Swarm Of Vehicles Research Articles

Towards secure and resilient unmanned aerial vehicles swarm network based on blockchain

AbstractBlockchain, as an emerging technology, has found diverse applications in solving trust issues in multi‐party cooperation. The field of Unmanned Aerial Vehicle (UAV) swarm networks is no exception, as UAVs are susceptible to attacks from malicious counterparts due to limited computing and storage resources. In this paper, the authors present an adaptive networking mechanism for UAVs based on blockchain technology, which incorporates three key technologies. Utilizing Global Unique Identifier (GUID) and block storage, UAVs can achieve rapid identity authentication and efficient swarm switching. By evaluating node participation in block consensus, malicious nodes within the UAV swarm can be accurately identified. Additionally, routing learning based on node security attributes and link quality ensures secure and efficient data transmission. Simulation test results demonstrate the practicality and applicability of the proposed blockchain‐based adaptive networking mechanism for UAV swarm networks.

A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be realized to expand the detection range, and more complex tasks can be completed without human participation, so as to improve work efficiency and reduce the consumption of manpower and material resources. In AUSCNs, clustering is a key method to lower energy consumption. However, there is a challenge to select cluster heads in AUSCNs because of the limitation of transmission distances and the complexity of network topological structures. In addition, this problem has been confirmed as NP-hard. In this paper, a new niche adaptive elite evolutionary algorithm (NAEEA) is proposed to solve this problem. NAEEAs can search within various complicated stochastic situations at high speeds with characterized high precision and fast convergence. This algorithm integrates the merits of elite selection and adaptive adjusting to achieve high performance. In NAEEAs, a new adaptive operator is designed to speed up the convergence rate, while a novel elite operator is proposed to avoid local optima and raise the exploration ability. Furthermore, a new niche operator is also proposed to increase the population diversity. The simulation results show that, compared with an evolutionary algorithm (EA), a simulated annealing algorithm (SA) and a leapfrog algorithm (SFLA), clustering energy consumption based on an NAEEA is significantly reduced, and the network energy consumption of the AUSCN is up to 21.43%, 25.00% and 25.76% lower than the other three algorithms, respectively.

Performance Analysis of Visual–Inertial–Range Cooperative Localization for Unmanned Autonomous Vehicle Swarm

The swarm of small UAVs is an emerging technology that will enable abundant cooperative tasks. To tackle the positioning problem for the UAV swarm, cooperative localization (CL) has been intensively studied since it uses relative measurement to improve the positioning availability and accuracy for the swarm in GPS-denied environments. Besides relying on inter-UAV range measurement, traditional CL algorithms need to place anchors as location references, which limits their applicability. To implement an infrastructure-less swarm navigation system, a consumer-grade camera together with an inertial device can provide rich environment information, which can be recognized as a kind of local location reference. This paper aims to analyze the fundamental performance of visual–inertial–range CL, which is also a popular metric for UAV planning and sensing optimizing, especially for resource-limited environments. Specifically, a closed-form Fisher information matrix (FIM) of visual–inertial–range CL is constructed in Rn×SO(n) manifold. By introducing an equivalent FIM and utilizing of the sparsity of the FIM, the performance of pose estimation can be efficiently calculated. A series of numerical simulations validate its effectiveness for analyzing the CL performance.

Neural Swarm Control Algorithm for Underwater Vehicles

The paper presents the application of an evolutionary recurrent neural network to control the swarm of underwater vehicles. In the swarm, one vehicle is the leader and the others are followers. The leader leads the swarm along a predefined trajectory without regard for the followers while the followers follow the leader and avoid collisions with all other vehicles. Avoiding collisions by the swarm with external obstacles is done by changing the depth. The leader is responsible for detecting the obstacles and informing all the followers about the need to change the depth. To follow the leader, the followers use the information about the distance to it. Directional information is unavailable to them. To avoid collisions inside the swarm, the followers use shortrange sensors.

