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A swarm of Unmanned Aerial Vehicles (UAVs) comprises multiple UAVs that are capable of completing tasks beyond the capabilities of a single UAV. Due to the unique challenges of UAV missions, these vehicles often operate far from base stations, making network connectivity crucial for the successful completion of UAV swarm missions. However, existing methods do not account for strategies to maintain the overall connectivity of the UAV network when nodes are under attack. To address this issue, we propose a method named Adaptive Communication Power Control (ACPC) that dynamically adjusts UAV communication power to mitigate potential connectivity losses caused by node failures or malicious attacks. This adjustment ensures that the network can maintain information exchange among the remaining UAVs even in the event of disruptions. Additionally, we introduce a novel evaluation method to assess the overall connectivity of the network and validate ACPC through simulations of UAV swarm missions where some UAVs experience failures. Using this evaluation method, we measured the network’s state before and after attacks and recovery and calculated the additional energy consumption required by the UAVs. The results indicate that our method can increase the resilience of the UAV network by up to 5.36 times, while only raising the total communication energy consumption to 1.53 times. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the rapid development of unmanned aerial vehicles (UAVs) technologies, a substantial increase on the employ of UAV swarms in a wide range of civilian and military tasks has been witnessed. Advanced confrontation control approach can greatly improve UAVs’ capabilities and effectively free pilots from dangerous, boring, and burdensome confrontation missions. How to efficiently control UAV swarms in the air-to-air confrontation is still a hard problem. In this paper, considering the influence of the defending angle of UAV, we propose a general attacking cost function and an adaptive attacking strategy (AAS) to improve the capability of UAV swarm against another UAV swarm in an airborne battlefield. A multi-agent based UAV swarm air-to-air confrontation model is established, where red UAV swarm versus blue UAV swarm were simulated in a visual 3D and discrete-event environment. Extensive simulations are performed to verify the performance of AAS, the results show that AAS outperforms other traditional strategies by a large margin. In particular, UAV swarm adopting AAS can obtain a very high winning percentage even though the size of the swarm is only half of its opposing swarm that uses random or low velocity strategy. Meanwhile, AAS is quite robust to cope with different UAV swarm sizes. To improve the usability and practicability of AAS, we also propose a lightweight strategy called empirical adaptive attacking strategy (EAAS). The simulation results indicate that EAAS is easy to use and can retain the similar effects to AAS especially for large scale UAV swarms. Our work will illuminate new insights into the area of UAV swarm versus UAV swarm. [ABSTRACT FROM AUTHOR]

This paper proposes a spatiotemporal coordinated control method for unmanned aerial vehicle (UAV) swarms based on predefined time convergence in response to the coordinated ground attack problem of UAV swarms in a three-dimensional battlefield environment. First, we analyze the UAV swarm’s ground attack task scenario and its relevant elements and establish models for the UAV swarm, ground-moving targets, and attack problems. Building upon problem modeling, this paper considers the spatiotemporal coordinated strategy for a UAV swarm and designs a spatiotemporal coordinated control law for a three-dimensional spatial UAV swarm ground attack. The proposed spatiotemporal coordinated control law can guide the UAV swarm to reach the desired attack position within a specified time, achieving a spatiotemporal coordinated attack on ground-moving targets. A theoretical analysis based on the Lyapunov function demonstrates that the proposed control law can asymptotically stabilize the attack position error variable under a predefined time condition. Simulation results show that the proposed coordinated control law can achieve spatiotemporal coordination of the UAV swarm in a ground-moving target turning motion, enabling the completion of the multiangle coordinated attack on ground-moving targets within a specified time. Currently, research on the coordinated ground attack of UAV swarms mostly focuses on stationary targets, making it difficult to apply to coordinated attack tasks on moving targets. Although the coordinated tracking of moving targets by UAV swarms has been reported in the literature, spatiotemporal coordination among UAVs has not been considered. Therefore, this paper focuses on the spatiotemporal coordinated control strategy of UAV swarms, with a background of coordinated ground attacks by UAV swarms. First, this paper conducts a detailed analysis of the UAV swarm’s coordinated ground attack task in terms of time and multiple elements to establish a comprehensive task profile. Subsequently, by focusing on the core elements of coordinated ground attack by UAV swarms, this paper models both UAV swarms and ground-moving targets. Based on the established models and coordinated control strategy, this paper considers spatiotemporal coordination constraints and designs acceleration commands for each UAV to guide them to reach the specified attack positions at predefined time points, enabling coordinated attacks on the ground targets simultaneously. The designed acceleration control commands serve as inputs to the UAV’s autopilot, further generating lower-level control commands, such as speed, flight path angle, and heading angle, thereby achieving stable flight control of the UAVs. The proposed coordinated control strategy endows the UAV swarm with the capability to achieve spatiotemporal synchronized coordinated attacks, thereby enhancing the operational effectiveness of UAV-coordinated ground attacks.

