their associated ground terminals (GTs) by using a UAV-enabled interference channel (UAV-IC). In particular, we optimize the UAVs' trajectory control by jointly considering both the communication throughput and the total energy consumption of the whole system. We aim to maximize the global energy efficiency (GEE) of a task for multi-UAV communications, in which the problem is challenging to optimally solve due to its non-convex nature and strongly coupled variables. To tackle this problem, first, we investigate and propose a global energy-efficient optimization problem based on the fly-hover-communicate protocol. Second, we extend our proposed solution from the single UAV-enabled system to multiple UAV-GT pairs cases. In addition, we consider the general scenario in which the UAVs also communicate while flying. Based on the successive convex approximation technique and the path discretization method, the GREEN strategy is designed for optimizing UAV trajectories in this scenario. The simulation results show that the proposed strategy can achieve significantly higher GEE than the benchmark schemes for multi-UAV enabled communications.

Formalization of the Problem of Optimization of Base Places and Routes of the UAV Group

Introduction. The problem of planning the mission of a set of heterogeneous unmanned aerial vehicles (UAVs)is considered, which is to survey and/or service a given set of targets in the field. A mathematical model of the problem and algorithms for its solving that is based on deterministic local search, as well as optimization by ant colonies are proposed. The efficiency of algorithms is investigated based on the results of solving problems with real objects in the field. The relative error of the results of each algorithm was obtained, which allowed to compare their efficiency. The purpose of the paper is to solve a routing problem in different ways to reduce overall mission cost and compare the efficiency. The problem statement considers multiple starting points and destinations (depots) for UAVs with determined capacity, so algorithms proposed in the paper are designed to optimize the initial placement. Each UAV has a maximum flight distance because of an energy limit, though vehicles can be recharged by visiting one of previously placed depots. The mission goal is to visit all the given targets while minimizing the overall cost, so fuel consumption over distance, depot placement, and resources needed to survey and/or service of the target by each UAV are considered as components of the final cost metric to be minimized considering a set of specific constraints. Results. To solve the given UAV routing problem, a max-min algorithm of ant systems was developed, which features step-by-step interaction of ants to form solutions, a hybrid taboo search algorithm and a deterministic local search algorithm - the decay vector method. The developed algorithms were tested both on the known travelling salesman problems, and on specially developed problems with multiple depots and additional restrictions. Conclusions. The proposed algorithms which are based on ant colony optimization are compared both in terms of accuracy and computation time. A hybrid algorithm achieved slightly better score, though computation time has increased. Keywords: routing, combinatorial optimization, UAV, local search, ant colony optimization algorithms.

ОЦЕНКА ВЛИЯНИЯ ИЗМЕНЯЮЩИХСЯ УСЛОВИЙ ОКРУЖАЮЩЕЙ СРЕДЫ НА РАСПРЕДЕЛЕНИЕ ВЫЧИСЛИТЕЛЬНОЙ НАГРУЗКИ В ГРУППЕ БПЛА

The paper considers the problem of workload distribution in a group of unmanned aerial vehicles(UAVs) when monitoring a certain area in a changing environment, which has a direct impact on theonboard energy resources consumption. The stage of a monitoring problem-solving, which includes thedistribution of UAVs over scanning bands, is described here. When this stage is carried, there is noopportunity to take into account the factors of environmental impact. But these factors are crucialdue to the limited onboard energy resources. In this regard, a situation is very likely when the UAV isnot able to complete the sub-task assigned to it, which jeopardizes the completion of the entire missionof the group. To avoid this situation, it is proposed to use the technique of a decision-making onthe need to relocate the workload in a group of mobile robots (MR). The decision-making is based onthe ontological analysis procedure, which allows limiting the number of choices for workload relocation.The ontology model of the workload distribution in a group of UAVs was developed. This modeltakes into account the possibility of additional performance involvement either by means of the resourcesof neighboring UAVs, or by means of devices of the "foggy" layer. Examples of productionrules are given, on the basis of which a decision is made on the need to relocate the workload. Acomparative estimation of the resources volume involved in the implementation of two methods ofworkload relocation problem solving, depending on the frequency of changes in environmental conditions,is carried out. The results of computational experiments have shown that the method based onontological analysis is more efficient in comparison with the method based on LDG (Local DeviceGroup) in terms of the amount of resources involved. This makes it possible to increase the time of jointmission implementation by the UAV group.