Unmanned Aerial Vehicle Cluster Research Articles

Simulation and Analysis of Grid Formation Method for UAV Clusters Based on the 3 × 3 Magic Square and the Chain Rules of Visual Reference

Large-scale unmanned aerial vehicle (UAV) formations are vulnerable to disintegration under electromagnetic interference and fire attacks. To address this issue, this work proposed a distributed formation method of UAVs based on the 3 × 3 magic square and the chain rules of visual reference. Enlightened by the biomimetic idea of the plane formation of starling flocks, this method adopts the technical means of airborne vision and a cooperative target. The topological structure of the formation’s visual reference network showed high static stability under the measurement of the network connectivity index. In addition, the dynamic self-healing ability of this network was analyzed. Finally, a simulation of a battlefield using matlab showed that, when the loss of UAVs reaches 85% for formations with different scales, the UAVs breaking formation account for 5.1–6% of the total in the corresponding scale, and those keeping formation account for 54.4–65.7% of the total undestroyed fleets. The formation method designed in this paper can maintain the maximum number of UAVs in formation on the battlefield.

A self-adaptive deployment model of UAV cluster for emergency communication network

Equipped with micro wireless sensor nodes, a unmanned aerial vehicle) cluster can form an emergency communication network, which can have several applications such as environmental monitoring, disaster relief, military operations and so on. However, situations where there is excessive aggregation and small amount of dispersion of the unmanned aerial vehicle cluster may occur when the network is formed. To mitigate these, a solution based on a 3D virtual force driven by self-adaptive deployment (named as 3DVFSD) is proposed. As a result, the three virtual forces of central gravity, uniform force, and boundary constraint force are combined to act on each node of the communication network. By coordinating the distance between the nodes, especially the threshold of the distance between the boundary node and the boundary, the centralized nodes can be relatively dispersed. Meanwhile, the nodes can be prevented from being too scattered by constraining the distance from the boundary node to the end. The simulation results show that the 3DVFSD algorithm is superior to the traditional virtual force-driven deployment strategy in terms of convergence speed, coverage, and uniformity.

Building a Connected Communication Network for UAV Clusters Using DE-MADDPG

Clusters of unmanned aerial vehicles (UAVs) are often used to perform complex tasks. In such clusters, the reliability of the communication network connecting the UAVs is an essential factor in their collective efficiency. Due to the complex wireless environment, however, communication malfunctions within the cluster are likely during the flight of UAVs. In such cases, it is important to control the cluster and rebuild the connected network. The asymmetry of the cluster topology also increases the complexity of the control mechanisms. The traditional control methods based on cluster consistency often rely on the motion information of the neighboring UAVs. The motion information, however, may become unavailable because of the interrupted communications. UAV control algorithms based on deep reinforcement learning have achieved outstanding results in many fields. Here, we propose a cluster control method based on the Decomposed Multi-Agent Deep Deterministic Policy Gradient (DE-MADDPG) to rebuild a communication network for UAV clusters. The DE-MADDPG improves the framework of the traditional multi-agent deep deterministic policy gradient (MADDPG) algorithm by decomposing the reward function. We further introduce the reward reshaping function to facilitate the convergence of the algorithm in sparse reward environments. To address the instability of the state-space in the reinforcement learning framework, we also propose the notion of the virtual leader–follower model. Extensive simulations show that the success rate of the DE-MADDPG is higher than that of the MADDPG algorithm, confirming the effectiveness of the proposed method.

Collaborative Computation Offloading and Resource Allocation in Multi-UAV-Assisted IoT Networks: A Deep Reinforcement Learning Approach

In the fifth-generation (5G) wireless networks, Edge-Internet-of-Things (EIoT) devices are envisioned to generate huge amounts of data. Due to the limitation of computation capacity and battery life of devices, all tasks cannot be processed by these devices. However, mobile-edge computing (MEC) is a very promising solution enabling offloading of tasks to nearby MEC servers to improve quality of service. Also, during emergency situations in areas where network failure exists, unmanned aerial vehicles (UAVs) can be deployed to restore the network by acting as Aerial Base Stations and computational nodes for the edge network. In this article, we consider a central network controller who trains observations and broadcasts the trained data to a multi-UAV cluster network. Each UAV cluster head acts as an agent and autonomously allocates resources to EIoT devices in a decentralized fashion. We propose model-free deep reinforcement learning (DRL)-based collaborative computation offloading and resource allocation (CCORA-DRL) scheme in an aerial to ground (A2G) network for emergency situations, which can control the continuous action space. Each agent learns efficient computation offloading policies independently in the network and checks the statuses of the UAVs through Jain’s Fairness index. The objective is minimizing task execution delay and energy consumption and acquiring an efficient solution by adaptive learning from the dynamic A2G network. Simulation results reveal that our scheme through deep deterministic policy gradient, effectively learns the optimal policy, outperforming A3C, deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network and greedy-based offloading for local computation in stochastic dynamic environments.

