Swarm Of Unmanned Aerial Vehicles Research Articles

ASMTP: Anonymous secure messaging token‐based protocol assisted data security in swarm of unmanned aerial vehicles

AbstractSwarm of Unmanned Aerial Vehicles (UAVs) broaden the field of application in various fields like military surveillance, crop monitoring in agriculture, combat operations, etc. Unfortunately, they are becoming increasingly susceptible to security attacks, such as jamming, information leakage and spoofing, as they become more common and in more demand. So, there is a wider need for UAVs, which requires the design of strong security procedures to fend off such attacks and security dangers. Even though several studies focused on security aspects, many questions remain unanswered, particularly in the areas of secure UAV‐to‐UAV communication, support for perfect forward secrecy and non‐repudiation. In a battle situation, it is extremely important to close these gaps. The security requirements for the UAV communication protocol in a military setting were the focus of this study. In this paper, we present the issues faced by the UAV swarm, especially during military surveillance operations. To secure the communication link in UAV, a new protocol for UAV Swarm communication is proposed with anonymous secure messaging token‐based protocol (ASMTP). The proposed protocol secures UAV‐to‐base station communication and safeguards the metadata of the sender and receiver nodes. The proposed model maintains the confidentiality, integrity and availability of data in the UAV Swarm and achieves robustness. In addition, it provides a different strategy for the cybersecurity gaps in the swarm of UAVs during military surveillance and combat operations.

Heterogeneous Unmanned Swarm Formation Containment Control Based on Reinforcement Learning

In response to the problem of time-varying spherical formation control for a heterogeneous unmanned aerial vehicle (UAV) swarm system with dynamic uncertainty in the system model, this paper proposes an optimal distributed formation containment control method based on reinforcement learning. By combining the time-varying formation containment vector design with the distributed predefined time observer and constructing the augmented system of a multi-quadrotor UAV system and an observer, the problem of time-varying formation containment control in heterogeneous swarm systems is transformed into a stabilization problem. By introducing a value function with a discount factor, the stabilization problem of a heterogeneous UAV swarm system is transformed into an optimal control problem. Using the “actor-critic” neural network, combined with an off-policy reinforcement learning algorithm and distributed control methods, the solution to the formation containment controller is achieved in a data-driven manner. The stability of distributed observer and formation containment controller as well as the convergence of reinforcement learning algorithms are demonstrated using Lyapunov and related theory. The numerical simulation results demonstrate that the observation error of the designed distributed observer converges within a predefined time of 0.1 s, while the overall formation tracking error of the heterogeneous swarm converges within 2.74 s, thereby further validating the effectiveness and superiority of the designed control scheme.

Network Analysis of Decentralized Fault-Tolerant UAV Swarm Coordination in critical Missions

Unmanned Aerial Vehicles (UAVs) have gained prominence across various sectors for their versatile applications. While their advantages are evident, addressing concerns associated with their deployment is essential to ensure reliability. This study presents an innovative approach for coordinating a group of UAVs in aerial survey missions. The decentralized strategy presented in this article allow UAVs to self-organize into linear formation, optimize their coverage paths, and adapt to agent failures, thereby ensuring efficient and adaptive mission execution. The strategy has been tested and validated on two different platforms: the inter-UAV communication performance is evaluated on NS-3 simulator to measure metrices such as packet delivery ratio, throughput, delay, and routing overhead within the UAV swarms, while mission efficiency and fault tolerance is analyzed on ROS framework, and visualized on Gazebo simulator with real-time parameters. Through experimental results, we show that, after proper tuning of control parameters, the approach succeeds in flock formation with high level of fault tolerance, offering higher efficiency in terms of mission time, transmission delay, packet delivery rate, and control overhead, when compared to the benchmark approaches.

