Multiple Unmanned Aerial Vehicles Research Articles

A robust multiple Unmanned Aerial Vehicles 3D path planning strategy via improved particle swarm optimization

Path planning algorithms for Unmanned Aerial Vehicles (UAVs) are essential in various domains, such as search and rescue operations, agriculture, and delivery services. Nonetheless, finding optimal flight paths in complex environments with obstacles remains challenging. This paper proposes a novel path planning algorithm that uses a Nash equilibrium approach based on game theory to strike a balance between the exploitation and exploration capabilities of UAVs. The algorithm is designed in a simulated flight environment with obstacles, utilizing the self-awareness and group awareness coefficients from particle swarm optimization(PSO) as participants in the game theory model. The proposed path planning algorithm significantly improves the navigation of multiple UAVs in complex scenarios with obstacles by achieving a balance between their exploitation and exploration capabilities. Simulation experiments demonstrate that the proposed method surpasses traditional approaches, exhibiting an average improvement of 32.57%, 32.17%, and 29.33% in the algorithm convergence time, average flight distance, and average flight time, respectively. The significance of this research lies in its contribution to advancing UAV path planning algorithms. By integrating game theory and PSO, the proposed method optimizes the exploration and exploitation capabilities of UAVs, leading to improved navigation and resource utilization.

Cooperative situational awareness of multi-UAV system based on improved D-S evidence theory

Situational awareness (SA) of unmanned aerial vehicles (UAVs) has been a research hotspot over the decades. Existing research mainly focuses on the SA of a single UAV in two-dimensional planes, whereas obstacles are assumed a priori. To eliminate these constraints, this study considers the cooperative situational awareness (CSA) problem of multi-UAV systems in the scenario of crossing a three-dimensional (3D) obstacle belt while no prior information of obstacles is required. First, the distribution models of the multi-UAV system and the obstacles are built based on two reference frames. Second, various types of uncertainties are characterized, which reflect an urgent need for CSA. Thus, a centralized CSA scheme is proposed and conducted on multiple UAVs and at different times, and the Dempster-Shafer (D-S) evidence theory is introduced to address information uncertainties and achieve high-accuracy information fusion. Next, to deal with the high-conflict evidence situations that are common in practice, a modified D-S fusion method is further developed. A modified Pearson coefficient is utilized to measure the correlation between different pieces of evidence. Both information credibility and uncertainty are taken into account to evaluate the evidence from different perspectives, and a novel evidence weight assignment method is presented to treat high-conflict situations. Numerical simulations validate the effectiveness of the proposed CSA method. Compared to existing studies, the proposed method is applicable to different trust paradoxes and achieves the best performance among various fusion methods.

Performance Analysis and Optimization of UAV-Assisted Networks: Single UAV With Multiple Antennas Versus Multiple UAVs With Single Antenna

In this article, we make a comparison between the following two unmanned aerial vehicles (UAVs) network models: 1) multiple UAVs equipped with a single antenna and 2) a single UAV equipped with multiple antennas (MAs). In order to assess the performance of the two network models, we derive closed-form expressions for the outage probability, average symbol error probability, ergodic capacity, and energy efficiency of both models. After that, we formulate a joint optimization problem to obtain the optimal power values and UAV altitudes using the particle swarm optimization technique. We consider in the first scenario both uniformly and nonuniformly distributed UAVs assuming Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> fading channels. Numerical results are obtained for different simulation scenarios to validate our proposed model, followed by conclusions to show its effectiveness. The results show the superiority of the multiple uncorrelated antennas single UAV model over a single antenna multiple UAV model. Moreover, several comparisons have been performed between blanket transmission mode (BTM) and single transmission selection mode (STSM) for the multiple UAVs model, which reveal that the BTM outperforms the STSM for different systems parameters.