Resilience analysis for reconfigurable network with cascading failures

A network is a topology composed of nodes and the edges of connecting nodes. The nodes interact with each other through edges and realize the function of network. The break of one or several nodes in the network will cause the failure of other nodes through the connection relationship between nodes and this phenomenon is known as cascading failure. The function of large network depends on network structure. When some nodes or edges fail, the network can restore and maintain certain functions by means of reconstruction and load distribution. Based on the cluster of unmanned aerial vehicle (UAV) and communication network, the cascading failure and reconfigurable features are considered in this study. By introducing the limitation of the maximum number of connections of nodes, a multi-state cascading failure rule based on information load distribution and a dynamic network reconnection rule based on probability are proposed, and the influence of the spread and propagation of cascading failure behavior on the invulnerability of the network are studied. Through simulation experiment design and analysis, several feasible suggestions to improve the invulnerability of UAV clusters are concluded.

A cluster UAV inspired honeycomb defense system to confront military IoT: a dynamic game approach

Internet of things (IoT) technology and unmanned aerial vehicles (UAV) cluster combat technology are likely to be the key technologies deployed on the future battlefield. The present research indicates that the IoT selects the optimal strategy according to the attacker’s strategy to maintain the IoT connection. Thereafter, an IoT technology and UAV cluster technology battlefield confrontation system model is established. This system model is then transformed into a dynamic game model of the attacker and the defender, mainly considering the impact of clustered drone attacks on military IoT connectivity and the impact of military IoT technology attacks on drone combat strategies and command centers. To improve the game model and its payment function, we considered factors such as the relative weight of the IoT disconnected sensor nodes, the cost of deploying new nodes, the loss of the hive, and the number of destroyed drones. Theoretical analysis and simulation results show that the impact of the UAV cluster attack on the IoT connectivity is reduced, and the number of disconnected sensors of the attacker is lower than the number before the optimization.

Identification and Suppression in Flying Synchronous Emergence of UAV Clusters

Vicsek model is used to simulate the moving behavior of a UAV (Unmanned Aerial Vehicle) cluster, and the flying synchronization process of a UAV cluster is observed by different emerging measurements and the cooperative process of a UAV cluster is interfered at the right time.

Application of blockchain in the cluster of unmanned aerial vehicles

With the considerable exploration of unmanned aerial vehicles in civilian and military fields, the data storage and transmission mechanism of unmanned aerial vehicles exhibit significant security limitations, which cannot meet the strict requirements for data security in civilian and military business scenarios. In recent years, blockchain technology has been tried to be applied to multiple fields. The features of blockchain includes decentralization, immutability, transparency and auditability, make transactions more secure and tamper proof. In view of the many excellent features of blockchain, in this paper, blockchain technology is introduced into the application of cluster of unmanned aerial vehicles, aiming to solve the problems of identity authentication, secure and reliable transmission of data between unmanned aerial vehicles, and access control of the data on the blockchain, thereby to enhance the security of the cluster of unmanned aerial vehicles and to ensure the integrity and security of data.

Exposing Spoofing Attack on Flocking-Based Unmanned Aerial Vehicle Cluster: A Threat to Swarm Intelligence

With the rapid development of wireless communication technology and intelligent mobile devices, unmanned aerial vehicle (UAV) cluster is becoming increasingly popular in both civilian and military applications. Recently, a swarm intelligence-based UAV cluster study, aiming to enable efficient and autonomous collaboration, has drawn lots of interest. However, new security problems may be introduced with such swarm intelligence. In this work, we perform the first detailed security analysis to a kind of flocking-based UAV cluster with 5 policies, an upgrade version of the well-known Boids model. Targeting a realistic threat in a source-to-destination flying task, we design a data spoofing strategy and further perform complete vulnerability analysis. We reveal that such design and implementation are highly vulnerable. After breaking through the authentication of ad hoc on-demand distance vector (AODV) routing protocol by rushing attack, an attacker can masquerade as the first-arrival UAV within a specific scope of destination and generate data spoofing of arrival status to the following UAVs, so as to interfere with their normal flying paths of destination arrival and cause unexpected arrival delays amid urgent tasks. Experiments with detailed analysis from the 5-UAV cluster to the 10-UAV cluster are conducted to show specific feature composition-based attack effect and corresponding average delay. We also discuss promising defense suggestions leveraging the insights from our analysis.