Onboard cooperative relative positioning system for Micro-UAV swarm based on UWB/Vision/INS fusion through distributed graph optimization

In recent years, the swarm of micro Unmanned Aerial Vehicles (micro-UAVs) is becoming a research hotspot. As the basis of swarm autonomous flight, traditional relative positioning methods typically rely on external base stations or onboard sensors of high power consumption, the vast majority of which are not feasible on resource-constrained micro-UAVs flying in a complex environment. This article proposes a lightweight relative positioning system implemented on a micro-UAV platform weighing 150 g. We have developed a distributed graph optimization (DGO) scheme fusing the results of the onboard Ultra-Wideband (UWB) module, camera, and inertial sensors. A Delay Extended Kalman Filter (DEKF) is utilized to mitigate communication delays and enhance accuracy. This scheme enables micro-UAVs to collaboratively compute the relative position graph in real-time. Through real-world experiments, our system has achieved a relative positioning accuracy of 0.167 m, exhibiting significant potential for future swarm applications.

UAV swarm communication reliability based on a comprehensive SINR model

Communication reliability is one of the most important factors in ensuring the safe operation of unmanned aerial vehicles (UAVs). The reliability of wireless communication for a UAV swarm is typically characterized by the signal-to-interference-and-noise ratio (SINR). However, previous work on UAV swarm communication reliability did not use a comprehensive SINR model. In this paper, we derived two novel closed-form approximations – lognormal and generalized beta prime (GBP) - of UAV swarm communication reliability based on a comprehensive SINR model. The model includes shadowing, multipath fading, the dependence of fading on external factors, which are the probability of line-of-sight (LoS) and the physical environment, as well as all possible interference within the system, which may be from the UAVs and the ground control station (GCS). For typical swarm heights between 60 – 300 m and up to 32 UAVs, comparisons of the approximate and simulated reliabilities for UAV swarms with a radius up to a critical value (which increases with the number and transmit power of the UAVs) show that only the lognormal approximation is accurate for all uplink and downlink communications, up to a critical horizontal distance from the GCS (which increases with height). Benchmarking of the lognormal approximation shows that neglecting either shadowing or multipath fading leads to high approximation errors. An example of how the lognormal approximation can be used to evaluate and maintain the communication reliability of a UAV swarm during deployment is given. Furthermore, it can be used to derive other SINR-dependent performance metrics, such as ergodic capacity and symbol error rate, which are useful in UAV network design and monitoring.

DCFH: A dynamic clustering approach based on fire hawk optimizer in flying ad hoc networks

In flying ad hoc networks (FANETs), unmanned aerial vehicles (UAVs) communicate with each other without any fixed infrastructure. Because of frequent topological changes, instability of wireless communication, three-dimensional movement of UAVs, and limited resources, especially energy, FANETs deal with many challenges, especially the instability of UAV swarms. One solution to address these problems is clustering because it maintains network performance and increases scalability. In this paper, a dynamic clustering scheme based on fire hawk optimizer (DCFH) is proposed for FANETs. In DCFH, each cluster head calculates the period of hello messages in its cluster based on its velocity. Then, a fire hawk optimizer (FHO)-based dynamic clustering operation is carried out to determine the role of each UAV (cluster head (CH) or cluster member (CM)) in the network. To calculate the fitness value of each fire hawk, a fitness function is suggested based on four elements, namely the balance of energy consumption, the number of isolated clusters, the distribution of CHs, and the neighbor degree. To improve cluster stability, each CH manages the movement of its CMs and adjusts it based on its movement in the network. In the last phase, DCFH defines a greedy routing process to determine the next-hop node based on a score, which consists of distance between CHs, energy, and buffer capacity. Finally, DCFH is simulated using the network simulator version 2 (NS2), and its performance is compared with three methods, including the mobility-based weighted cluster routing scheme (MWCRSF), the dynamic clustering mechanism (DCM), and the Grey wolf optimization (GWO)-based clustering protocol. The simulation results show that DCFH well manages the number of clusters in the network. It improves the cluster construction time (about 55.51%), cluster lifetime (approximately 11.13%), energy consumption (about 15.16%), network lifetime (about 2.6%), throughput (approximately 3.9%), packet delivery rate (about 0.61%), and delay (approximately 14.29%). However, its overhead is approximately 8.72% more than MWCRSF.