Abstract Unmanned Aerial Vehicle (UAV) swarms have emerged as a promising technology for various applications, such as delivery, surveillance, and infrastructure inspection. An additional feature of deploying large UAV swarms is their use in mobile offloading networking. At the same time, this implies a key challenge in the efficient management of the computational and networking requirements for these offloading processes. This paper aims to fill this gap through a systematic literature review (SLR) analysing the research on distributed task offloading in UAV swarms. We conducted a systematic search of major scientific databases to identify relevant literature published between 2019 and 2023. A total of 63 papers were selected through a multistage screening process and their key contributions. This SLR aims to provide the current state of research on UAV swarm task offloading, including considerations for computation offloading, the role of UAV swarms, different aspects of UAV swarms, the number of UAVs in swarms impacting performance, and open issues. Our review also highlights UAV swarm offloading in various domains and discusses the challenges and limitations that must be addressed to ensure the widespread adoption of this technology. We outline the future research directions and potential applications of UAV swarm offloading, including its integration with other technologies.

UAV swarms have increasingly replaced human labor in various industries. However, controlling a large group of UAVs can be difficult and, if not done correctly, cause significant financial losses and delays. While many methods aim to improve how the group is controlled, little focus is given to optimizing and stabilizing movement trajectories, which limits the emergency response and reliability of UAV swarm in dynamic environments. This paper proposes a new method to control UAVs through Proximal Policy Optimization. This method utilizes two neural networks and incorporates the concept of a game. During training, one neural network generates an action based on the current state, while the other evaluates the output of the first network. With continuous refinement, the performance of both networks can be enhanced, ultimately leading to the optimal decision-making model. Additionally, this research introduces a hierarchical management mechanism to address the issue of complex computations in large bee colonies and to distribute control more evenly. Simulation results demonstrate that this approach can successfully reconstruct formations under various scenarios. Compared to similar algorithms, this method is at the forefront of tackling large-scale problems, with a collision rate close to 0 and a 100% success rate in emergency processing.

As an integral part to the air, space and ground integrated network, unmanned aerial vehicle (UAV) swarms have shown broad application prospects in complex tasks such as emergency search and rescue, offensive and defensive confrontation, intelligence reconnaissance and surveillance. The advancements in unmanned technology, coupled with the increasingly rich applications of multi-UAV collaborative intelligence, have engendered new requisites for the information exchange capabilities within UAV swarm networks. Under the umbrella of intent-based network architecture, UAV swarms are capable of autonomously managing the network, thereby mitigating the intricacies inherent in network management. Introducing intent-driven network into UAV swarm systems urgently requires breakthroughs in smart autonomous network technology to enhance the adaptability of intelligent groups in high-dynamic and uncertain environments. A new perspective and method for the collaborative operation and intelligent decision-making of UAV swarms was provided. The architecture, key technologies, and prospects of intent-based UAV swarm networks management were reviewed. Through in-depth analysis and discussion, a solid theoretical foundation and technical reference were aimed to be provided for the future development of UAV swarms.

Unmanned Aerial Vehicle (UAV) swarms have emerged as a promising technology for various applications, such as delivery, surveillance, and infrastructure inspection. An additional feature of deploying large UAV swarms is their use in mobile offloading networking. At the same time, this implies a key challenge in the efficient management of the computational and networking requirements for these offloading processes. This paper aims to fill this gap through a systematic literature review (SLR) analysing the research on distributed task offloading in UAV swarms. We conducted a systematic search of major scientific databases to identify relevant literature published between 2019 and 2023. A total of 63 papers were selected through a multistage screening process and their key contributions. This SLR aims to provide the current state of research on UAV swarm task offloading, including considerations for computation offloading, the role of UAV swarms, different aspects of UAV swarms, the number of UAVs in swarms impacting performance, and open issues. Our review also highlights UAV swarm offloading in various domains and discusses the challenges and limitations that must be addressed to ensure the widespread adoption of this technology. We outline the future research directions and potential applications of UAV swarm offloading, including its integration with other technologies. [ABSTRACT FROM AUTHOR]