Research on the Multiobjective Optimization of Microwave Wireless Power Receiving in an Unmanned Aerial Vehicle Network

With the wide application of various wireless energy transmission technologies and unmanned aerial vehicle clusters in both production and life, the use of microwave wireless energy transmission to provide a real-time energy supply for an unmanned aerial vehicle network in flight has become an effective way to extend its working time. This paper focuses on the optimization of the energy transmission efficiency and cost in the microwave wireless power receiving process of an unmanned aerial vehicle network. Considering the overall energy transmission efficiency from the power supply terminal to the power receiving network and the cost of the wireless power transmission equipment of the network, we have established a multiobjective wireless power receiving optimization model including a microwave energy emission source and an unmanned aerial vehicle network that receives energy. The model is optimized to select the best wireless power access point and the number of wireless power receiving modules in a network node. In the case study, the optimization model is solved using an evolutionary algorithm, and the solution results verify the effectiveness and correctness of the model.

Optimization of UAV Flight Control Algorithm and Flight Simulation in Two-dimension

Large-scale Unmanned Aerial Vehicle (UAV) groups flight simulation in two-dimensional planes is commonly applied to UAV cluster mission planning algorithm design. In this paper, an UAV control algorithm optimized for two-dimensional planes is designed. The tandem structure of closed loops, as well as the control laws of L1 and Total Energy Control System (TECS), are transplanted to an UAV model that is simplified to a moving particle of a two-dimensional space, and UAV flight simulation is performed based on the flight control algorithm. Compared with the traditional 3D space flight simulation, it can save hardware resources and improve the simulation efficiency. Compare with the flight simulation based on the geometric method, on the premise of maintaining the dynamics basis, the trajectory and dynamics curves are closer to the actual flight results in 3D space, and the dynamics data onto the entire flight can be recorded. It has been verified that 50 UAVs need only 0.8s to perform 100 square-area snake-like search simulation experiments, and the fitting degree of the UAV’s flight curve to the three-dimensional space is greater than 80%. The two-dimensional plane flight control algorithm and flight simulation proposed in this paper provide a new simulation method for the design of UAV cluster mission planning algorithms, therefore can help promote the development of the subject.

Feasibility of reinforcement learning for UAV-based target searching in a simulated communication denied environment

Target searching is crucial in real-world scenarios such as search and rescue in disaster sites and battlefield target reconnaissance. Unmanned aerial vehicles (UAVs) are an ideal technical solution for target searching in large-scale and high-risk areas because they are agile, low cost, and able to collaborate and carry different sensors. In complex scenarios like battlefields, due to the lack of communication infrastructures and the intensive interference, UAVs often operate in communication denied environments. As a result, fast and reliable communication channels between UAVs and ground operators are difficult to establish. Thus, in such conditions, UAVs must be able to complete tasks autonomously and intelligently, without receiving real-time commands from the operators. With the rapid advances in artificial intelligence, reinforcement learning has shown potentiality for solving continuous decision problems. The target searching problem studied in this paper falls into this category and is suitable for adopting reinforcement learning technologies. However, the feasibility of reinforcement learning in UAV-based target searching in communication denied environments is not clear and, thus, requires in-depth investigations. As a pilot study in this direction, this paper models the target searching problem in communication denied and confrontation situations and proposes a simulation environment based on this model. Extensive experiments are conducted to answer the following questions. (1) Can reinforcement learning be applied in target searching by multi-UAVs in communication denied environments? (2) What are the advantages and disadvantages of different reinforcement learning algorithms in solving this problem? (3) How the degree of communication denial influences the performance of these algorithms? The current mainstream reinforcement learning technologies are adopted to perform simulations, whose results are analyzed quantitatively, leading to the following observations. (1) Reinforcement learning can effectively solve target searching problems for multi-UAVs in communication denied environments. (2) Compared with other algorithms, an autonomous decision-making UAV cluster based on a deep Q-network (DQN)exhibits the best problem-solving ability. (3) The algorithm performance changes with the degree of communication denial but remains largely stable when the communication condition varies.

