Subgroup Differentiation for Aerial Swarm Search and Surveillance in Limited Sensing Areas

We present a swarm of autonomous Unmanned Aerial Vehicles (UAVs) capable of persistent surveillance as well as engagement of the hostile targets identified on the ground. That is, for a given area of interest, that might be hostile, we design a capability of monitoring ground targets by the swarm of autonomous armed UAVs in persistent way that will be capable of engaging these targets if necessary. The UAVs decide by themselves when and how to come back to the maintenance site(s) in order to be recharged of refueled. A single Operating Control Unit (OCU) is sufficient to integrate with such a swarm of UAVs. Its role is mainly focused on sending high level commands, some of which might overwrite the UAVs autonomous intention. For example, a commander might request through OCU engagement of target(s), look for specific target(s), or immediate return of some UAVs to the maintenance sites. Since UAVs are autonomous then the communication links from OCU to UAVs do not need to be continuously maintained.

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.

This work was supported in part by the National Natural Science Foundation of China, China, under Grant 71601181, in part by the Young Talents Lifting Project, China, under Grant 17JCJQQT048, in part by the Huxiang Young Talents, China, under Grant 2018RS3079, and in part by the Complex Situational Cognitive Technology under Grant 315050202.

In the field of unmanned aerial vehicle (UAV) swarm control, achieving efficient consensus is paramount. This paper proposes a molecular dynamics-based UAV swarm consensus control strategy. The strategy emulates the random motion of molecules in a vacuum, establishing a framework for UAV swarm behavior that aligns with principles from molecular dynamics. The framework is built upon the Vicsek model, a cornerstone in swarm dynamics, and employs the Lennard-Jones and Morse potential functions to model the attractive and repulsive forces between UAVs. Through simulation, this paper explores how different potential functions and swarm sizes affect consensus control, finding the Morse potential particularly advantageous. Theoretical analysis and experimental results demonstrate that these potential functions not only prevent UAV collisions but also facilitate the emergence of swarming behaviors, thereby enhancing the collaborative efficiency and stability of UAV swarms. This advancement significantly boosts the overall performance of swarm control, paving the way for the deployment of UAV swarms in complex environments.

In 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.

An unmanned aerial vehicle (UAV) swarm is a fast-moving system where self-adaption is necessary when conducting a mission. The major causative factors of mission failures are inevitable disruptive events and uncertain threats. Given the unexpected disturbances of events and threats, it is important to study how a UAV swarm responds and enable the swarm to enhance resilience and alleviate negative influences. Cooperative adaptation must be established between the swarm’s structure and dynamics, such as communication links and UAV states. Thus, based on previous structural adaptation and dynamic adaptation models, we provide a co-adaptation model for UAV swarms that combines a swarm’s structural characteristics with its dynamic characteristics. The improved model can deal with malicious events and contribute to a rebound in the swarm’s performance. Based on the proposed co-adaptation model, an improved resilience metric revealing the discrepancy between the minimum performance and the standard performance is proposed. The results from our simulation experiments show that the surveillance performance of a UAV swarm bounces back to its initial state after disruptions happen in co-adaptation cases. This metric demonstrates that our model can contribute towards the swarm’s overall systemic resiliency by withstanding and resisting unpredictable threats and disruptions. The model and metric proposed in this article can help identify best practices in improving swarm resilience.

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.

In this paper, a novel real-time path planning algorithm for the unmanned aerial vehicle (UAV) swarm, based on improved artificial potential field method (APFM), is proposed to solve the issue that the traditional path planning method for a single UAV is not suitable for distributed UAV swarm. In this algorithm, UAV is not only subjected to the typical attraction of the target and repulsion of the obstacle in APFM but also to other forces. The attraction among UAV swarm is imported to keep the UAV swarm formation relatively compact, and the repulsion among UAV swarm is imported to prevent collisions between them, and the repulsion by the new obstacle is imported to solve the problem of incomplete reconnaissance obstacle outside the defense area. This method inherits APFM’s advantages, such as fast calculation speed, high real-time performance, and small memory occupation. The simulation results show the local extremum problem of a single UAV subjected to the attraction equal to the repulsion in APFM is solved. UAV swarm can reach their targets in tight formation. They can well avoid obstacles and new obstacles, and avoid collision among UAV swarm. All those verify the effectiveness of the algorithm.KeywordsUAV swarmDistributed systemReal-time path planningFormation constraintImproved APFM