Stop & Offload: Periodic data offloading in UAV networks

Swarms of Unmanned Aerial Vehicles (UAVs) are a key technology to support communication in many harsh environments where fixed infrastructures (e.g., 5G) are disrupted or not available. However, the fast mobility and highly dynamic network topology pose unique challenges and require the development of novel multi-hop routing protocols. Previous work in this direction extends geographical protocols or adapts approaches designed for Mobile Ad-hoc NETworks (MANETs), rarely taking full advantage of UAV capabilities. In this paper, we introduce a novel data offloading approach, namely Stop & Offload, that exploits the device controllable mobility to facilitate network routing. The swarm of UAVs performs data offloading synchronously and recurrently. At fixed intervals of time, the swarm interrupts the sensing mission (Stop) and moves, as little as possible, to build a connected formation to the base station and offload the data (Offload). We provide both centralized solutions — assuming a long-range control channel — and a distributed solution — working in the absence of a control channel. By means of extensive simulations we show that our proposals outperform state-of-the-art solutions, decreasing the time taken to build a connected formation of about 45% and increasing the time spent on sensing of 10%. Additionally, we compared our protocol with various routing strategies and observe remarkable improvements, including a 50% reduction in average packet delay.

A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms

Accurate localization is the foundation of unmanned aerial vehicle (UAV) swarm applications in the global navigation satellite system (GNSS)-denied environment. However, the implementation of UAV formation in the real world is costly and time-consuming, which leads to difficulties in developing navigation algorithms. A real-time simulator for navigation in GNSS-denied environments is proposed, which includes world, model, controller, scene matching navigation (SMN), relative navigation and formation controller modules. Each module can be modified, which means that users can test their own algorithms. A novel inertial-aided SMN (ISMN) algorithm is developed and a relative navigation method that does not rely on inter communication is proposed. ISMN and relative navigation based on a camera and ultrawideband (UWB) are tested on the platform. Based on the developed simulation system, the navigation algorithms can be verified easily, which can reduce the time and personnel requirements during flight testing.

High-Resolution L-Band TomoSAR Imaging on Forest Canopies with UAV Swarm to Detect Dielectric Constant Anomaly.

A rigorous TomoSAR imaging procedure is proposed to acquire high-resolution L-band images of a forest in a local area of interest. A focusing function is derived to relate the backscattered signals to the reflectivity function of the forest canopies without resorting to calibration. A forest voxel model is compiled to simulate different tree species, with the dielectric constant modeled with the Maxwell-Garnett mixing formula. Five different inverse methods are applied on two forest scenarios under three signal-to-noise ratios in the simulations to validate the efficacy of the proposed procedure. The dielectric-constant profile of trees can be used to monitor the moisture content of the forest. The use of a swarm of unmanned aerial vehicles (UAVs) is feasible to carry out TomoSAR imaging over a specific area to pinpoint potential spots of wildfire hazards.

Nuclearization of Indian Ocean Region Implications for Strategic Stability in South Asia

This research focuses on nuclearization of the Indian Ocean Region (IOR) by India and its impact on strategic stability between India and Pakistan. The research question addressed in this paper is how nuclearization of IOR by India can have negative implications for South Asian strategic stability? With the help of major powers through Military Logistic Support Agreements and by being a member of alliances like Quadrilateral Security Dialogue, India will have access to more sophisticated technologies in the IOR. This Indian naval nuclear and military exceptionalism will assist India to use coercive strategies and nuclear blackmail vis-à-vis Pakistan. This will negatively impact strategic stability between India and Pakistan. Pakistan will have to consequently acquire assured second-strike capability as well as, invest in antisubmarine warfare capabilities including swarms of unmanned underwater vehicles controlled by artificial intelligence. The findings indicate that as India will not revise its naval nuclear ambitions, Pakistan must work for a progressing economic outlook so that it can opt for assured second strike capability in the future. The research methodology utilized in this article is qualitative. Both primary and secondary sources of data were employed in this research. Interviews were conducted for the collection of primary data.

Relative Localization within a Quadcopter Unmanned Aerial Vehicle Swarm Based on Airborne Monocular Vision

Swarming is one of the important trends in the development of small multi-rotor UAVs. The stable operation of UAV swarms and air-to-ground cooperative operations depend on precise relative position information within the swarm. Existing relative localization solutions mainly rely on passively received external information or expensive and complex sensors, which are not applicable to the application scenarios of small-rotor UAV swarms. Therefore, we develop a relative localization solution based on airborne monocular sensing data to directly realize real-time relative localization among UAVs. First, we apply the lightweight YOLOv8-pose target detection algorithm to realize the real-time detection of quadcopter UAVs and their rotor motors. Then, to improve the computational efficiency, we make full use of the geometric properties of UAVs to derive a more adaptable algorithm for solving the P3P problem. In order to solve the multi-solution problem when less than four motors are detected, we analytically propose a positive solution determination scheme based on reasonable attitude information. We also introduce the maximum weight of the motor-detection confidence into the calculation of relative localization position to further improve the accuracy. Finally, we conducted simulations and practical experiments on an experimental UAV. The experimental results verify the feasibility of the proposed scheme, in which the performance of the core algorithm is significantly improved over the classical algorithm. Our research provides viable solutions to free UAV swarms from external information dependence, apply them to complex environments, improve autonomous collaboration, and reduce costs.