Clustering optimization algorithm for large-scale unmanned aerial vehicle based on intelligent behavior of pigeons

In future air battlefield, clusters of large-scale unmanned aerial vehicle (UAV) will become the dominant force. The effective grouping and clustering of large-scale UAV cluster systems are necessary steps to complete combat tasks. Due to the limited communication constraints in the battlefield, UAVs cannot obtain comprehensive and effective global combat information. Thus, this paper proposed a large-scale UAV clustering optimization algorithm based on the intelligent behavior of pigeons. Also, this paper studied and analyzed the intelligent behavior of the flock of pigeons with excellent navigation ability and mapped the hierarchical network mechanism in the flight process of the pigeon flock into the pigeon-inspired optimization (PIO) algorithm. Hence, it solved the problem of incomplete information in a limited interactive environment. On one hand, it is more effective and direct to guide the pigeon flock from adjacent individuals during flight. Therefore, under the limited interaction condition, the global optimal information of the basic PIO algorithm is replaced by the optimal individual information within the interaction range. On the other hand, the central position renewal of the pigeon flock consists of three parts: inertia part, imitation part, and environmental impact part. In order to verify the effectiveness of the improved PIO algorithm in a limited interactive range, this paper adopted three algorithms to cluster three datasets. Simulation results reveal that the improved PIO algorithm achieved significant improvement in the optimal solution and the average optimal solution; notably, the computation time does not increase significantly, thus providing an effective solution for the clustering of UAV cluster systems in the actual combat environment.

UAV Cluster-Based Video Surveillance System Optimization in Heterogeneous Communication of Smart Cities

Video surveillance system is the integration of computers, networks, communications, and video CODEC, etc. Because of its distributed architecture, parallel image processing and ease of installation and expansion, it is widely used in many fields such as education, transportation and industry. However, there are some challenges of video surveillance applications in smart cities such as large scale of video events, low quality and big delay of video data transmission, and the loss of video surveillance data integrity. In order to solve the above problems, this paper designs a series of optimization algorithms and scheduling strategies based on Unmanned Aerial Vehicle (UAV) cluster. Firstly, we construct a full device coverage network with UAV cluster in heterogeneous communication environment of smart cities. Secondly, we formulate the scheduling problem of UAV cluster as bi-objective fragile bin packing problem, and design an optimal scheduling algorithm with constant approximation performance ratio. The simulation experimental results fully demonstrate the effectiveness, feasibility and robustness of the proposed solution in terms of system life cycle, video decodable frame rate, the ratio of UAV flight time to system life cycle, throughput and delay.

Dynamic Tasks Scheduling Model of UAV Cluster Based on Flexible Network Architecture

With the rapid growth of application demands and the real-time change of environmental situations, the defects of the UAV task network in adaptability, flexibility, and resilience are becoming more and more prominent. The current network architecture that the junction of points and lines is fixed cannot dynamically provide capacity requirements in real-time due to the failure nodes encountered in the Unmanned Aerial Vehicle (UAV) task scheduling process. To address this challenging issue, this paper proposes a flexible network architecture supporting dynamic fault-tolerant task scheduling model (DSM-FNA) for the UAV cluster. To be specific this paper resorts to super network theory, combining the management theory of flexible network and resilience network to carry out the organizational calculation on the model, and also draw upon linear transformation function to weight and stratify the capability value according to the ability requirement required by the task. Then, a flexible network architecture dynamic scheduling algorithm (FDSA) is proposed, and the substitution strategy is designed for the failure point, to realize the capability and dynamically adapt to the task. Finally, compared with the classical Max-Min algorithm and other algorithms, it is verified that the FDSA algorithm performs better dynamic adjustment for quick response in case of UAV cluster emergencies.

A Two-Layer Task Assignment Algorithm for UAV Swarm Based on Feature Weight Clustering

For the large-scale operations of unmanned aerial vehicle (UAV) swarm and the large number of UAVs, this paper proposes a two-layer task and resource assignment algorithm based on feature weight clustering. According to the numbers and types of task resources of each UAV and the distances between different UAVs, the UAV swarm is divided into multiple UAV clusters, and the large-scale allocation problem is transformed into several related small-scale problems. A two-layer task assignment algorithm based on the consensus-based bundle algorithm (CBBA) is proposed, and this algorithm uses different consensus rules between clusters and within clusters, which ensures that the UAV swarm gets a conflict-free task assignment solution in real time. The simulation results show that the algorithm can assign tasks effectively and efficiently when the number of UAVs and targets is large.

