Swarm Of Aerial Vehicles Research Articles

A configuration maintenance and cooperative navigation method for UAV swarms under intermittent GNSS conditions

PurposeIn response to the challenges of maintaining the configuration and navigation stability of low-cost unmanned aerial vehicle swarms under intermittent global navigation satellite system (GNSS) signal conditions, this study aims to introduce a fast network topology generation algorithm and a hybrid covariance filter.Design/methodology/approachFirst, using spatially stable structures and the principle of three-sphere intersection, connectivity between nodes is rapidly generated, ultimately forming a network topology with tetrahedrons as the basic unit. This ensures the stability of the configuration. Subsequently, a problem arises from the improper distribution of internal confidence within the system when some nodes are connected to GNSS, whereas others rely solely on ranging. In response, a hybrid covariance method with independent relative and absolute covariance matrices is proposed, which can improve the overall navigation precision of the swarm.FindingsSimulation results show that the approach achieves rapid convergence of relative positioning errors to less than 0.5 m for internode distances over 100 m. When one, two and three anchor nodes are accessed, the positioning accuracy of the proposed method is improved by 31.59 %, 64.53 % and 64.48 %, respectively, compared with the existing methods.Originality/valueThe proposed method can stabilize configurations and improve overall positioning accuracy, providing support for addressing distributed navigation issues in intermittent GNSS signals.

An Intent Recognition Method for Aerial Swarm Based on Attention Pooling Mechanism

In the realm of unmanned aerial vehicle (UAV) swarm intent recognition, conventional approaches predominantly focus on the attributes derived from singular targets at discrete instances. This trend leads to a significant limitation: the inability to effectively harness and capture the collective feature information of the entire swarm over temporal sequences. To address this gap, this study introduces a comprehensive end-to-end UAV swarm intent recognition approach. Initially, this method utilizes the distance threat coefficient and angular threat coefficient between UAVs to construct the graphical structural representation of the UAV swarm. Subsequently, an innovative deep learning framework, designated as attention-pool based on graph attention network and long short-term memory, which integrates a graph attention network, a novel graph pooling strategy, and a long short-term memory, is developed. This architecture can process the graphically structured data derived from the swarm modeling and accurately deduce the collective intent. Through experimental validation and analyses against existing methodologies, as well as ablation studies, it is evidenced that the model outperforms state-of-the-art methods in terms of accuracy of intent recognition.

Advanced Path Planning for UAV Swarms in Smart City Disaster Scenarios Using Hybrid Metaheuristic Algorithms

In disaster-stricken areas, rapid restoration of communication infrastructure is critical to ensuring effective emergency response and recovery. Swarm UAVs, operating as mobile aerial base stations (MABS), offer a transformative solution for bridging connectivity gaps in environments where the traditional infrastructure has been compromised. This paper presents a novel hybrid path planning approach combining affinity propagation clustering (APC) with genetic algorithms (GA), aimed at maximizing coverage, and ensuring quality of service (QoS) compliance across diverse environmental conditions. Comprehensive simulations conducted in suburban, urban, dense urban, and high-rise urban environments demonstrated the efficacy of the APC-GA approach. The proposed method achieved up to 100% coverage in suburban settings with only eight unmanned aerial vehicle (UAV) swarms, and maintained superior performance in dense and high-rise urban environments, achieving 97% and 93% coverage, respectively, with 10 UAV swarms. The QoS compliance reached 98%, outperforming benchmarks such as GA (94%), PSO (90%), and ACO (88%). The solution exhibited significant stability, maintaining consistently high performance, highlighting its robustness under dynamic disaster scenarios. Mobility model analysis further underscores the adaptability of the proposed approach. The reference point group mobility (RPGM) model consistently achieved higher coverage rates (95%) than the random waypoint model (RWPM) (90%), thereby demonstrating the importance of group-based mobility patterns in enhancing UAV deployment efficiency. The findings reveal that the APC-GA adaptive clustering and path planning mechanisms effectively navigate propagation challenges, interference, and non-line-of-sight (NLOS) conditions, ensuring reliable connectivity in the most demanding environments. This research establishes the APC-GA hybrid as a scalable and QoS-compliant solution for UAV deployment in disaster response scenarios. By dynamically adapting to environmental complexities and user mobility patterns, it advances state-of-the-art emergency communication systems, offering a robust framework for real-world applications in disaster resilience and recovery.