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Unmanned aerial vehicles (UAVs) are widely used for missions in dynamic environments. Deep Reinforcement Learning (DRL) can find effective strategies for multiple agents that need to cooperate to complete the task. In this article, the challenge of controlling the movement of a fleet of UAVs is addressed by Multi-Agent Deep Reinforcement Learning (MARL). The collaborative movement of the UAV fleet can be controlled centrally and also in a decentralized fashion, which is studied in this work. We consider a dynamic military environment with a fleet of UAVs, whose task is to destroy enemy targets while avoiding obstacles like mines. The UAVs inherently come with a limited battery capacity directing our research to focus on the minimum task completion time. We propose a continuous-time-based Proximal Policy Optimization (PPO) algorithm for multi-aGent Learning In Dynamic Environments (GLIDE). In GLIDE, the UAVs coordinate among themselves and communicate with the central base to choose the best possible action. The action control in GLIDE can be controlled in a centralized and decentralized way, and two algorithms called Centralized-GLIDE (C-GLIDE), and Decentralized-GLIDE (D-GLIDE) are proposed on this basis. We developed a simulator called UAV SIM, in which the mines are placed at randomly generated 2D locations unknown to the UAVs at the beginning of each episode. The performance of both the proposed schemes is evaluated through extensive simulations. Both C-GLIDE and D-GLIDE converge and have comparable performance in target destruction rate for the same number of targets and mines. We observe that D-GLIDE is up to 68% faster in task completion time compared to C-GLIDE and could keep more UAVs alive at the end of the task. [ABSTRACT FROM AUTHOR]

Employing multiple drones as a swarm to complete missions can sharply improve the working efficiency and expand the scope of investigation. Remote UAV swarms utilize satellites as relays to forward investigation information. The increasing amount of data demands higher transmission rate and complex field network coding (CFNC) is deemed as an effective solution for data return. CFNC applied to UAV swarms enhances transmission efficiency by occupying only two time slots, which is less than other network coding schemes. However, conventional CFNC applied to UAVs is combined with constant coding and modulation scheme and results in a waste of spectrum resource when the channel conditions are better. In order to avoid the waste of power resources of the relay satellite and further improve spectral efficiency, a CFNC transmission scheme with MPSK modulation is proposed in this paper. For the proposed scheme, the satellite relay no longer directly forwards information, but transmits information after processing according to the current channel state. The proposed transmission scheme not only maintains throughput advantage of CFNC, but also enhances spectral efficiency, which obtains higher throughput performance. The symbol error probability (SEP) and throughput results corroborated by Monte Carlo simulation show that the proposed transmission scheme improves spectral efficiency in multiples compared to the conventional CFNC schemes. In addition, the proposed transmission scheme enhances the throughput performance for different topology structures while keeping SEP below a certain value. [ABSTRACT FROM AUTHOR]

UAV swarms have increasingly replaced human labor in various industries. However, controlling a large group of UAVs can be difficult and, if not done correctly, cause significant financial losses and delays. While many methods aim to improve how the group is controlled, little focus is given to optimizing and stabilizing movement trajectories, which limits the emergency response and reliability of UAV swarm in dynamic environments. This paper proposes a new method to control UAVs through Proximal Policy Optimization. This method utilizes two neural networks and incorporates the concept of a game. During training, one neural network generates an action based on the current state, while the other evaluates the output of the first network. With continuous refinement, the performance of both networks can be enhanced, ultimately leading to the optimal decision-making model. Additionally, this research introduces a hierarchical management mechanism to address the issue of complex computations in large bee colonies and to distribute control more evenly. Simulation results demonstrate that this approach can successfully reconstruct formations under various scenarios. Compared to similar algorithms, this method is at the forefront of tackling large-scale problems, with a collision rate close to 0 and a 100% success rate in emergency processing. • Propose the improved PPO to complete formation reconstruction and obstacle avoidance. • Introduce hierarchical control mechanism to reduce complexity and improve stability. • Have fast response, strong execution efficiency and high anti-disturbance ability. [ABSTRACT FROM AUTHOR]