Огляд анатомії ройових систем. Проблема управління

The article reveals modern approaches to building a swarm system with an emphasis on the swarm of unmanned aerial vehicles. The main components of the architecture of the swarming system were considered: swarming model, communication network, control system. The difference between the principles of controlling individual UAV and a large group (swarm) of UAVs is considered. The importance and necessity of further development of methods and systems for managing swarm systems is outlined.

Огляд анатомії ройових систем. Проблема управління

The article reveals modern approaches to building a swarm system with an emphasis on the swarm of unmanned aerial vehicles. The main components of the architecture of the swarming system were considered: swarming model, communication network, control system. The differ-ence between the principles of controlling individual UAV and a large group (swarm) of UAVs is considered. The importance and necessity of further development of methods and systems for managing swarm systems is outlined.

Graph-Based Multi-agent Reinforcement Learning for Large-Scale UAVs Swarm System Control

In this study, a novel graph-embedding technique based on a graph neural network (GNN) is proposed to identify the topology in the motion of a unmanned aerial vehicles (UAV) swarm and quickly obtain local information around each agent. We also propose a model reference reinforcement learning method to learn the potential field function and determine an appropriate strategy for each agent that can satisfy the requirements of collaborative motion and obstacle avoidance for large-scale UAV swarms. First, a new swarm structure is proposed to provide reserved maneuvering space for UAVs during flight. In addition, a method was proposed to encode the obstacle avoidance behavior of multiple UAVs in a continuous space into spatial maps. A graph attention mechanism (GAT) structure based on local information was proposed to obtain dynamic graph information, and each individual output action was obtained according to the current state information. To improve the training effect, this method can restrain the UAV group while maintaining the formation and preventing collisions among the UAV. Second, a new distributed control algorithm based on multi-agent reinforcement learning (MARL) is proposed by learning the potential field function using local information obtained by a GNN. Each individual can repel and cooperate with the target within a short range and attract objects over a long distance. Finally, simulation results demonstrate the effectiveness and superiority of the proposed method, which has great potential for application in online autonomous collaboration.

Integrated Host- and Content-Centric Routing for Efficient and Scalable Networking of UAV Swarm

Efficient and scalable networking is a key enabler of the Unmanned Aerial Vehicle (UAV) swarms, wherein multiple UAVs cooperatively execute complicated tasks. Despite the intermittent connections due to the UAV mobility, stable paths may exist temporally in periods like formation keeping, which is rarely considered or utilized in existing UAV routing designs. In this paper, we propose an integrated host- and content-centric routing (IHCR) mechanism to harness the advantages of both routing mechanisms. Specifically, the routing information of stable paths is reused in a host-centric manner to reduce the flooding for path exploring. In addition, the route failure detection and re-routing are content-centric to adjust to the topology dynamics. The challenges lie in the inherent contradiction between host-centric and content-centric routing mechanisms (e.g., naming spaces) and the tradeoff between path reusing and re-routing. To overcome these challenges, we appropriately incorporate node names into content names and then fully exploit reusable paths via time-based route failure detection and delayed forwarding. Packet-level simulation results show that IHCR increases the packet delivery ratio by 60.1%, and enlarges the achievable network scale by 4.2 times compared to state-of-the-art routing mechanisms.

Enhancing wildfire propagation model predictions using aerial swarm-based real-time wind measurements: A conceptual framework