Distributed Robust Formation Tracking Control for Quadrotor UAVs with Unknown Parameters and Uncertain Disturbances

In this paper, the distributed formation tracking control problem of quadrotor unmanned aerial vehicles is considered. Adaptive backstepping inherently accommodates model uncertainties and external disturbances, making it a robust choice for the dynamic and unpredictable environments in which unmanned aerial vehicles operate. This paper designs a formation flight control scheme for quadrotor unmanned aerial vehicles based on adaptive backstepping technology. The proposed control scheme is divided into two parts. For the position subsystem, a distributed robust formation tracking control scheme is developed to achieve formation flight of quadrotor unmanned aerial vehicles and track the desired flight trajectory. For the attitude subsystem, an adaptive disturbance rejection control scheme is proposed to achieve attitude stabilization during unmanned aerial vehicle flight under uncertain disturbances. Compared to existing results, the novelty of this paper lies in presenting a disturbance rejection flight control scheme for actual quadrotor unmanned aerial vehicle formations, without the need to know the model parameters of each unmanned aerial vehicle. Finally, a quadrotor unmanned aerial vehicle swarm system is used to verify the effectiveness of the proposed control scheme.

The formation of unmanned aerial vehicle swarm

The Unmanned Aerial Vehicles (UAVs) have played a much more important role in daily life, ranging from entertainment and military use, in which case UAV swarm is adopted as means to lower the operation cost, as well as to compensate for the insufficient performance of a single drone. However, sharp distinctions between individual drones and UAV swarm exist not only in the field of controlling, but in the area of aerodynamics as well. To conclude, the operator, instead of a team of engineers, needs to perform severe tasks with the help of several drones which act as a swarm rather than interfere with each other. Meanwhile, in order to improve the efficiency of UAV swarm, different formations are looked into from different fields, mainly aerodynamics and control engineering. This review will mainly focus on the aerodynamics performance of formations of swarming UAVs, both in close formation flight and extended formation flight. Comparisons will also be made to show the merits and demerits of these formations, providing convenience for path planning of different tasks.

Collaborative Search and Package Delivery Strategy for UAV Swarms Under Area Restrictions

The rapid implementation of multi-task decoupling in restricted flight areas for unmanned aerial vehicle swarms is crucial to ensure swarm effectiveness. This study introduces a task-switching mechanism in the bio-inspired rule-based (Bio-RB) decision-making algorithm and establishes a mapping relationship from behavioral rules to task modes. A complete decision model is constructed for the cooperative search and package delivery tasks. To further improve the search efficiency of swarms in restricted areas, a boundary-handling strategy based on the combination of path prediction and virtual agents is proposed. The overall scheme is termed the task-driven rule-based (Task-RB) decision-making algorithm. The proposed Task-RB method is evaluated under full-flow simulation. Numerical experiments demonstrate the superior performance of the proposed Task-RB method against the Bio-RB method under different instances.

A decentralized decision-making algorithm of UAV swarm with information fusion strategy

Centralized and decentralized unmanned aerial vehicle (UAV) swarms rely on networked communication to exchange information among individuals and generate cooperative behaviors. This study proposes an Information-Fusion based Decentralized Swarm Decision Algorithm (IFDSDA) to coordinate UAV swarms when communication is interfered with or fails. Each UAV has the ability to perceive the area ahead using a monocular camera. An information fusion strategy is presented to unify the two types of information obtained from communication and visual perception in a standardized manner. This mechanism enables UAVs to fully utilize different information in cases of communication failure. Each UAV is controlled by a decentralized swarm decision module that takes the fused information as inputs and generates the heading orientation by combining basic action rules of UAVs. The weight parameters of the combination are optimized using the heuristic genetic algorithm in an offline manner. The collision/obstacle avoidance and area search missions are used as test scenarios for the proposed IFDSDA. Simulations with high confidence are conducted, which demonstrate the effectiveness, inherent scalability, and robustness of the proposed method, comparing it with the state-of-the-art ISOA method and its variant. This study reduces the dependence on network communication within the swarm and improves the adaptability of UAV swarms in complex battlefield environments.