Decentralized, Privacy-Preserving Routing of Cellular-Connected Unmanned Aerial Vehicles for Joint Goods Delivery and Sensing

Unmanned aerial vehicles (UAVs) have been extensively applied to goods delivery and in-situ sensing. It becomes increasingly probable that multiple UAVs are delivering goods and carrying out sensing tasks at the same time. The destinations of the UAVs are usually required to jointly design their trajectories and sensing selections, leading to privacy concerns for the UAVs. This paper presents a new game-theoretic routing framework for joint goods delivery and sensing of multiple cellular-connected UAVs, where the UAVs minimize their energy consumption and connectivity outage, maximize their sensing reward, and ensure timely goods delivery and trajectory privacy by optimizing their trajectories and sensing task selections in a decentralized manner. The key idea is that we unify routing and sensing in a single task selection process, which is further transformed into routing on a task-time graph. Another important aspect is that we design a non-cooperative potential game for the routing on the task-time graph. A distributed strategy is developed, where each UAV only reports its sensing task selections and withholds its destination information and its best response produced by the Bellman-Ford algorithm. By this means, the destination and trajectory privacy of the UAVs are protected. Simulations show that the new game-theoretic approach can ensure timely delivery and achieve close-to-optimal solutions with significantly lower complexity compared to a centralized brute-force approach.

Asymptotical Consensus of MIMO Linear Multiagent Systems With a Nonautonomous Leader and Directed Switching Topology: A Continuous Approach

In this paper, we investigate the asymptotical consensus tracking problems for multiple-input-multiple-output (MIMO) linear multi-agent systems (MASs) with directed switching topology and a non-autonomous leader subject to nonzero unknown inputs. First, we design a full order unknown input observer (UIO) based on relative outputs to estimate the relative full states. Based on this UIO, we design a continuous consensus controller by introducing a decay function which remains positive into the term that is used to eliminate the effects of leader's nonzero inputs. And by using the multiple Lyapunov functions (MLFs) method, we prove that asymptotical consensus can be achieved if the average dwell time (ADT) is greater than a positive threshold. Second, we design a continuous consensus controller based on a reduced order UIO that can significantly simplify calculations. Finally, we give two examples about multiple unmanned aerial vehicles (UAVs) to verify the theoretical results. Compared with existing works, the continuous controllers here can not only achieve zero error consensus tracking, but also are chattering free.

Analysis using a Model-Based Approach of Path Planning for Multiple Uavs in the Context of Surveying Building Damage Following a Disaster

In the wake of disasters, efficient and timely assessment of building damage is of paramount importance for effective disaster response and recovery efforts. Unmanned Aerial Vehicles (UAVs), commonly known as drones, have emerged as indispensable tools for postdisaster surveying due to their ability to rapidly cover large areas and provide high-resolution imagery. The effective utilization of multiple UAVs in such scenarios necessitates optimal path planning to maximize coverage, minimize mission time, and ensure safe navigation. This paper presents a comprehensive model-based analysis of multi-UAV path planning strategies tailored for surveying building damage in postdisaster environments. The study commences by emphasizing the critical role of UAVs in disaster management, especially in the context of assessing structural damage. Acknowledging the challenges posed by complex urban landscapes and the need for swift response, the research focuses on the development and evaluation of path planning strategies that leverage advanced modeling techniques. The proposed model integrates spatial data, building blueprints, and environmental information to generate a realistic representation of the disaster-affected area, facilitating informed decision- making for path planning. A range of path planning approaches is examined within the model-based framework. These include grid-based methods, which discretize the area into cells and deploy UAVs to cover designated regions, and graph-based approaches such as Rapidly-exploring Random Trees (RRT), which generate paths through probabilistic exploration. The paper also delves into machine learning-enhanced strategies, leveraging reinforcement learning algorithms to adapt UAV paths based on real-time observations and evolving disaster dynamics. Furthermore, the study underscores the scalability of the model-based approach, demonstrating its applicability to scenarios involving a varying number of UAVs. Through comprehensive experimentation, the paper provides insights into the optimal deployment of UAV teams, shedding light on the balance between increased coverage and potential communication overhead.

Energy Efficient Computation Offloading in Aerial Edge Networks With Multi-Agent Cooperation

With the high flexibility of supporting resource-intensive and time-sensitive applications, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is proposed as an innovational paradigm to support the mobile users (MUs). As a promising technology, digital twin (DT) is capable of timely mapping the physical entities to virtual models, and reflecting the MEC network state in real-time. In this paper, we first propose an MEC network with multiple movable UAVs and one DT-empowered ground base station to enhance the MEC service for MUs. Considering the limited energy resource of both MUs and UAVs, we formulate an online problem of resource scheduling to minimize the weighted energy consumption of them. To tackle the difficulty of the combinational problem, we formulate it as a Markov decision process (MDP) with multiple types of agents. Since the proposed MDP has huge state space and action space, we propose a deep reinforcement learning approach based on multi-agent proximal policy optimization (MAPPO) with Beta distribution and attention mechanism to pursue the optimal computation offloading policy. Numerical results show that our proposed scheme is able to efficiently reduce the energy consumption and outperforms the benchmarks in performance, convergence speed and utilization of resources.