The Internet of Things (IoT) can be supported by multiple isolated sensor networks (MISN) when the connectivity of IoT is constrained by obstacles. Mobile nodes have been used as relays to connect partitioned networks. However, mobile nodes have the problem of not being able to move or moving slowly when encountering obstacles. In order to tackle this issue, three types of Unmanned Aerial Vehicle (UAV) swarm modes are proposed, and UAV swarm assisted connectivity enhancement algorithms (UsCE) are designed. A UAV swarm with high degree of freedom and flexibility provides a new way in IoT for solving the above connectivity problem. Our target is to find an optimal solution that minimizes the number of UAVs in the swarm and maximizes the connection time of MISN. We divide the working modes of a UAV swarm into hovering and flying. Firstly, the ground sink nodes are classified by a MISN's sink node classification algorithm to generate hovering points for the UAV swarm. Secondly, the results are optimized and adjusted by a minimum UAV swarm hovering connection algorithm to obtain an optimal solution under the hovering mode. Finally, we achieve an optimal connectivity when the UAV swarm works in flying mode through the UAV swarm flight connectivity algorithm and compare it with two previously proposed algorithms. Simulation results show that the complexity of the algorithms is low, the connection time of MISN increases significantly, and the number of UAVs is small. An optimal UAV swarm assisted connectivity enhancement scheme for MISN of different scales is derived.

Autonomous inspection of large geographical areas is a central requirement for efficient hazard detection and disaster management in future cyber-physical systems such as smart cities. In this regard, exploiting unmanned aerial vehicle (UAV) swarms is a promising solution to inspect vast areas efficiently and with low cost. In fact, UAVs can easily fly and reach inspection points, record surveillance data, and send this information to a wireless base station (BS). Nonetheless, in many cases, such as operations at remote areas, the UAVs cannot be guided directly by the BS in real- time to find their path. Moreover, another key challenge of inspection by UAVs is the limited battery capacity. Thus, realizing the vision of autonomous inspection via UAVs requires \emph{energy-efficient path planning} that takes into account the energy constraint of each individual UAV. In this paper, a novel path planning algorithm is proposed for performing energy-efficient inspection, under stringent energy availability constraints for each UAV. The developed framework takes into account all aspects of energy consumption for a UAV swarm during the inspection operations, including energy required for flying, hovering, and data transmission. It is shown that the proposed algorithm can address the path planning problem efficiently in polynomial time. Simulation results show that the proposed algorithm can yield substantial performance gains in terms of minimizing the overall inspection time and energy. Moreover, the results provide guidelines to determine parameters such as the number of required UAVs and amount of energy, while designing an autonomous inspection system.

The resilience of unmanned aerial vehicle (UAV) swarm is its joint capability to resist possible threat, adapt to disruptive events, and restore its intended performance under a specific time period. The quantitative assessment of the UAV swarm resilience requires a thorough understanding of its missions. In this paper, a mission-oriented framework is proposed to implement the resilience evaluation for the UAV swarm. Guided by the framework, the resilience evaluation for the UAV swarm performing joint reconnaissance mission is studied. A UAV swarm model is developed for joint reconnaissance mission based on complex networks and agent-based models. The following aspects of the UAV swarm are considered in the proposed model, namely, the mission orientation, UAV attributes, swarm topology, UAV cooperative strategy, UAV information exchange and fusion strategy, potential threats, recovery strategies, etc. Then, a novel performance metric is proposed to measure the mission capability of the UAV swarm performing joint reconnaissance mission. Results from the simulations show that, compared with existing studies, the proposed approach can provide more realistic and objective resilience evaluation for the mission-oriented UAV swarm. The above works can be used to support the decision making and the optimal design of the UAV swarm, given different missions.