Low-Complexity Failed Element Diagnosis for Radar-Communication mmWave Antenna Array with Low SNR

The millimeter-wave (mmWave) antenna array plays an important role in the excellent performance of wireless sensors networks (WSN) or unmanned aerial vehicle (UAV) clusters. However, the array elements are easily damaged in its harsh working environment but hard to be repaired or exchanged timely, resulting in a serious decline in the beamforming performance. Thus, accurate self-diagnosis of the failed elements is of great importance. In previous studies, there are still significant difficulties for large-scale arrays under extremely low SNR. In this paper, a diagnosis algorithm with low complexity and high reliability for the failed elements is proposed, which is based on a joint decision of communication signal and sensing echoes. Compared with the previous studies, the complexity of the algorithm is reduced by the construction of low-dimensional feature vectors for classification, the decoupling of the degree of arrival (DOA) estimation and the failed pattern diagnosis, with the help of the sub-array division. Simulation results show that, under an ultra-low SNR of −12.5 dB for communication signals and −16 dB for sensing echoes, an accurate self-diagnosis with a block error rate lower than 8% can be realized. The study in this paper will effectively promote the long-term and reliable operation of the mmWave antenna array in WSN, UAV clusters and other similar fields.

A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming

Video streaming has become a kind of main information carried by Unmanned Aerial Vehicles (UAVs). Unlike single transmission, when a cluster of UAVs execute the real-time video shooting and uploading mission, the insufficiency of wireless channel resources will lead to bandwidth competition among them and the competition will bring bad watching experience to the audience. Therefore, how to allocate uplink bandwidth reasonably in the cluster has become a crucial problem. In this paper, an intelligent and distributed allocation mechanism is designed for improving users’ video viewing satisfication. Each UAV in a cluster can independently adjust and select its video encoding rate so as to achieve flexible uplink allocation. This choice relies neither on the existence of the central node, nor on the large amount of information interaction between UAVs. Firstly, in order to distinguish video service from ordinary data, a utility function for the overall Quality of Experience (QoE) is proposed. Then, a potential game model is built around the problem. By a distributed self-learning algorithm with low complexity, all UAVs can iteratively update their own bandwidth strategy in a short time until equilibria, thus achieving the total quality optimization of all videos. Numeric simulation results indicate, after a few iterations, that the algorithm converges to a set of correlation equilibria. This mechanism not only solves the uplink allocation problem of video streaming in UAV cluster, but also guarantees the wireless resource providers in distinguishing and ensuring network service quality.

Safe multi-cluster UAV continuum deformation coordination

This paper proposes a paradigm for coordination of multiple unmanned aerial vehicle (UAV) clusters in a shared motion space. UAVs are arranged in a finite number of teams each bounded by a leading triangle. Collective motion of each UAV cluster is managed by a continuum deformation defined by three leaders at the vertices of a leading triangle and followers contained within this triangle. Each triangular cluster can deform substantially to support maneuverability in constrained spaces. This paper specifies necessary conditions to guarantee obstacle avoidance as well as collision avoidance within and across all clusters operating in a shared motion space. Given initial and target configurations, an existing planner (A*) identifies the shortest coordinated leader UAV paths from initial to final configuration in a manner that satisfies safety constraints. An illustrative simulation case study is presented. Continuum deformation containment offers scalability in collision-free UAV motion planning not previously realized in the detect-and-avoid literature. The proposed multi-cluster coordination protocol also extends previous cooperative control to address detect-and-avoid (DAA) given multiple cooperative teams with different destinations.

Ready Player One: UAV-Clustering-Based Multi-Task Offloading for Vehicular VR/AR Gaming

With rapid development of unmanned aerial vehicle (UAV) technology, application of the UAVs for task offloading has received increasing interest in the academia. However, real-time interaction between one UAV and the mobile edge computing (MEC) node is required for processing the tasks of mobile end users, which significantly increases the system overhead and is unable to meet the demands of large-scale artificial intelligence (AI) based applications. To tackle this problem, in this article, we propose a new architecture for UAV clustering to enable efficient multi-modal multi-task task offloading. By the proposed architecture, the computing, caching and communication resources are collaboratively optimized using AI based decision-making. This not only increases the efficiency of UAV clusters, but also provides insight into the fusion of computation and communication.