Robust Leaderless Time-Varying Formation Control for Nonlinear Unmanned Aerial Vehicle Swarm System With Communication Delays.

This article investigates the tracking-oriented robust leaderless time-varying formation (TVF) control problem for unmanned aerial vehicle swarm systems (UAVSSs) with Lipschitz nonlinear dynamics under directed topology, where external disturbances are random and bounded, and communication delays (CDs) are bounded. In this article, a state-feedback control approach is adopted to make sure that a UAVSS forms a desired TVF and follows a specified trajectory when CDs and external disturbances occur. First, a novel PD-like formation control protocol with several unknown parameters and CDs is designed. The protocol contains the information of the local neighborhood status and its differential quantities. Second, the tracking-oriented robust leaderless TVF control problem with Lipschitz dynamics, external disturbances, and CDs is transformed into a problem about asymptotic stability of a lower dimensional closed-loop control system through a special matrix decomposition. Third, a theorem is proposed to determine the unknown parameters of the control protocol and the upper bound of CDs. In the theorem, sufficient conditions for a UAVSS to attain the anticipated TVF and trajectory tracking are obtained. A Lyapunov-Krasovskii (LK) functional is constructed to verify that the error among the practical flight state of UAVs, the anticipant TVF configuration, and tracking trajectory can asymptotically converge to 0. Finally, with the presentation of a simulation case, the effectiveness of the theoretical results is illustrated.

Dynamic operations and maintenance of an unmanned aerial vehicle swarm for continuous emergency communication

Unmanned aerial vehicle (UAV)-aided continuous emergency communications have recently emerged as a key solution to provide data transmission for disaster areas, thanks to their flexible deployment and high mobility. In practice, due to the limited onboard energy and state deterioration, UAVs need energy supplement and maintenance. However, existing researches mainly focus on UAV deployment and rarely study policies related to their operations and maintenance. To ensure the continuous and reliable execution of communication tasks, a dynamic operations and maintenance policy is proposed to assign tasks and determine maintenance activities for UAVs. First, a dynamic operations and maintenance policy composed of a task assignment policy and a maintenance policy is proposed. Next, the dynamic operations and maintenance joint optimization problem is formulated as a Markov decision process (MDP) to optimize the performance of the UAV swarm, including coverage, fairness, operations and maintenance cost. Then, a deep reinforcement learning approach is tailored to optimize the proposed MDP, where the repeated states are eliminated by state preprocessing, and an action mask method is utilized to satisfy operational constraints. Finally, the proposed approach is tested by its application in the operations and maintenance of a UAV swarm for continuous emergency communication.

Q-Learning based system for Path Planning with Unmanned Aerial Vehicles swarms in obstacle environments

Path Planning methods for the autonomous control of Unmanned Aerial Vehicle (UAV) swarms are on the rise due to the numerous advantages they bring. There are increasingly more scenarios where autonomous control of multiple UAVs is required. Most of these scenarios involve a large number of obstacles, such as power lines or trees. Despite these challenges, there are also several advantages; if all UAVs can operate autonomously, personnel expenses can be reduced. Additionally, if their flight paths are optimized, energy consumption is reduced, leaving more battery time for other operations. In this paper, a Reinforcement Learning-based system is proposed to solve this problem in environments with obstacles by utilizing Q-Learning. This method allows a model, in this case, an Artificial Neural Network, to self-adjust by learning from its mistakes and successes. Regardless of the map’s size or the number of UAVs in the swarm, the goal of these paths is to ensure complete coverage of an area with fixed obstacles for tasks like field prospecting. Setting goals or having any prior information apart from the provided map is not required. During the experimentation phase, five maps of varying sizes were used, each with different obstacles and a varying number of UAVs. To evaluate the quality of the results, the number of actions taken by each UAV to complete the task in each experiment was considered. The results indicate that the system achieves solutions with fewer movements as the number of UAVs increases. An increasing number of UAVs on a map lead to solutions in fewer moves. The results have been compared, and a statistical significance analysis has been conducted on the proposed model’s outcomes, demonstrating its capabilities. Thus, it is shown that a two-layer Artificial Neural Network used to implement a Q-Learning algorithm is sufficient to operate on maps with obstacles.

