UAV Swarm Research Articles

A Path-Planning Method for UAV Swarm under Multiple Environmental Threats

To weaken or avoid the impact of dynamic threats such as wind and extreme weather on the real-time path of a UAV swarm, a path-planning method based on improved long short-term memory (LSTM) network prediction parameters was constructed. First, models were constructed for wind, static threats, and dynamic threats during the flight of the drone. Then, it was found that atmospheric parameters are typical time series data with spatial correlation. The LSTM network was optimized and used to process time series parameters to construct a network for predicting atmospheric parameters. The state of the drone was adjusted in real time based on the prediction results to mitigate the impact of wind or avoid the threat of extreme weather. Finally, a path optimization method based on an improved LSTM network was constructed. Through simulation, it can be seen that compared to the path that does not consider atmospheric effects, the optimized path has significantly improved flightability and safety.

CoDetect: cooperative anomaly detection with privacy protection towards UAV swarm

CoDetect: cooperative anomaly detection with privacy protection towards UAV swarm

Collaborative target assignment problem for large-scale UAV swarm based on two-stage greedy auction algorithm

This paper introduces a collaborative allocation model designed for multiple UAVs and diverse targets in maritime combat situations. The model incorporates factors such as distance, angle, interception rate, and recognition rate to comprehensively represent the UAVs' overall damage advantage against targets. Given the complexity of real-world environments and real-time demands, large-scale UAV swarm missions necessitate swift and effective responses. To address this, the paper proposes a Two-Stage Greedy Auction Algorithm, enabling the rapid and efficient completion of cooperative strike tasks within large-scale UAV swarms while preventing deadlock occurrences. In the initial allocation stage, the entropy weight method is utilized to assess task advantages, ensuring a rational allocation criterion for various metrics during the strike process. Subsequently, to enhance the overall effective strike rate within all constraints, a reassignment algorithm is designed based on effective strike benefit indices and the initial assignment result. Simulation results demonstrate the algorithm's quick and stable running time in small-scale and large-scale scenarios.

Multiagent RL-Based Joint Trajectory Scheduling and Resource Allocation in NOMA-Assisted UAV Swarm Network

Multiagent RL-Based Joint Trajectory Scheduling and Resource Allocation in NOMA-Assisted UAV Swarm Network

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.

Doppler Characterization in LEO Satellite-Aided UAV Swarm Networks

Doppler Characterization in LEO Satellite-Aided UAV Swarm Networks

Dynamic UAV Swarm Collaboration for Multi-Targets Tracking Under Malicious Jamming: Joint Power, Path and Target Association Optimization

Dynamic UAV Swarm Collaboration for Multi-Targets Tracking Under Malicious Jamming: Joint Power, Path and Target Association Optimization

Modeling Wind and Obstacle Disturbances for Effective Performance Observations and Analysis of Resilience in UAV Swarms

UAV swarms have multiple real-world applications but operate in a dynamic environment where disruptions can impede performance or stop mission progress. Ideally, a UAV swarm should be resilient to disruptions to maintain the desired performance and produce consistent outputs. Resilience is the system’s capability to withstand disruptions and maintain acceptable performance levels. Scientists propose novel methods for resilience integration in UAV swarms and test them in simulation scenarios to gauge the performance and observe the system response. However, current studies lack a comprehensive inclusion of modeled disruptions to monitor performance accurately. Existing approaches in compartmentalized research prevent a thorough coverage of disruptions to test resilient responses. Actual resilient systems require robustness in multiple components. The challenge begins with recognizing, classifying, and implementing accurate disruption models in simulation scenarios. This calls for a dedicated study to outline, categorize, and model interferences that can be included in current simulation software, which is provided herein. Wind and in-path obstacles are the two primary disruptions, particularly in the case of aerial vehicles. This study starts a multi-step process to implement these disruptions in simulations accurately. Wind and obstacles are modeled using multiple methods and implemented in simulation scenarios. Their presence in simulations is demonstrated, and suggested scenarios and targeted observations are recommended. The study concludes that introducing previously absent and accurately modeled disruptions, such as wind and obstacles in simulation scenarios, can significantly change how resilience in swarm deployments is recorded and presented. A dedicated section for future work includes suggestions for implementing other disruptions, such as component failure and network intrusion.

Consensus-based virtual leader tracking algorithm for flight formation control of swarm UAVs

Consensus-based virtual leader tracking algorithm for flight formation control of swarm UAVs

Over-the-Air Federated Transfer Learning Over UAV Swarm for Automatic Modulation Recognition in V2X Radio Monitoring

Over-the-Air Federated Transfer Learning Over UAV Swarm for Automatic Modulation Recognition in V2X Radio Monitoring

Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics

Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics

Collaborative strategy research of target tracking based on natural intelligence by UAV swarm

Regarding the regional area target collaborative tracking problem widely existing in intelligent scenarios, this paper built a distributed UAV swarm framework inspired by natural intelligence to heighten intricate missions’ efficiency. Also, a standoff collaboratively continuous tracking strategy was proposed based on a lateral guidance law with an improved Reference Point Guidance (RPG) and a longitudinal guidance law with an improved phase collaboration. Under an uncertain environment, this framework used an improved bat algorithm (IBA) to optimize the speed allocation of the UAV swarm’s online control strategy with information consensus estimation. Compared with a case without the designed transformation, statistically, the results demonstrate that the framework operates efficiently and robustly in phase error convergence, swarm flight distance, and fuel consumption, where a dynamic target exists.