Stackelberg-Game-Based Intelligent Offloading Incentive Mechanism for a Multi-UAV-Assisted Mobile-Edge Computing System

We study the intelligent offloading problem for a multiple unmanned aerial vehicle (multi-UAV)-assisted mobile edge computing (MEC) system in a MEC scenario where a natural disaster has damaged the edge server. The study has two steps. First, the task offloading destination is determined by minimizing the total energy consumption of the multiple UAVs in the system. We propose the server selection game-theoretic (SSGT) algorithm and demonstrate its convergence through simulation experiments. Second, we propose an offloading incentive mechanism to price computing resources for a single UAV-MEC server. Considering the UAV’s power consumption and mobile users’ willingness, we model the interaction between the UAV-MEC server and mobile users as a Stackelberg game. We prove the existence of a Nash equilibrium by theoretical analysis and experimental verification and design the multi-round iterative game (MRIG) algorithm based on arithmetic descent to achieve the optimal solution, i.e., the utility tradeoff between the UAV-MEC server and mobile users. Finally, the simulation results show that our proposed scheme can increase the value of overall user satisfaction (SoU) more than other schemes, which proves that the incentive mechanism of resource pricing can supply computing power support for ground mobile users in a UAV-assisted MEC system more effectively.

Deployment of Energy-Efficient Aerial Communication Platforms With Low-Complexity Detection

Due to their flexibility, mobility and autonomy, unmanned aerial vehicles (UAVs) are considered as a potential candidate to operate as flying base stations to provide a specific geographical area with air-to-ground wireless communications services. Besides, the limitation of the on-board energy motivates the design of a more energy-efficient way to transmit information. Moreover, the deployment of multiple UAVs can affect the quality of experience (QoE) of users. In this paper, we propose a method that combines the concept of index modulation (IM) with the UAV communications systems, which we refer to as IM-UAV, to attain an improved energy-efficiency (EE). Furthermore, based on the proposed IM-UAV communication system, a gradient descent based UAV deployment scheme is designed to maximize the downlink average rate of the ground users (GUs) in the target area. On the other hand, the maximum likelihood (ML) detection for the IM-UAV requires a high computational complexity for detection at the GUs, while providing the best possible performance. Hence, we propose a low-complexity detection scheme that can separately detect the index symbols and data symbols to reduce the computational complexity at the receiver side. The simulation results demonstrate that the proposed deployment method is capable of attaining the appropriate positions to deploy the UAVs, while the EE is also improved by combining IM with UAV communication system. In addition, the proposed low-complexity detection scheme reduces the computation complexity by slightly sacrificing the bit error rate (BER) performance.

The low latency networking method for task-driven MEC-enabled UAV swarm

Unmanned Aerial Vehicles (UAVs) are increasingly being used for special missions as Mobile Edge Computing (MEC) devices, and the utilization of multiple UAVs who are integrated into formations for collaboratively executing tasks that cannot be performed by single aircraft has also been a recent research hit. In this paper, we present a low latency networking method aims to shorten the preparation time so that MEC-enabled swarm can as quick as possible concentrate on the task execution. For reducing the time of swarm initialization, we consider applying the Leader-Follower (L-F) topology model in networking and formation control. Firstly, by combining the nature of wireless communication and correlation analysis, we improve the traditional L-F model to avoid too many nodes accessing the same node and raise the communication efficiency. Secondly, in order to choose the topology that can provide better performance in network, we adopt an improved Particle Swarm Optimization (PSO) algorithm to customize the appropriate factor coefficients during leader selection for swarms with different densities and communication initial conditions. Experiments and evaluations demonstrate that the proposed algorithm has significant effect in reducing network latency.

LEO satellite assisted UAV distribution using combinatorial bandit with fairness and budget constraints.

In this paper, an integration between a low earth orbit satellite (LEO-Sat) and unmanned aerial vehicle (UAV) is proposed to assist users in post-disaster areas. In this scenario, multiple UAVs will be distributed to fully cover the victims and provide rescue services, while LEO-Sat provides backhaul links for UAVs to the ground base station (GBS). In this regard, we consider the problem of efficient UAVs distribution to maximize the total sum rate of the victims while assuring fairness in their coverage within the limited resources of UAVs batteries and LEO-Sat bandwidth. In this paper, UAV distribution problem is considered as a combinatorial multi-armed bandit (MAB) with arms' fairness and limited UAVs battery budget (CMAB-FB) constraints. Additionally, the utilization of LEO-Sat bandwidth resources is optimized based on the average traffic demands of the LEO-UAV links by means of gradient decent algorithm. The results of numerical analysis indicate that the proposed approach outperforms other naïve ben chmarks.