SYNERGISTIC CONFLICT-FREE UAV SWARM MOVEMENT MODELING USING METAHEURISTIC APPROACHES

The paper considers a new synergistic conflict-free movement model for unmanned aerial vehicle (UAV) swarm on the battlefield, based on the metaheuristic approach of particle swarm optimization (PSO). The paper presents an improved algorithm that ensures effective swarm coordination, traffic safety and efficient communication between UAVs. The model is designed to be used in various combat conditions and demonstrates the ability of the swarm to adapt to dynamic changes on the battlefield and perform tasks with high efficiency. Improvements to the PSO algorithm include the addition of collision avoidance force vectors, which allows each UAV take into account the position of its neighbors and avoid conflict situations. This ensures a more stable and smoother UAV swarm movement, reducing the risk of collisions and increasing the overall effectiveness of combat missions. The model also provides for the ability to adapt to changing conditions on the battlefield, which allows the UAV swarm to respond effectively to new threats and challenges.The simulation results show that the proposed metaheuristic approach based on the improved PSO algorithm is capable of calculating suboptimal trajectories for UAV swarm, minimizing the risk of collisions and improving the overall performance of combat missions. A comparative analysis with the classical PSO algorithm has revealed the advantages of the proposed model in the context of the efficiency of coordination and safety of UAV swarm movement. These results confirm the prospects of using the developed approach to control UAV swarms in combat conditions.The proposed algorithm allows each UAV in the swarm to take into account the other UAV position and speed, which allows maintaining the optimal distance between them, reducing collision probability. This is achieved by introducing an avoidance force vector that is directed away from other UAVs. This approach allows a swarm of UAVs to act as a single organized structure, which significantly increases the efficiency of performing tasks in complex and dynamic combat conditions. In addition, the model takes into account various combat scenarios, including obstacle avoidance, target acquisition, and retreat to a safe distance. This makes the algorithm a versatile tool for managing UAV swarms in real-world combat conditions, where the speed of reaction and accuracy of task execution are crucial.

Distributed Task Allocation for Multiple UAVs Based on Swarm Benefit Optimization

The auction mechanism stands as a pivotal distributed solution approach for addressing the task allocation problem in unmanned aerial vehicle (UAV) swarms, with its rapid solution capability well-suited to meet the real-time requirements of aerial mission planning for UAV swarms. Building upon the auction mechanism, this paper proposes a distributed task allocation method for multi-UAV grounded in swarm benefits optimization. The method introduces individual benefit variation to quantify the effect of a task on the benefit of a single UAV, thereby enabling direct optimization of swarm benefit through these individual benefit variations. Within the formulated individual benefit calculation, both the spatial distance between tasks and UAVs and the initial task value along with its temporal decay are taken into account, ensuring a thorough and accurate assessment. Additionally, the method incorporates real-time updates of individual benefits for each UAV, reflecting the dynamic state of task benefit fluctuations within the swarm. Monte Carlo simulation experiments demonstrate that, for a swarm size of 16 UAVs and 80 tasks, the proposed method achieves an average swarm benefit improvement of approximately 2% and 4% over the Consensus-Based Bundle Algorithm (CBBA) and Performance Impact (PI) methods, respectively, thus validating its effectiveness.