During short-range air combat involving unmanned aircraft vehicle (UAV) swarms, UAVs must make accurate maneuver decisions based on information from both enemy and friendly UAVs. This dual requirement of competition and cooperation presents a significant challenge in the field of unmanned air combat. In this paper, a method based on multi-agent reinforcement learning (MARL) is proposed to address this issue. An actor network containing three subnetworks that can handle different types of situational information is designed. Hence, the results from simpler one-on-one scenarios are leveraged to enhance the complex swarm air combat training process. Separate state spaces for local and global information are designed for the actor and critic networks. A detailed reward function is proposed to encourage participation. To prevent lazy participants in air combat, a reward assignment operation is applied to distribute these dense rewards. Simulation testing and ablation experiments demonstrate that both the transfer operation and reward assignment operation can effectively deal with the swarm air combat scenario, and reflect the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Focusing on the tasks collaboration requirements of Unmanned Aerial Vehicle (UAV) swarm in complex battlefield environment, it is especially important to ensure the reliability of routing communication within the formation. Wireless ultraviolet (UV) secret communication can meet the reliable and secret communication needs of UAV swarm in the complex atmospheric environment. Moreover, considering the limited energy carried by UAVs, in order to maximize the flight time of UAVs, this paper proposes an unequal clustering energy consumption balancing routing algorithm (UCEBRA) of UAV swarm based on UV secret communication. Combined the advantages of wireless UV scattering communication, this algorithm improves the selection threshold of candidate cluster heads (CHs) and the clustering conditions of nodes according to the performance of UAV nodes. Furthermore, a function, which can comprehensively reflect both distance and remaining energy of UAVs, is built in the process of inter-cluster routing, to balance the energy consumption of the CHs. The simulation results show that compared with low-energy adaptive clustering hierarchy (LEACH) and energy-efficient unequal clustering (EEUC), the energy efficiency of the UAV network is increased by 21.7% and 4.6% respectively, and the lifetime of the network prolongs by 26.2% and 7.8% under UCEBRA algorithm. Therefore, the algorithm proposed in this paper can efficiently balance the energy consumption of nodes and thus prolong the lifetime of UAV swarm. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the main equipment for earthquake disaster rescue and reconnaissance battlefield, the robustness of UAV swarm communication system is an important index of destruction resistance. In this paper, we model the UAV swarm communication system with the help of complex dependency network theory, introduce the relative network effectiveness ratio as an index of the overall robustness of the communication system network, which accurately reflects the interdependence between nodes, and provides a new perspective for evaluating the robustness of the communication system under the target attack. A new perspective is provided for assessing the robustness of communication systems under target attack. Based on the classical M-L model and the load redistribution strategy with dynamic information, we establish the dependent homogeneous ER-ER and heterogeneous ER-BA network models, consider the characteristics of the degree of dependent nodes in the two-layer network for target attack, and analyze the dynamic process of their cascading failure under the attack, as well as the influence of the characteristic parameters, such as the probability of connecting edges of any two nodes, the average degree and the capacity coefficient, on the overall robustness of the communication network. It is shown that network robustness and network dependency strength are negatively correlated, and a reduction of 52.09% to 72.9% dependency strength can be considered in the cascade failure process to resist more serious communication network collapse. For isomorphic networks, the difference in dependency patterns has a greater impact by reducing the overall robustness of RL and AL compared to DL by 9.25% and 15.95%, respectively.

In modern warfare, the use of UAV swarm to deceive and interfere with the hostile networked radar has been widely used as an effective countermeasure. However, when facing a complex combat environment, the digital storage and forwarding equipment carried by the UAV swarm has forwarding delay error due to its limited accuracy. This delay error will lead to a deviation between the generated false target point and the preset false target point to reduce the effectiveness of deception significantly. To address this issue, in given prior knowledge of the location and RF radiation parameters of networked radar stations, this paper proposes a solution based on the concept of “homologous test” and the certain spatial resolution of networked radar. The boundary conditions for effective deception jamming by digital storage and forwarding technology to networked radar are analyzed, and the influence of forwarding delay error on deception jamming effect in typical networked radar systems is identified. Simulation results demonstrate that the analysis and deduction results can provide a reliable assessment of whether the actual jammer performance can effectively deceive and interfere with the networked radar.