Multi‐agent reinforcement learning based transmission scheme for IRS‐assisted multi‐UAV systems

AbstractIn this paper, a transmission scheme based on multi‐agent reinforcement learning for intelligent reflecting surface (IRS)‐assisted multiple unmanned aerial vehicles (UAVs) systems is proposed. The proposed scheme is based on reinforcement learning and alternating optimization algorithm, which can effectively improve communication quality and ensure fairness. The scheme is divided into two parts. In the first part, the multi‐UAV cooperation problem is modeled as a markov decision process. The objective of each UAV is to maximize the minimum user channel gain. To achieve stable strategies for all agents, the Multi‐agent Deep Deterministic Policy Gradient (MADDPG) algorithm is applied to train UAVs trajectories to reach the Nash equilibrium. The MADDPG algorithm is centralized trained at the base station and executed in a distributed manner by each UAV, ensuring efficient and effective coordination among agents. In the second part, an alternating optimization algorithm is formulated to optimize active and passive beamforming. Considering the non‐convexity of the fairness objective, by using auxiliary variables and semi‐definite relaxation method, the problem of maximizing the minimum user achievable rate is transformed into a feasibility problem. Simulation results show that the proposed scheme can effectively train UAVs trajectories and improve the communication performance of all users fairly.

Q-Learning based system for Path Planning with Unmanned Aerial Vehicles swarms in obstacle environments

Path Planning methods for the autonomous control of Unmanned Aerial Vehicle (UAV) swarms are on the rise due to the numerous advantages they bring. There are increasingly more scenarios where autonomous control of multiple UAVs is required. Most of these scenarios involve a large number of obstacles, such as power lines or trees. Despite these challenges, there are also several advantages; if all UAVs can operate autonomously, personnel expenses can be reduced. Additionally, if their flight paths are optimized, energy consumption is reduced, leaving more battery time for other operations. In this paper, a Reinforcement Learning-based system is proposed to solve this problem in environments with obstacles by utilizing Q-Learning. This method allows a model, in this case, an Artificial Neural Network, to self-adjust by learning from its mistakes and successes. Regardless of the map’s size or the number of UAVs in the swarm, the goal of these paths is to ensure complete coverage of an area with fixed obstacles for tasks like field prospecting. Setting goals or having any prior information apart from the provided map is not required. During the experimentation phase, five maps of varying sizes were used, each with different obstacles and a varying number of UAVs. To evaluate the quality of the results, the number of actions taken by each UAV to complete the task in each experiment was considered. The results indicate that the system achieves solutions with fewer movements as the number of UAVs increases. An increasing number of UAVs on a map lead to solutions in fewer moves. The results have been compared, and a statistical significance analysis has been conducted on the proposed model’s outcomes, demonstrating its capabilities. Thus, it is shown that a two-layer Artificial Neural Network used to implement a Q-Learning algorithm is sufficient to operate on maps with obstacles.

Multi-UAV Cooperative and Continuous Path Planning for High-Resolution 3D Scene Reconstruction

Unmanned aerial vehicles (UAVs) are extensively employed for urban image captures and the reconstruction of large-scale 3D models due to their affordability and versatility. However, most commercial flight software lack support for the adaptive capture of multi-view images. Furthermore, the limited performance and battery capacity of a single UAV hinder efficient image capturing of large-scale scenes. To address these challenges, this paper presents a novel method for multi-UAV continuous trajectory planning aimed at the image captures and reconstructions of a scene. Our primary contribution lies in the development of a path planning framework rooted in task and search principles. Within this framework, we initially ascertain optimal task locations for capturing images by assessing scene reconstructability, thereby enhancing the overall quality of reconstructions. Furthermore, we curtail energy costs of trajectories by allocating task sequences, characterized by minimal corners and lengths, among multiple UAVs. Ultimately, we integrate considerations of energy costs, safety, and reconstructability into a unified optimization process, facilitating the search for optimal paths for multiple UAVs. Empirical evaluations demonstrate the efficacy of our approach in facilitating collaborative full-scene image captures by multiple UAVs, achieving low energy costs while attaining high-quality 3D reconstructions.