Storage Availability Prediction in Unmanned Aerial Vehicle Swarms Using Agent‐Based Simulation

ABSTRACTIn practice, when an unmanned aerial vehicle (UAV) swarm is not executing a mission, its UAVs will be stored as inventory. To ensure that the UAV swarm can be quickly deployed when needed, it is necessary to assess and predict its storage state. Due to the flexible configuration of UAV swarms and the complex factors that affect them during storage, existing storage state indicators and prediction methods cannot meet the requirements of UAV swarm storage. In order to address these issues, a UAV swarm storage availability prediction method based on agent‐based simulation (ABS) is proposed. Considering the degradation of health status, maintenance, support, and other factors during the storage period of UAVs, a UAV swarm storage state measurement metric that covers the storage cycle is proposed. Based on this metric, a UAV swarm storage availability model is established. Then, considering the dynamic adaptability and internal complex interactions of UAV swarms, the ABS is used to realize the modeling and prediction of UAV swarm storage availability. Finally, a UAV swarm rescue case is used to illustrate its scientific validity and accuracy. Therefore, this study offers a scientific and efficient method for measuring the availability of UAV swarms, providing valuable insights for rapid response and decision‐making during the transition from storage to deployment. It also presents a viable approach for availability modeling and prediction in complex, emergent swarm systems.

A Dynamic Task Allocation Method for Unmanned Aerial Vehicle Swarm Based on Wolf Pack Labor Division Model

A Dynamic Task Allocation Method for Unmanned Aerial Vehicle Swarm Based on Wolf Pack Labor Division Model

Extending Conflict-Based Search for Optimal and Efficient Quadrotor Swarm Motion Planning

Multi-agent pathfinding has been extensively studied by the robotics and artificial intelligence communities. The classical algorithm, conflict-based search (CBS), is widely used in various real-world applications due to its ability to solve large-scale conflict-free paths. However, classical CBS assumes discrete time–space planning and overlooks physical constraints in actual scenarios, making it unsuitable for direct application in unmanned aerial vehicle (UAV) swarm. Inspired by the decentralized planning and centralized conflict resolution ideas of CBS, we propose, for the first time, an optimal and efficient UAV swarm motion planner that integrates state lattice with CBS without any underlying assumption, named SL-CBS. SL-CBS is a two-layer search algorithm: (1) The low-level search utilizes an improved state lattice. We design emergency stop motion primitives to ensure complete UAV dynamics and handle spatio-temporal constraints from high-level conflicts. (2) The high-level algorithm defines comprehensive conflict types and proposes a motion primitive conflict detection method with linear time complexity based on Sturm’s theory. Additionally, our modified independence detection (ID) technique is applied to enable parallel conflict processing. We validate the planning capabilities of SL-CBS in classical scenarios and compare these with the latest state-of-the-art (SOTA) algorithms, showing great improvements in success rate, computation time, and flight time. Finally, we conduct large-scale tests to analyze the performance boundaries of SL-CBS+ID.

Task allocation for UAV swarms under communication attacks: An approach based on game theory and negotiation mechanism

This research delves into the method of task allocation for Unmanned Aerial Vehicle (UAV) swarms utilizing game theory and negotiation mechanisms in the face of communication network disruptions. Initially, a UAV swarm topology network characterized by a scale-free structure is established. Within this network, specific nodes undergo a single, deliberate batch attack. To address this, an edge-compensation strategy is formulated for network restoration, aiming to reinstate network connectivity to an optimal level. Subsequently, an algorithm is developed, integrating a bi-directional selection negotiation mechanism and coalition game theory, to determine the initial coalition for UAV task allocation. Following this, a traversal algorithm is employed to optimize the initial coalition, ultimately yielding the final task allocation coalition result. This methodology proves effective in repairing the topology network of UAV swarms during attack conditions, leading to the derivation of an optimal UAV allocation scheme that enhances the success rate of task allocations.