Hierarchical clustering cooperation flocking based on feedback mechanism

The mathematical theory of flocking underlies several omnipresent multi-agent phenomena in biology, ecology, sensor networks, and economics, and also plays an important role in the study of swarm intelligence. Motivated by the results of recent research on flocking estimates, we study a class of hierarchical clustering cooperative models based on feedback mechanism. First, we present hierarchical clustering cooperative models with feedback. By means of new differential inequality techniques and Lyapunov functional method, sufficient criteria on the asymptotic convergence of these models are obtained. Meanwhile, the appropriate initial data that allows collision avoidance between agents are presented. Further, similar results are extended to the case where some pairs of agents do not have direct communication, but the whole system is connected. Moreover, the free-will agents are added to the clustering cooperative models, and unconditional convergence is proved. Combined clustering cooperative flocking with virtual structure, a scenario is simulated with unmanned aerial vehicle (UAV) swarms accomplishing coordinated control with multiple formations. The introduction of the cluster-structure and feedback mechanism makes the flocking models more suitable for practical applications.

Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process.

The rapid advancement and increasing number of applications of Unmanned Aerial Vehicle (UAV) swarm systems have garnered significant attention in recent years. These systems offer a multitude of uses and demonstrate great potential in diverse fields, ranging from surveillance and reconnaissance to search and rescue operations. However, the deployment of UAV swarms in dynamic environments necessitates the development of robust experimental designs to ensure their reliability and effectiveness. This study describes the crucial requirement for comprehensive experimental design of UAV swarm systems before their deployment in real-world scenarios. To achieve this, we begin with a concise review of existing simulation platforms, assessing their suitability for various specific needs. Through this evaluation, we identify the most appropriate tools to facilitate one's research objectives. Subsequently, we present an experimental design process tailored for validating the resilience and performance of UAV swarm systems for accomplishing the desired objectives. Furthermore, we explore strategies to simulate various scenarios and challenges that the swarm may encounter in dynamic environments, ensuring comprehensive testing and analysis. Complex multimodal experiments may require system designs that may not be completely satisfied by a single simulation platform; thus, interoperability between simulation platforms is also examined. Overall, this paper serves as a comprehensive guide for designing swarm experiments, enabling the advancement and optimization of UAV swarm systems through validation in simulated controlled environments.

Compact and ordered swarms of unmanned aerial vehicles in cluttered environments.

The globally coordinated motion produced by the classical swarm model is typically generated by simple local interactions at the individual level. Despite the success of these models in interpretation, they cannot guarantee compact and ordered collective motion when applied to the cooperation of unmanned aerial vehicle (UAV) swarms in cluttered environments. Inspired by the behavioral characteristics of biological swarms, a distributed self-organized Reynolds (SOR) swarm model of UAVs is proposed. In this model, a social term is designed to keep the swarm in a collision-free, compact, and ordered collective motion, an obstacle avoidance term is introduced to make the UAV avoid obstacles with a smooth trajectory, and a migration term is added to make the UAV fly in a desired direction. All the behavioral rules for agent interactions are designed with as simple a potential function as possible. And the genetic algorithm is used to optimize the parameters of the model. To evaluate the collective performance, we introduce different metrics such as (a) order, (b) safety, (c) inter-agent distance error, (d) speed range. Through the comparative simulation with the current advanced bio-inspired compact and Vasarhelyi swarm models, the proposed approach can guide the UAV swarm to pass through the dense obstacle environment in a safe and ordered manner as a compact group, and has adaptability to different obstacle densities.

Research on Unmanned Aerial Vehicle Cluster Collaborative Countermeasures Based on Dynamic Non-Zero-Sum Game under Asymmetric and Uncertain Information

Unmanned aerial vehicle (UAV) swarm coordinated confrontation is a hot topic in academic research at home and abroad, and dynamic maneuver decision-making is one of the most important research fields for UAV countermeasures. Aiming at the complexity, uncertainty and confrontation of UAV cooperative confrontation, concepts such as relative advantage degree and advantage coefficient are introduced, and game theory is used as a framework to construct a dynamic non-zero-sum game UAV cluster cooperative confrontation decision-making model, and finally convert it into an optimization problem. On this basis, using the Nash equilibrium solution method of multi-strategy fusion particle swarm algorithm, by introducing adaptive inertia weight and local mutation strategy, while enhancing the diversity of the population, it can ensure the local accurate search ability of the particle swarm. The simulation results of the example are verified. The effectiveness of the proposed model and method is confirmed.