The dynamic behaviour of wildfires is mainly influenced by weather, fuel, and topography. Based on fundamental conservation laws involving numerous physical processes and large scales, atmospheric models require substantial computational resources. Therefore, coupling wildfire and atmospheric models is impractical for high resolutions. Instead, a static atmospheric wind field is typically input into the wildfire model, either as boundary conditions on the control surface or distributed over the control volume. Wildfire propagation models may be (i) data-driven; (ii)theoretical; or (iii)mechanistic surrogates. Data-driven models are beyond the scope of this paper. Theoretical models are based on conservation laws (species, energy, mass, momentum) and are, therefore, computationally intensive; e.g. the Fire Dynamics Simulator (FDS). Mechanistic surrogate models do not closely follow fire dynamics laws, but related laws observed to make predictions more efficiently with sufficient accuracy; e.g. FARSITE, and FDS with the Level Set model (FDS-LS). Whether theoretical or mechanistic surrogate, these wildfire models may be coupled with or decoupled from wind models (e.g. Navier-Stokes equations). Only coupled models account for the effect of the fire on the wind field. In this paper, a series of simulations of wildfire propagation on grassland are performed using FDS-LS to study the impact of the fire-induced wind on the fire propagation dynamics. Results show that coupling leads to higher Rates of Spread (RoS), closer to those reported from field experiments, with increasing wind speeds and higher terrain slopes strengthening this trend. Aiming to capture the fire–wind interaction without the hefty cost of solving Navier-Stokes equations, a conceptual framework is proposed: 1) a swarm of unmanned aerial vehicles measure wind velocities at flight height; 2) the wind field is constructed with the acquired data; 3) the high-altitude wind field is mapped to near-surface, and 4) the near-surface wind field is fed into the wildfire model periodically. A series of simulations are performed using an in-house decoupled physics-based reduced-order fire propagation model (FireProM-F) enhanced by wind field “measurements”. In this proof of concept, wind velocities are not measured but extracted from physics-based Large Eddy Simulations taken as ground truth. Unsurprisingly, higher measurement frequencies lead to more accurate and realistic predictions of the propagating fire front. An initial attempt is made to study the effect of wind measurement uncertainty on the model predictions by adding Gaussian noise. Preliminary results show that higher noise leads to the fire front displaying more irregular shapes and slower propagation.

UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering

When an unmanned aerial vehicles (UAV) swarm is used for edge computing, and high-speed data transmission is required, accurate tracking of the UAV swarm’s centroid is of great significance for the acquisition and synchronization of signal demodulation. Accurate centroid tracking can also be applied to accurate communication beamforming and angle tracking, bringing about a reception gain. Group target tracking (GTT) offers a suitable framework for tracking the centroids of UAV swarms. GTT typically involves accurate modeling of target maneuvering behavior and effective state filtering. However, conventional coordinate-uncoupled maneuver models and multi-model filtering methods encounter difficulties in accurately tracking highly maneuverable UAVs. To address this, an innovative approach known as 3DCDM-based GRU-MM is introduced for tracking the maneuvering centroid of a UAV swarm. This method employs a multi-model filtering technique assisted by a gated recurrent unit (GRU) network based on a suitable 3D coordinate-coupled dynamic model. The proposed dynamic model represents the centroid’s tangential load, normal load, and roll angle as random processes, from which a nine-dimensional unscented Kalman filter is derived. A GRU is utilized to update the model weights of the multi-model filtering. Additionally, a smoothing-differencing module is presented to extract the maneuvering features from position observations affected by measurement noise. The resulting GRU-MM method achieved a classification accuracy of 99.73%, surpassing that of the traditional IMM algorithm based on the same model. Furthermore, our proposed 3DCDM-based GRU-MM method outperformed the Singer-KF and 3DCDM-based IMM-EKF in terms of the RMSE for position estimation, which provides a basis for further edge computing.

Affine Formation Maneuver Control for Multi-Agent Based on Optimal Flight System

The use of affine maneuver control to maintain the desired configuration of unmanned aerial vehicle (UAV) swarms has been widely practiced. Nevertheless, the lack of capability to interact with obstacles and navigate autonomously could potentially limit its extension. To address this problem, we present an innovative formation flight system featuring a virtual leader that seamlessly integrates global control and local control, effectively addressing the limitations of existing methods that rely on fixed configuration changes to accommodate real-world constraints. To enhance the elasticity of an algorithm for configuration change in an obstacle-laden environment, this paper introduces a second-order differentiable virtual force-based metric for planning local trajectories. The virtual field comprises several artificial potential field (APF) forces that adaptively adjust the formation compared to the existing following control. Then, a distributed and decoupled trajectory optimization framework that considers obstacle avoidance and dynamic feasibility is designed. This novel multi-agent agreement strategy can efficiently coordinate the global planning and local trajectory optimizations of the formation compared to a single method. Finally, an affine-based maneuver approach is employed to validate an optimal formation control law for ensuring closed-loop system stability. The simulation results demonstrate that the proposed scheme improves track accuracy by 32.92% compared to the traditional method, while also preserving formation and avoiding obstacles simultaneously.