Three Dimensional Formation Control of Unmanned Aerial Vehicles in Obstacle Environments

Today, the use of small unmanned aerial vehicles (UAVs) has increased due to technological advances. There has been an interest in using multiple UAVs instead of a single UAV to accomplish a given mission. This is because there are many scenarios where the capabilities of a single UAV are inadequate due to certain constraints (battery capacity, time in the air). For this reason, swarm UAV studies have increased. A swarm UAV consists of a large number of UAVs cooperating to accomplish a specific mission.&#x0D; &#x0D; This study shows three-dimensional formation control of a swarm UAV system in an obstacle environment. A centralized control architecture is used in this process. All task assignments are made from a centralized system. The Artificial potential fields method creates the formation at the target point by avoiding obstacles. The study was carried out using the Robot Operating System (ROS). The methods were tested in Webots simulation environment. Crazyflie robots were used in the experiments. &#x0D; &#x0D; In the simulation environment, square, star and v formations were first tested in two dimensions. Then, as an example of three-dimensional formation, the cubic and pyramid formations were created and observed.

An autonomous task assignment and decision-making method for coverage path planning of multiple pesticide spraying UAVs

As the approaching of Agriculture 4.0 era and advancements of new technologies of Unmanned Aerial Vehicles (UAVs) and particularly quadcopter UAVs, plant protection quadcopters are becoming increasingly popular and practical in pesticide spraying, fertilization, pollination, seeding, and other agricultural activities. One of the main problems for plant protection quadcopters is completing planning tasks efficiently and quickly. Therefore, this paper proposes an autonomous task assignment and decision-making method for coverage path planning by multiple cooperative quadcopters. The Sequential Quadratic Programming (SQP) method is adopted to acquire the optimal solution for the proposed problem. Then, a simulation platform by MATLAB Graphical User Interfaces (GUIs) is established using the Stateflow technique, and multiple ZY-UAV-680 quadrotor UAVs are employed to carry out the actual flight tests. Finally, simulations and actual flight tests are conducted to demonstrate the effectiveness of the autonomous optimal task assignment and decision-making method. The final results show that: the proposed method can divide multiple UAVs reasonably to several blocks; the time differences between the simulation test and real flight test are only 39.8 sec and 20.6 sec, and accounts for 6.6% and 3.9% of the spraying time spent on real flight test by three cooperative quadrotors, respectively; the optimal scheme can save 60.8 sec and 80.0 sec in the simulation tests and the real flight tests, which accounts for 10.8% and 13.2% of the time spent on average scheme, respectively. Therefore, it can be concluded that the simulation results can match the real flight test results quite well regardless of average scheme or optimal scheme, and the optimal scheme is more efficient and timesaving than the average scheme.

Maximizing data gathering and energy efficiency in UAV-assisted IoT: A multi-objective optimization approach

The data volume and energy consumption are important design aspects of data collection in the Internet of Things (IoT). In this paper, the unmanned aerial vehicles (UAVs)-assisted data gathering (DG) in IoT is investigated, in which multiple rotary-wing UAVs are dispatched to gather data from multiple terrestrial IoT devices within specified coverage range. In order to ensure the freshness of the collected data, we must consider that each UAV should spend as little time as possible to collect data. Moreover, it is assumed that UAVs collect data from the devices only when they are hovering, and the transmission distance and on-board energy of the UAVs are limited. Under these practical hypotheses, we formulate a UAV-assisted data gathering multi-objective optimization problem (UAVDGMOP) to simultaneously maximize the total data volume collected by UAVs, minimize the maximum-time for UAVs collecting data and approaching the optimal positions, and minimize the total energy consumptions of UAVs via jointly optimizing the locations of UAVs, the transmission power of UAVs, and the scheduling of UAV–device pairs. Moreover, the formulated UAVDGMOP consists of the hybrid and complex solutions, which is difficult to be solved by using the conventional methods, and thus we solve it by proposing a hybrid update multi-objective evolutionary algorithm based on decomposition (HUMOEA/D). Simulation results demonstrate that the proposed approach achieves higher data amount and energy efficiency of the network, and it is more suitable for solving the formulated UAVDGMOP comparing to other comparative approaches, including the random, uniform and layering deployment approaches, as well as other multi-objective evolutionary algorithms (MOEAs). Moreover, the proposed HUMOEA/D still achieves the overall excellent performance in more use cases.