Unmanned aerial vehicle (UAV) swarm intelligence technology has shown unique advantages in agricultural and forestry disaster detection, early warning, and prevention with its efficient and precise cooperative operation capability. In this paper, a systematic application strategy of UAV swarms in forest fire detection is proposed, including fire point detection, fire assessment, and control measures, based on the fusion of particle swarm optimization (PSO) and the artificial bee colony (ABC) algorithm. The UAV swarm application strategy provides optimized paths to quickly locate multiple mountain forest fire points in 3D forest modeling environments and control measures based on the analysis of the fire situation. This work lays a research foundation for studying the precise application of UAV swarm technology in real-world forest fire detection and prevention. [ABSTRACT FROM AUTHOR]

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In a satellite-denied environment, a swarm of drones is capable of achieving relative positioning and navigation by leveraging the high-precision ranging capabilities of the inter-drone data link. However, because of factors such as high drone mobility, complex and time-varying channel environments, electromagnetic interference, and poor communication link quality, distance errors and even missing distance values between some nodes are inevitable. To address these issues, this paper proposes a low-rank optimization algorithm based on the eigenvalue scaling of the distance matrix. By gradually limiting the eigenvalues of the observed distance matrix, the algorithm reduces the rank of the matrix, bringing the observed distance matrix closer to the true value without errors or missing data. This process filters out distance errors, estimates and completes missing distance elements, and ensures high-precision calculations for subsequent topology perception and relative positioning. Simulation experiments demonstrate that the algorithm exhibits significant error filtering and missing element completion capabilities. Using the F-norm metric to measure the relative deviation from the true value, the algorithm can optimize the relative deviation of the observed distance matrix from 11.18% to 0.25%. Simultaneously, it reduces the relative positioning error from 518.05 m to 35.24 m, achieving robust topology perception and relative positioning for the drone swarm formation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

For the formation and obstacle avoidance challenges of UAVs (unmanned aerial vehicles) in complex scenarios, this paper proposes an improved collaborative strategy based on APF (artificial potential field). This strategy combines graph theory, the Leader–Follower method, and APF. Firstly, we used graph theory to design formation topology and dynamically adjust the distances between UAVs in real time. Secondly, we introduced APF to avoid obstacles in complicated environments. This algorithm innovatively integrates the Leader–Follower formation method. The design of this attractive field is replaced by the leader's attraction to the followers, overcoming the problem of unreachable targets in APF. Meanwhile, the introduced Leader–Follower mode reduces information exchange within the swarm, realizing a more efficient "few controlling many" paradigm. Afterwards, we incorporated rotational force to assist the swarm in breaking free from local minima. Ultimately, the stability of the integrated formation strategy was demonstrated using Lyapunov functions. The feasibility and effectiveness of the proposed strategy were validated across multiple platforms. [ABSTRACT FROM AUTHOR]

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Existing routing protocols without awareness of the topology causes excessive retransmissions, energy holes, and long delay, data routing performance was seriously deteriorated.Considering the relation of topology and routing, an intelligent route planning with jointing topology control (IRPJTC) method was proposed.IRPJTC consisted of two part, the virtual force-based adaptive topology control (VFATC), and the PPO-based geographic routing protocol (PPO-GRP).Based on neighbor’s mobility information, the distance between UAVs was adaptively adjusted by VFATC to provide stable links between UAVs.Combined with link stability metric in VFATC, end-to-end delay and energy consumption, a multi-objective reward function was designed by PPO-GRP to train optimal routing strategy.According to the performance study, the proposed IRPJTC reduces existing routing protocols by 12.11% of end-to-end delay, and 4.56% of energy consumption, and has a better energy balance ability.

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.

The cooperative region search of UAV swarm can obtain ground information of the mission region and reduce the uncertainty of environmental information effectively. The traditional collaborative region search methods based on the balanced allocation of divided region and the heuristic algorithms depend on the pre-designed rules and heavy computation, and have no ability to generate new rules of the cooperative search. These algorithms belong to the algorithms that can not evolve new rules. Due to the complexity of the mission environment, the algorithms must contain fast,intelligent and robust characteristics, the cooperative searching algorithms of UAV swarm based on emerging theory with strong information fusion ability, self-learning ability have been widely concerned. Evolutionary and reinforcement learning algorithms are the important parts of the emerging theory and both of them can generate some new cooperative searching rules according to the different environment and task. The paper would systematically analyze and summarize the current research status and progress of cooperative search methods. Finally, the shortcomings of the existing research and the further development are put forward.