End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms

An unmanned aerial vehicle (UAV) swarm has emerged as a powerful tool for mission execution in a variety of applications supported by deep neural networks (DNNs). In the context of UAV swarms, conventional methods for efficient data processing involve transmitting data to cloud and edge servers. However, these methods often face limitations in adapting to real-time applications due to the low latency of cloud-based approaches and weak mobility of edge-based approaches. In this paper, a new system called deep reinforcement learning-based resilient layer distribution (DRL-RLD) for distributed inference is designed to minimize end-to-end latency in UAV swarm, considering the resource constraints of UAVs. The proposed system dynamically allocates CNN layers based on UAV-to-UAV and UAV-to-ground communication links to minimize end-to-end latency. It can also enhance resilience to maintain mission continuity by reallocating layers when inoperable UAVs occur. The performance of the proposed system was verified through simulations in terms of latency compared to the comparison baselines, and its robustness was demonstrated in the presence of inoperable UAVs.

Comparative Reliability Analysis of Unmanned Aerial Vehicle Swarm Based on Mathematical Models of Binary-State and Multi-State Systems

A key aspect in evaluating the performance of a UAV or its swarm is reliability. The reliability is calculated based on various mathematical models. Traditionally, Binary-State System (BSS) models, which assess two states—operational and faulty—are employed. However, some studies suggest using a Multi-State System (MSS) model, which allows for a detailed analysis by considering multiple states beyond just operational and faulty. Both mathematical models allow for the evaluation of Unmanned Aerial Vehicle (UAV) swarms based on availability, which is considered as a probability of swarm mission implementation. There is one more similar assessment computed based on MSS, which is named the probabilities of the performance level. There are not any recommendations for applications of these mathematical models and assessments for reliability analyses of UAV swarms. This paper introduces a comparative study on the availability of UAV swarms using both BSS and MSS models and the probability of performance levels of UAV swarms. This study provides quantitative and qualitative recommendations to exploit these mathematical models and assessments for UAV swarms according to computational complexity and informativeness. The comparative analysis shows that the evaluation of UAV swarm failure should be based on BSS, and the analysis of operation states should be implemented based on probabilities’ performance levels instead of swarm availability. These results are confirmed by quantitative and statistical examinations of UAV swarms of different types based on both BSS and MSS. The number of UAVs is changed from 2 to 20 in these examinations.

Research on Target Allocation for Hard-Kill Swarm Anti-Unmanned Aerial Vehicle Swarm Systems

In response to the saturated attacks by low, slow, and small UAV swarms, there is currently a lack of effective countermeasures. Counter-UAV swarm technology is an important issue that urgently requires breakthroughs. This paper conducts research on a mid–short-range hard-kill counter-swarm scenario where fewer swarms confront multiple swarms and stronger swarms confront weaker swarms. The requirement is for counter-swarm UAVs to quickly penetrate the swarm at mid–short range and collide with as many incoming UAVs as possible to destroy them. To address the sparse solution space problem, an improved genetic algorithm that integrates multiple strategies is adopted to calculate the spatial density distribution of the incoming swarm. A baseline is identified through gradient descent that maximizes the density integral in a straight-line direction. Based on this baseline, the solution space for single strikes on the swarm is filtered. During the solution process, an elite strategy is introduced to prevent the overall degradation of the population performance. Additionally, the feasibility of the flight trajectory needs to be assessed. A piecewise cubic spline interpolation method is used to optimize the flight trajectory, minimizing the maximum curvature. Ultimately, multiple counter-swarm UAV targets within the swarm and their corresponding trajectories are obtained.