A Communication System for Dynamic Leader Selection in Distributed UAV Swarm Architecture

A Communication System for Dynamic Leader Selection in Distributed UAV Swarm Architecture

NISAC-EKF: An integrated localization deployment algorithm for UAV swarms based on NARX and EKF

The mobility of both unmanned aerial vehicles (UAVs) and users, often results in frequent interruptions of communication links, affecting network communication performance. This problem can be solved through reasonable UAV location deployment. This paper proposes an integrated NISAC-EKF algorithm to enhance the localization deployment capability of UAV swarms. Initially, the nonlinear autoregressive exogenous model (NARX) is trained on crowd location data in a communication dataset, and integrated sensing and communication (ISAC) techniques are used to achieve communication-aided sensing. Subsequently, the extended Kalman filter (EKF) is enhanced by introducing probabilistic switching weights into the prediction of location errors, using interactive multiple model. Finally, the sequence predicted by NARX guides the EKF inputs, and the movement direction of the ground crowd adjusts the UAV location for effective demand tracking. Simulation results demonstrated that the NISAC-EKF algorithm ensures excellent performance in terms of network communication capacity and communication rate, even in the presence of varying ground demand.

PUF-based dynamic secret-key strategy with hierarchical blockchain for UAV swarm authentication

The evolving UAV swarm flight environment exacerbates the difficulty of secure message delivery within the swarm. Physical Unclonable Functions (PUFs) have the potential to provide lightweight physical identities to UAV nodes. Nevertheless, the explicit storage of a private key within each UAV device invariably results in privacy leakage arising from physical hijacking. Therefore, to accomplish unified management and decentralized storage of identity informations for UAVs, as well as to simultaneously realize the dynamic updating of private key, we propose a PUF-based Dynamic Secret-key strategy with Hierarchical Blockchain (PDSHB) for UAV swarm authentication. Specifically, the master blockchain ensures that all UAVs’ registration information is tamper-proof, while the dynamic sub-blockchain synchronizes the UAV information within the swarm. Meanwhile, we have developed a more reliable and efficient PUF-based dynamic secret-key strategy that eliminates the need to store any secret information within UAVs, effectively mitigating the risk of key leakage resulting from physical attacks. Protocol security is demonstrated through formal security proofs, informal security analysis, and AVISPA security checking. Performance experiments based on Raspberry Pi 4B and FPGA-based F450 UAVs also show that the proposed protocol can deal with various security threats of dynamic swarms of UAVs and reduce computational and communication overheads during authentication.

Distributed observer-based close formation control for UAV swarm under outside disturbances and wake interferences

This work investigates the issue on distributed close formation tracking control for a class of fixed-wing UAVs under prescribed performance, outside disturbances, and wake interferences. Firstly, by considering the existence of uncertain terms, a second-order kinematical model is established for each UAV system. Secondly, in order to ensure the safe flight of each UAV, an improved prescribed performance method is proposed to constrain the tracking error within the given restraint. Thirdly, based on the exogenous models of the outside disturbance and wake interference, two kinds of distributed disturbance observers are respectively presented to derive their estimations, in which the approximations of the uncertainties are included. Fourthly, according to the safe distance, estimations of the disturbances and uncertainties, and tracking error, a distributed cooperative controller is designed and an overall closed-loop system is deduced on the basis of Kronecker product. Then, a sufficient condition is established to ensure the asymptotical stability for the close-loop system and later, an easy-to-test method of checking the controller gains, observer ones, and adaptive law is presented. Finally, the effectiveness and superiorities of the proposed control strategy are verified by resorting to a simulated example.

Area Surveillance With Low Detection Probability Using UAV Swarms

Area Surveillance With Low Detection Probability Using UAV Swarms

METHODOLOGICAL FOUNDATIONS OF OPTIMAL SWARM MANAGEMENT OF UAVS IN ENERGY CROP SPRAYING WITH AGROCHEMICAL AGENTS

The work is devoted to research on the optimal management of a UAV swarm when spraying energy crops with toxic chemicals. The relevance of the work lies in the fact that, at present, the solution of chemical plant protection tasks using UAVs is based on the individual experience of a human operator, who manages a swarm of UAVs, as a rule, in a non-optimal way with significant time and cost costs. The purpose of the article is to develop methodological principles for optimal management of a UAV swarm when spraying energy crops with agrochemicals. In order to achieve the goal, the following tasks must be solved: the formulation of the task of optimizing the management of a UAV swarm is substantiated, taking into account the time and cost constraints for the performance of chemical plant protection tasks, a method of optimal management of a UAV swarm when spraying energy crops with toxic chemicals and foliar feeding is developed, which provides a solution tasks to maximize the number of sprayed plants in each field section under time and cost constraints, a structural diagram of the solution to the task of optimizing the UAV swarm management was developed for the high-quality practice of chemical plant protection tasks. The scientific novelty of the work consists in the application of the dynamic programming method in the distribution and management of a swarm of UAVs on each site when spraying energy crops with agrochemicals. The practical value of the work lies in the development of a structural scheme for solving the problem of optimizing the control of a UAV swarm, with the help of which the optimal number of UAVs in a given time sequence is calculated in special software. automated operator's workplace for high-quality performance of chemical protection and plant nutrition tasks.

Improving Connectivity in 6G Maritime Communication Networks With UAV Swarms

Improving Connectivity in 6G Maritime Communication Networks With UAV Swarms