In order to enhance the secrecy transmission of unmanned aerial vehicle (UAV) swarm, a novel physical-layer key generation (PLKG) efficient UAV swarm trajectory planning method is proposed in this work. Different from the existing UAV trajectory planning methods that only focus on collision avoidance, energy saving and improving transmission quality, the proposed method incorporates the PLKG capability into the UAV trajectory design. Based on the findings of the fact that the wireless scattering experienced by UAV signals from different UAV trajectories are typically distinct, the channel randomness oriented PLKG behaves differently for various UAV trajectories. As a result, we propose to incorporate the PLKG metric into the UAV trajectory planning objectives, in order to improve the secrecy of UAV swarm transmissions. The theoretical derivation between UAV swarm PLKG metric and the wireless scattering entropy is obtained, and we propose a channel entropy knowledge sharing mechanism for UAV swarm to achieve PLKG efficient swarm trajectory planning. Simulation results show that the key generation performance can be largely improved in comparison to the traditional secrecy rate maximization oriented UAV swarm trajectory planning.

In the existing cellular networks, it remains a challenging problem to communicate with and control an unmanned aerial vehicle (UAV) swarm with both high reliability and low latency. Due to the UAV swarm's high working altitude and strong ground-to-air channels, it is generally exposed to multiple ground base stations (GBSs), while the GBSs that are serving ground users (occupied GBSs) can generate strong interference to the UAV swarm. To tackle this issue, we propose a novel two-phase transmission protocol by exploiting cellular plus device-to-device (D2D) communication for the UAV swarm. In Phase I, one swarm head is chosen for ground-to-air channel estimation, and all the GBSs that are not serving ground users (available GBSs) transmit a common control message to the UAV swarm simultaneously, using the same cellular frequency band. Both the swarm head and other swarm members can utilize the high power gain from multiple available GBSs' transmission, to combat the strong interference from occupied GBSs, while some UAVs may fail to decode the message due to uncorrelated ground-to-air channels. In Phase II, all the UAVs that have decoded the message in Phase I further relay it to the other UAVs in the swarm via D2D communication, by exploiting the less interfered D2D frequency band and the proximity among UAVs. In this paper, we aim to characterize the reliability performance of the above two-phase transmission protocol, i.e., the expected percentage of UAVs in the swarm that can decode the common control message, which is a non-trivial problem due to the complex system setup and the intricate coupling between the two transmission phases. Nevertheless, we manage to obtain an approximated expression of the reliability performance of interest, under reasonable assumptions and with the aid of the Pearson distributions. Numerical results validate the accuracy of our analytical results and show the effectiveness of our proposed protocol over other benchmark protocols. We also study the effect of key system parameters on the reliability performance, to reveal useful insights on the practical design of cellular-connected UAV swarm communication.

Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition. However, due to the lack of continuous connections between the UAV swarm and ground base stations (BSs), using centralized ML will be challenging, particularly when dealing with a large volume of data. In this paper, a novel framework is proposed to implement distributed federated learning (FL) algorithms within a UAV swarm that consists of a leading UAV and several following UAVs. Each following UAV trains a local FL model based on its collected data and then sends this trained local model to the leading UAV who will aggregate the received models, generate a global FL model, and transmit it to followers over the intra-swarm network. To identify how wireless factors, like fading, transmission delay, and UAV antenna angle deviations resulting from wind and mechanical vibrations, impact the performance of FL, a rigorous convergence analysis for FL is performed. Then, a joint power allocation and scheduling design is proposed to optimize the convergence rate of FL while taking into account the energy consumption during convergence and the delay requirement imposed by the swarm's control system. Simulation results validate the effectiveness of the FL convergence analysis and show that the joint design strategy can reduce the number of communication rounds needed for convergence by as much as 35% compared with the baseline design.

Velocity controllers for a swarm of unmanned aerial vehicles