Metropolis criterion pigeon-inspired optimization for multi-UAV swarm controller

This paper presents a new multiple unmanned aerial vehicle swarm controller based on Metropolis criterion. This paper presents the design of a controller, utilizing the improved Metropolis criterion pigeon-inspired optimization (IMCPIO) and proportional-integrational-derivative (PID) algorithms, and conducts comparative experiments. Simulation outcomes demonstrate the enhanced performance of the multi-unmanned aerial vehicle formation controller, which is based on IMCPIO, when compared to the basic pigeon-inspired optimization (PIO) algorithm and the genetic algorithm. The IMCPIO algorithm for the energy difference discrimination makes it a faster convergence and more stable effective optimization. Hence, the controller introduced in this study proves to be both practical and resilient.

Distributed time-varying optimization with coupled constraints: Application in UAV swarm predefined-time cooperative consensus

This paper addresses the global optimal consensus problem with time-varying objective functions and state-coupled inequality constraints. By integrating the predefined-time consensus and prediction-correction schemes, a distributed optimization protocol is proposed for the cooperative interception scenarios involving the unmanned aerial vehicle (UAV) swarm. Specifically, the δ-exact penalty method is utilized to eliminate the coupled constraints. A prediction-correction term with the penalty function is then established to track the trajectory of the time-varying optimal solution. Additionally, an edge-based predefined-time consensus term is introduced to facilitate the state consensus among UAVs. Under the undirected and connected topology, the proposed protocol ensures the predefined-time state consensus and asymptotically minimizes the sum of individual convex objective functions. Furthermore, the protocol is extended to other scenarios involving switching topology, directed detail-balanced topology, and external disturbances. Numerical simulations illustrate that the UAV swarm can form the predesigned interception formation with a predefined-time frame and effectively track the target swarm.

MADRL-based UAV swarm non-cooperative game under incomplete information

Unmanned Aerial Vehicles (UAVs) play increasing important role in modern battlefield. In this paper, considering the incomplete observation information of individual UAV in complex combat environment, we put forward an UAV swarm non-cooperative game model based on Multi-Agent Deep Reinforcement Learning (MADRL), where the state space and action space are constructed to adapt the real features of UAV swarm air-to-air combat. The multi-agent particle environment is employed to generate an UAV combat scene with continuous observation space. Some recently popular MADRL methods are compared extensively in the UAV swarm non-cooperative game model, the results indicate that the performance of Multi-Agent Soft Actor-Critic (MASAC) is better than that of other MADRL methods by a large margin. UAV swarm employing MASAC can learn more effective policies, and obtain much higher hit rate and win rate. Simulations under different swarm sizes and UAV physical parameters are also performed, which implies that MASAC owns a well generalization effect. Furthermore, the practicability and convergence of MASAC are addressed by investigating the loss value of Q-value networks with respect to individual UAV, the results demonstrate that MASAC is of good practicability and the Nash equilibrium of the UAV swarm non-cooperative game under incomplete information can be reached.

Knowledge graph completion assisted graph neural network for spectrum resource optimization of UAV swarm

Unmanned aerial vehicles (UAVs) are playing an increasing critical role in industrial, agricultural and military Scenario. However, the energy consuming cannot be ignored, which constrains the flying time and quality of service (QoS), especially in large UAV swarm. In this paper, a knowledge graph completion assisted graph neural network (KGC-GNN) is proposed for UAV swarm aiming at optimizing the spectrum resource to improve the energy efficiency (EE). The proposed network consists of two parts: a graph attention network based on relational coding (GATRC) encoder and a task-oriented decoder based on InteractE model named CP-IE. The further simulation demonstrates that our approach can attain near-optimal spectrum resource optimization scheme with higher energy efficiency and achieve rapid convergence with the channel distribution information (CDI), UAV and environment parameters.