The advent of AI and 5G technologies has markedly enhanced the intelligence and connectivity of UAVs, leading to the development of UAV swarms. These swarms not only exhibit superior efficiency and adaptability in collective tasks but also offer considerable potential in both civilian and military sectors. However, despite the innovative insights provided by UAV swarm networking in trajectory forecasting, current approaches face obstacles due to the inherent dynamic complexity of these swarms, often neglecting the data from inter-swarm interactions. This research begins by defining metrics of link channel capacity to record the informational exchanges within UAV swarms, thus laying the foundation for a network of UAV swarms. It then advances a dynamic graph neural network (DynGN) model that utilizes an encoder-decoder structure combining a graph convolutional network with a gated recurrent unit. This model processes both the evolving network configuration and trajectory data of UAV swarms simultaneously, enabling more precise trajectory predictions. Through experiments focusing on prediction accuracy, node number stability, and noise robustness, the effectiveness of the model is assessed. Results indicate that the DynGN model outperforms conventional trajectory prediction models, achieving notable improvements in accuracy and fit quality. Moreover, its robustness against noise in dynamic trajectory data highlights its extensive utility in practical mission contexts.

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.

The cooperative region search of UAV swarm can obtain ground information of the mission region and reduce the uncertainty of environmental information effectively. The traditional collaborative region search methods based on the balanced allocation of divided region and the heuristic algorithms depend on the pre-designed rules and heavy computation, and have no ability to generate new rules of the cooperative search. These algorithms belong to the algorithms that can not evolve new rules. Due to the complexity of the mission environment, the algorithms must contain fast,intelligent and robust characteristics, the cooperative searching algorithms of UAV swarm based on emerging theory with strong information fusion ability, self-learning ability have been widely concerned. Evolutionary and reinforcement learning algorithms are the important parts of the emerging theory and both of them can generate some new cooperative searching rules according to the different environment and task. The paper would systematically analyze and summarize the current research status and progress of cooperative search methods. Finally, the shortcomings of the existing research and the further development are put forward. [ABSTRACT FROM AUTHOR]

Swarm control of unmanned aerial vehicles (UAV) has emerged as a challenging research area, primarily attributed to the presence of conflicting behaviors among individual UAVs and the influence of external movement disturbances of UAV swarms. However, limited attention has been drawn to addressing the fission–fusion motion of UAV swarms for unknown dynamic obstacles, as opposed to static ones. A Bio-inspired Fission–Fusion control and planning via Reinforcement Learning (BiFRL) algorithm for the UAV swarm system is presented, which tackles the problem of fission–fusion behavior in the presence of dynamic obstacles with homing capabilities. Firstly, we found the kinematics models for the UAV and swarm controller, and then we proposed a probabilistic starling-inspired topological interaction that achieves reduced overhead communication and faster local convergence. Next, we develop a self-organized fission–fusion control framework and a fission decision algorithm. When dealing with various situations, the swarm can autonomously re-configure itself by fissioning an optimal number of agents to fulfill the corresponding tasks. Finally, we design a sub-swarm confrontation algorithm for path planning optimized by reinforcement learning, where the sub-swarm can engage in encounters with dynamic obstacles while minimizing energy expenditure. Simulation experiments demonstrate the capability of the UAV swarm system to accomplish self-organized fission–fusion control and planning under different interference scenarios. Moreover, the proposed BiFRL algorithm successfully handles adversarial motion with dynamic obstacles and effectively safeguards the parent swarm. [ABSTRACT FROM AUTHOR]

Existing routing protocols without awareness of the topology causes excessive retransmissions, energy holes, and long delay, data routing performance was seriously deteriorated. Considering the relation of topology and routing, an intelligent route planning with jointing topology control (IRPJTC) method was proposed. IRPJTC consisted of two part, the virtual force-based adaptive topology control (VFATC), and the PPO-based geographic routing protocol (PPO-GRP). Based on neighbor's mobility information, the distance between UAVs was adaptively adjusted by VFATC to provide stable links between UAVs. Combined with link stability metric in VFATC, end-to-end delay and energy consumption, a multi-objective reward function was designed by PPO-GRP to train optimal routing strategy. According to the performance study, the proposed IRPJTC reduces existing routing protocols by 12.11% of end-to-end delay, and 4.56% of energy consumption, and has a better energy balance ability. [ABSTRACT FROM AUTHOR]