Tethered Unmanned Aerial Vehicles—A Systematic Review

In recent years, there has been a remarkable surge in the development and research of tethered aerial systems, thus reflecting a growing interest in their diverse applications. Long-term missions involving aerial vehicles present significant challenges due to the limitations of current battery solutions. Tethered vehicles can circumvent such restrictions by receiving their power from an element on the ground such as a ground station or a mobile terrestrial platform. Tethered Unmanned Aerial Vehicles (UAVs) can also be applied to load transportation achieved by a single or multiple UAVs. This paper presents a comprehensive systematic literature review, with a special focus on solutions published in the last five years (2017–2022). It emphasizes the key characteristics that are capable of grouping publications by application scope, propulsion method, energy transfer solution, perception sensors, and control techniques adopted. The search was performed in six different databases, thereby resulting in 1172 unique publications, from which 182 were considered for inclusion in the data extraction phase of this review. Among the various aircraft types, multirotors emerged as the most widely used category. We also identified significant variations in the application scope of tethered UAVs, thus leading to tailored approaches for each use case, such as the fixed-wing model being predominant in the wind generation application and the lighter-than-air aircraft in the meteorology field. Notably, the classical Proportional–Integral–Derivative (PID) control scheme emerged as the predominant control methodology across the surveyed publications. Regarding energy transfer techniques, most publications did not explicitly describe their approach. However, among those that did, high-voltage DC energy transfer emerged as the preferred solution. In summary, this systematic literature review provides valuable insights into the current state of tethered aerial systems, thereby showcasing their potential as a robust and sustainable alternative to address the challenges associated with long-duration aerial missions and load transportation.

Joint online 3D trajectory optimisation and power allocation of UAVs as mobile access points in a downlink distributed multi-user MIMO system

Unmanned aerial vehicles (UAVs) are introduced as one of the key-enablers for 5G beyond networks due to their low cost and flexible deployment, and mobility degrees-of-freedom. Moreover, low sensitivity to blockage and uncorrelated channels in distributed multiple-input multiple-output (D-MIMO) systems, and their extension which is called cell-free massive MIMO, have made them an attractive research field. The location and number of access points (APs) in D-MIMO systems highly influence the system performance in terms of energy and spectral efficiency that can be handled by UAVs mobility and flexible deployment. In this paper, joint power allocation, and 3D trajectory design in a D-MIMO system for a downlink scenario where multiple UAVs serve as mobile APs (MbAPs) is investigated. Contrary to the existing works in multi-mobile base stations, we introduce space-division multiple access (SDMA) for the online trajectory design as a multi-access technology which does not suffer from the non-casual channel property in traditional trajectory designs. In the proposed design, the smallest user’s ergodic rate lower bound is maximised by jointly optimising MbAPs’ 3D trajectory and transmit power. The block coordinate descent (BCD) algorithm is deployed to break the main problem into two sub-problems and iteratively optimise over the two. Considering the non-convex nature of both of sub-problems, successive convex approximation is also exploited. For performance analysis, complexity and convergence property of the proposed algorithm are investigated. Finally, numerical results validate the improved performance of the proposed system in a practical scenario, where users can connect and disconnect to and from the network.

Multi-UAV-assisted covert communications for secure content delivery in Internet of Things

Unmanned aerial vehicles (UAVs) have found extensive applications in fast deploying Internet of Things in non-infrastructure areas due to their high maneuverability and robust networking. However, the open nature of wireless channels makes air-to-ground content delivery face serious security risks. A multi-UAV-assisted covert communication system model is proposed in this paper, which focuses on the secure content delivery from multiple UAVs to multiple legitimate ground users (LGUs) under the detection of an illegal interceptor. Our objective is to maximize the average covert rate (ACR) for all LGUs by synergistically optimizing UAV-LGU scheduling, multi-UAV transmit power, and multi-UAV trajectory, while complying with the covertness constraint. To cope with the formulated non-convex problem, we divide it into three sub-problems, and then propose an iterative algorithm to alternately solve them. Simulation results demonstrate that the proposed algorithm can significantly improve the ACR of the system by adaptively adjusting the Multi-UAV trajectory, flight speed and transmit power, while meeting the covertness constraint. Moreover, compared with the two benchmark schemes, the proposed joint optimization scheme can enhance the ACR by 5% and 31%, respectively, when the flight period is 100 s.