Graphene Devices for Aerial Wireless Communications at THz

This paper investigates the state-of-the-art of graphene-based technologies for the prospective use cases of end-to-end terahertz (THz) communication systems, such as industrial Internet of Things (IoT) applications and unmanned aerial vehicles (UAVs). THz communications offer ultra-high throughput and enhanced sensing capabilities, enabling advanced applications like UAV swarms and integrated sensing, localization, and mapping. The potential of wireless THz communication can be unlocked by graphene technology. Graphene, owing to its remarkable electrical, thermal, and mechanical properties, emerges as a promising candidate for a multitude of applications in aerial wireless communications in the THz band, including high-speed electronic devices, tunable metamaterials, and innovative antennas. However, reliable tools for the simulation-based design of graphene components, able to account for the fabrication-related uncertainties, are still missing. This paper presents the envisaged possibilities of wireless communications in THz bands, overviews graphene devices for RF applications at THz, and discusses the open issues of modelling THz devices and THz channel.

See farther and more: a master-slave UAVs based synthetic optical aperture imaging system with wide and dynamic baseline.

An open challenge remained in designing an optical system to capture the aerial image with a wide field of view (FoV) and high resolution. The optical system of one camera from a single unmanned aerial vehicle (UAV) can hardly promise the FoV and resolution. The conventional swarm UAVs can form the camera array with a short or fixed baseline. They can capture the images with a wide FoV and high resolution, but the cost is the requirement of many UAVs. We aim to design a camera array with a wide and dynamic baseline to reduce the demand for UAVs to organize a synthetic optical aperture. In this thought, we propose a master-slave UAVs-based synthetic optical aperture imaging system with a wide and dynamic baseline. The system consists of one master UAV and multiple slave UAVs. Master and slave UAVs provide the global and local FoVs, respectively, and improve the efficiency of image acquisition. In such a system, fusing UAV images becomes a new challenge due to two factors: (i) the small FoV overlap of slave UAVs and (ii) the gap in resolution scale from slave to master UAV images. To deal with it, a coarse-to-fine stitching method is proposed to stitch up the multi-view images into one to obtain a wide FoV with high resolution. A video stabilization method has also been designed for the proposed imaging system. Challenges caused by wide and dynamic baselines can thus be solved by the above methods. Actual data experiments demonstrate that the proposed imaging system achieves high-quality imaging results.

Deployment of a UAV swarm-based LiFi network in the obstacle-ridden environment: algorithms of finding the path for UAV placement

The subject of this study is unmanned aerial vehicle (UAV)-based wireless networks in an obstacle-ridden environment. The aim of this study is to develop methods and software to ensure reliable LiFi communication using swarm UAVs in an obstacle-ridden environment. The objectives are as follows: 1) to describe the problem of providing a reliable UAV swarm-based LiFi network, requirements for the composition and use of UAVs, and assumptions; 2) to develop the methodology for solving research tasks; 3) to develop the method and algorithms for solving the problem, considering the requirements, assumptions, and practical limitations; 4) to explore the algorithms by developing software for modeling and searching for rational UAV placement to ensure the required UAV-based LiFi network characteristics; 5) to provide experiments and illustrative examples of the developed tool’s application. The following results were obtained. 1) The requirements for the composition and use of UAVs for creating LiFi networks, as well as assumptions and limitations for the methodology development and research task solving. 2) An obstacle avoidance method using the left and right angles algorithm. 3) A method for obstacle avoidance using the controlled waterfall algorithm. 4) A software tool for modeling and searching for rational UAV placement to ensure the required LiFi network characteristics. The tool allows route construction under obstacles in 2D space and a comparison of the developed algorithms for various variants of obstacle placement. Conclusions. The main contribution of this research is a set of methods, algorithms, and software tools for providing communications between two points using LiFi technologies and a swarm of UAVs supporting these communications as transmitters in conditions of mechanical obstacles.