With the fast development of unmanned aerial vehicles (UAVs) and the user increasing demand of UAV video transmission, UAV video service is widely used in dynamic searching and reconnoitering applications. Video transmissions not only consider the complexity and instability of 3D UAV network topology but also ensure reliable quality of service (QoS) in flying ad hoc networks (FANETs). We propose hedge transfer learning routing (HTLR) for dynamic searching and reconnoitering applications to address this problem. Compared with the previous transfer learning framework, HTRL has the following innovations. First, hedge principle is introduced into transfer learning. Online model is continuously trained on the basis of offline model, and their weight factors are adjusted in real-time by transfer learning, so as to adapt to the complex 3D FANETs. Secondly, distributed multi-hop link state scheme is used to estimate multi-hop link states in the whole network, thus enhancing the stability of transmission links. Among them, we propose the multiplication rule of multi-hop link states, which is a new idea to evaluate link states. Finally, we use packet delivery rate (PDR) and energy efficiency rate (EER) as two main evaluation metrics. In the same NS3 experimental scenario, the PDR of HTLR is at least 5.11% higher and the EER is at least 1.17 lower than compared protocols. Besides, we use Wilcoxon test to compare HTLR with the simplified version of HTLR without hedge transfer learning (N-HTLR). The results show that HTRL is superior to N-HTRL, effectively ensuring QoS. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Due to the advantages of rapid deployment, flexible response and strong invulnerability, unmanned aerial vehicle (UAV) swarm has been widely applied in collaborative warfare and emergency communication. However, UAV swarm in complex environments is prone to chaotic collapse due to obstructions. A UAV swarm obstacle avoidance system model for multi-narrow type obstacles is established. Due to the fact that only one UAV is allowed to pass through each small hole at any given moment, addressing the issue of congestion caused by swarming effects becomes crucial in addition to managing the competitive allocation of multiple UAVs to multiple holes. Aiming at this problem, a dual-game real-time obstacle avoidance scheme is proposed for UAV swarm with multi-narrow type obstacle scenarios, which divides the flight process of the UAV swarm into two stages: maintaining the flight state of the UAV swarm unchanged when no obstacles are encountered, and implementing matching separation and motion state switching by means of dual-game strategy when facing multi-narrow type obstacles, ultimately achieving orderly passage after multiple rounds of games. For the proposed scheme, a dual-game based Flocking (DGF) obstacle avoidance algorithm is proposed. Specifically, the motion state of each UAV obtained from the game is parameterized and integrated with the Flocking algorithm to calculate the motion control input for each UAV. The solution is iteratively obtained until the UAV swarm completes the obstacle avoidance. Simulation results demonstrate that the proposed DGF algorithm not only enables smooth obstacle avoidance for the UAV swarm in multi-narrow type obstacle scenarios, but also effectively resolves the internal chaos problem in the UAV swarm, thereby preventing rigid collisions.

Unmanned aerial vehicles (UAVs) are widely used for missions in dynamic environments. Deep Reinforcement Learning (DRL) can find effective strategies for multiple agents that need to cooperate to complete the task. In this article, the challenge of controlling the movement of a fleet of UAVs is addressed by Multi-Agent Deep Reinforcement Learning (MARL). The collaborative movement of the UAV fleet can be controlled centrally and also in a decentralized fashion, which is studied in this work. We consider a dynamic military environment with a fleet of UAVs, whose task is to destroy enemy targets while avoiding obstacles like mines. The UAVs inherently come with a limited battery capacity directing our research to focus on the minimum task completion time. We propose a continuous-time-based Proximal Policy Optimization (PPO) algorithm for multi-aGent Learning In Dynamic Environments (GLIDE). In GLIDE, the UAVs coordinate among themselves and communicate with the central base to choose the best possible action. The action control in GLIDE can be controlled in a centralized and decentralized way, and two algorithms called Centralized-GLIDE (C-GLIDE), and Decentralized-GLIDE (D-GLIDE) are proposed on this basis. We developed a simulator called UAV SIM, in which the mines are placed at randomly generated 2D locations unknown to the UAVs at the beginning of each episode. The performance of both the proposed schemes is evaluated through extensive simulations. Both C-GLIDE and D-GLIDE converge and have comparable performance in target destruction rate for the same number of targets and mines. We observe that D-GLIDE is up to 68% faster in task completion time compared to C-GLIDE and could keep more UAVs alive at the end of the task.

In response to the issue where the original synchronization time becomes inapplicable for UAV swarms after temporal consistency convergence due to obstacle avoidance, a new distributed consultative temporal consistency guidance law that takes into account threat avoidance has been proposed. Firstly, a six-degree-of-freedom dynamic model and a guidance control model for unmanned aerial vehicles (UAVs) are established, and the guidance commands are decomposed into control signals for the pitch and yaw planes. Secondly, based on the theory of dynamic inversion control, a temporal consistency guidance law for a single UAV is constructed. On the other hand, an improved artificial potential field theory is used and integrated with a predictive correction network to generate guidance commands for threat avoidance. A threshold smoothing method is employed to integrate the two guidance systems, and a cluster consultation mechanism is introduced to design a two-layer temporal synchronization architecture, which negotiates to change the synchronization time of the swarm to achieve the convergence of consistency once again. Finally, in typical application scenarios, simulation verification demonstrates the effectiveness of the control method proposed in this paper. The proposed control method achieves the guidance of UAV formations to synchronize their arrival at the target location under complex threat conditions.