Construction of kill webs with heterogeneous UAV swarms in dynamic contested environments

With the concept of "mosaic warfare," a novel combat style that involves constructing "kill webs" with unmanned aerial vehicle (UAV) swarms has emerged. However, little research has focused on this specific task scenario, particularly concerning the self-organization and adaptive collaboration of heterogeneous combat units in dynamic contested environments. Considering the scales and highly dynamic natures of such swarms, an adaptive communication network mechanism is developed based on the Molloy-Reed criterion. In contrast with common offline/noncombat task scenarios, the self-organization process is refined through agent-based modeling, and a combat effectiveness evaluation is introduced to provide enhanced task execution incentives. The proposed dynamic consensus-based coalition algorithm (DCBCA) addresses UAV intelligence defects such as "confusion," "forgetfulness," and "recklessness" during the dynamic target selection process, enabling effective bottom-up kill webs construction. Extensive simulation results demonstrate that the algorithmic system outlined in this paper can support the efficient and resilient operations of large-scale heterogeneous UAV swarms. The DCBCA outperforms the dynamically improved consensus-based grouping algorithm (CBGA) and the consensus-based timetable algorithm (CBTA) in terms of performance and convergence speed.

A Survey on Security of UAV Swarm Networks: Attacks and Countermeasures

The increasing popularity of Unmanned Aerial Vehicle (UAV) swarms is attributed to their ability to generate substantial returns for various industries at a low cost. Additionally, in the future landscape of wireless networks, UAV swarms can serve as airborne base stations, alleviating the scarcity of communication resources. However, UAV swarm networks are vulnerable to various security threats that attackers can exploit with unpredictable consequences. Against this background, this paper provides a comprehensive review on security of UAV swarm networks. We begin by briefly introducing the dominant UAV swarm technologies, followed by their civilian and military applications. We then present and categorize various potential attacks that UAV swarm networks may encounter, such as denial-of-service attacks, man-in-the-middle attacks and attacks against Machine Learning (ML) models. After that, we introduce security technologies that can be utilized to address these attacks, including cryptography, physical layer security techniques, blockchain, ML, and intrusion detection. Additionally, we investigate and summarize mitigation strategies addressing different security threats in UAV swarm networks. Finally, some research directions and challenges are discussed.

Distributed task allocation algorithm for heterogeneous unmanned aerial vehicle swarm based on coalition formation game

Distributed task allocation algorithm for heterogeneous unmanned aerial vehicle swarm based on coalition formation game

Real-Time Policy Optimization for UAV Swarms Based on Evolution Strategies

Multi-agent decision-making faces many challenges such as non-stationarity and sparse rewards, while the complexity and randomness of the real environment further complicate policy development. This paper addresses the high-dimensional policy optimization problems of unmanned aerial vehicle (UAV) swarms. By modeling the problem scenario as a Markov decision process, a real-time policy optimization algorithm based on evolution strategy (ES) pre-training is proposed. This approach combines decision-time planning with background planning to evaluate and integrate different sets of policy parameters in a temporal context. In the experimental phase, the policy network is trained using both ES and REINFORCE algorithms on a constructed simulation platform. Comparative experiments demonstrate the effectiveness of using ES for policy pre-training. Finally, the proposed real-time policy optimization algorithm further improves the performance of the swarm by approximately 10% in simulations, offering a feasible solution for adversarial games between swarms and extending the research scope of evolutionary algorithms.

Topology sensing of FANET under missing data

The topological structure of a flying ad hoc network (FANET) is crucial to understand, explain, and predict the behavior of unmanned aerial vehicle (UAV) swarms. Most studies focusing on topology sensing use perfect observations and complete datasets. However, the received signal dataset, being non-cooperative, commonly encounters instances of missing data, causing the performance of the existing algorithms to degrade. We investigate the issue of topology sensing of FANET based on external observations and propose a topology sensing method for FANET with missing data while introducing link-prediction methods to correct the topology inference results. First, we employ multi-dimensional Hawkes processes to model the communication event sequence in the network. Subsequently, to solve the problem in which the binary decision threshold is difficult to determine and cannot be adapted to the application scenario, we propose an extended multi-dimensional Hawkes model suitable for FANET and use the maximum likelihood estimation method for topology inference. Finally, to solve the problem of the low accuracy of inference results owing to missing data, we perform community detection on the observation network and combine the community detection and inference results to construct a mixed connection probability matrix, based on which we perform topology correction. The results of the analysis show that the topology sensing method proposed in this study is robust against missing data, indicating that it is an effective solution for solving this problem.

Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects

Interstellar objects (ISOs) are likely representatives of primitive materials invaluable in understanding exoplanetary star systems. Due to their poorly constrained orbits with generally high inclinations and relative velocities, however, exploring ISOs with conventional human-in-the-loop approaches is significantly challenging. This paper presents Neural-Rendezvous—a deep-learning-based guidance and control framework for encountering fast-moving objects, including ISOs, robustly, accurately, and autonomously in real time. It uses pointwise minimum norm tracking control on top of a guidance policy modeled by a spectrally normalized deep neural network, where its hyperparameters are tuned with a loss function directly penalizing the model predictive control state trajectory tracking error. We show that Neural-Rendezvous provides a high probability exponential bound on the expected spacecraft delivery error, the proof of which leverages stochastic incremental stability analysis. In particular, it is used to construct a non-negative function with a supermartingale property, explicitly accounting for the ISO state uncertainty and the local nature of nonlinear state estimation guarantees. In numerical simulations, Neural-Rendezvous is demonstrated to satisfy the expected error bound for 100 ISO candidates. This performance is also empirically validated using our spacecraft simulator and in high-conflict and distributed unmanned aerial vehicle swarm reconfiguration with up to 20 unmanned aerial vehicles.

Performance Analysis of Reconnaissance Coverage for HUAV Swarms under Communication Interference Based on Different Architectures

In environments with unknown communication interference, the mission efficiency of heterogeneous unmanned aerial vehicle (HUAV) swarms is often impacted by communication disruptions due to regions of strong interference encountered when executing reconnaissance and coverage missions. Existing research has rarely focused on communication interference or on the differences in HUAV characteristics under various control architectures; thus, in this paper we explore the performance differences between HUAV swarms based on centralized, distributed, and centralized-distributed architectures when executing reconnaissance and coverage missions in environments with unknown communication interference. First, a communication model in an unknown interference environment is constructed to reflect the real-time communication status of the swarm. Second, in response to the limitations of the traditional artificial potential field (APF) algorithm in this environment, a coverage-oriented artificial potential field (COAPF) algorithm is proposed. Finally, based on the COAPF algorithm, a multi-dimensional comparison of the mission completion efficiency of HUAV swarms with three different architectures is conducted. Our simulation results indicate that the distributed architecture is suitable for large-scale environments with strong interference, while the centralized–distributed architecture performs better in small-scale environments with weak interference. Conversely, the centralized architecture exhibits poor performance in all interference scenarios due to its lack of decision-making capabilities.

Age of information optimal UAV swarm-assisted sweep coverage in wireless sensor networks

For sweep coverage in wireless sensor networks (WSNs), the freshness of data directly affects the efficiency of task execution, and time is needed to continuously cover points of interest (POIs) to ensure obtaining all the data. However, existing studies ignored both. The outdated data and missing data may lead to decision-making errors, resulting in significant losses. To address this issue, this paper proposes a simultaneous sweep mode and a batch sweep mode of Unmanned Aerial Vehicle (UAV) swarm to achieve sweep coverage in WSNs, considering the freshness of data and the continuous coverage time of POIs, where the age of information (AoI) is adopted to measure the freshness of data. The target is to minimize the average AoI of POIs under the continuous coverage time constraint and the constraints of UAV swarm. Firstly, the POIs are clustered to obtain the best sweep points. Then, the UAV swarm sweep coverage (USSC) algorithm is designed for the two sweep modes. Finally, various simulations are conducted to verify the performance of the USSC algorithm. Simulation results show that the USSC algorithm can effectively minimize the average AoI compared to baseline algorithms. The script of the proposed algorithm can be found from:https://github.com/lilibeat/USSC.