Target assignment and trajectory planning are two crucial components of mission planning for unmanned aerial vehicle (UAV) swarms. In large-scale missions, the significance of planning efficiency becomes more pronounced. However, existing planning algorithms based on evolutionary computation and swarm intelligence face formidable challenges in terms of both efficiency and effectiveness. Additionally, the extensive trajectory planning involved is a significant factor affecting efficiency. Therefore, this paper proposes a dedicated method for large-scale mission planning. Firstly, to avoid extensive trajectory planning operations, this paper suggests utilizing a machine learning algorithm to establish a predictive model of trajectory length. To ensure predictive accuracy, an ensemble algorithm based on Gaussian process regression (GPR) is proposed. Secondly, to ensure the efficiency and effectiveness of target assignments in large-scale missions, this paper draws inspiration from a greedy search and proposes a simple yet effective target assignment algorithm. This algorithm can effectively handle a large number of decision variables and constraints involved in large-scale missions. Finally, we validated the effectiveness of the proposed method through 15 simulated missions of different scales. Among the 10 medium- to large-scale missions, our method achieved the best results in 9 of them, demonstrating the competitive advantage of our method in large-scale missions. Comparative results demonstrate the advantage of the proposed methods from both prediction and mission planning perspectives.

This paper investigates the target fencing control problem of fixed-wing Unmanned Aerial Vehicle (UAV) swarms with collision avoidance and connectivity maintenance in obstacle environments. A distributed cooperative fencing scheme for maneuvering targets is proposed without predefined accurate formation. Firstly, considering that not all states of the target can be obtained by UAVs, a differential state observer is developed to estimate the target’s unknown speed and control input. Secondly, by constructing potential functions with fewer parameter adjustments, corresponding negative gradient terms are calculated to guarantee the flight safety and communication connectivity of the swarm. A distributed cooperative controller is designed using the self-organized theory and consensus control. Additionally, the stability of the closed-loop system with the controller is analyzed based on Lyapunov stability theory. Finally, numerical simulations are performed to illustrate the effectiveness of the proposed scheme.

This paper provides an in-depth analysis of the current research landscape in the field of UAV (Unmanned Aerial Vehicle) swarm formation control. This review examines both conventional control methods, including leader–follower, virtual structure, behavior-based, consensus-based, and artificial potential field, and advanced AI-based (Artificial Intelligence) methods, such as artificial neural networks and deep reinforcement learning. It highlights the distinct advantages and limitations of each approach, showcasing how conventional methods offer reliability and simplicity, while AI-based strategies provide adaptability and sophisticated optimization capabilities. This review underscores the critical need for innovative solutions and interdisciplinary approaches combining conventional and AI methods to overcome existing challenges and fully exploit the potential of UAV swarms in various applications.

Unmanned aerial vehicle (UAV) swarm confrontation jamming offers a cost-effective and long-range countermeasure against hostile swarms. Intelligent decision-making is a key factor in ensuring its effectiveness. In response to the low-timeliness problem caused by linear programming in current algorithms, this paper proposes an intelligent decision-making algorithm for UAV swarm confrontation jamming based on the multi-agent actor–critic (M2AC) model. First, based on Markov games, an intelligent mathematical decision-making model is constructed to transform the confrontation jamming scenario into a symbolized mathematical problem. Second, the indicator function under this learning paradigm is designed by combining the actor–critic algorithm with Markov games. Finally, by employing a reinforcement learning algorithm with multithreaded parallel training–contrastive execution for solving the model, a Markov perfect equilibrium solution is obtained. The experimental results indicate that the algorithm based on M2AC can achieve faster training and decision-making speeds, while effectively obtaining a Markov perfect equilibrium solution. The training time is reduced to less than 50% compared to the baseline algorithm, with decision times maintained below 0.05 s across all simulation conditions. This helps alleviate the low-timeliness problem of UAV swarm confrontation jamming intelligent decision-making algorithms under highly dynamic real-time conditions, leading to more effective and efficient UAV swarm operations in various jamming and electronic warfare scenarios.