Unmanned Aerial Vehicle Swarm Systems Research Articles

The influence of neighbor selection on self-organized UAV swarm based on finite perception vision.

Recently, vision-based unmanned aerial vehicle (UAV) swarming has emerged as a promising alternative that can overcome the adaptability and scalability limitations of distributed and communication-based UAV swarm systems. While most vision-based control algorithms are predicated on the detection of neighboring objects, they often overlook key perceptual factors such as visual occlusion and the impact of visual sensor limitations on swarm performance. To address the interaction problem of neighbor selection at the core of self-organizing UAV swarm control, a perceptually realistic finite perception visual (FPV) neighbor selection model is proposed, which is based on the lateral visual characteristics of birds, incorporates adjustable lateral visual field widths and orientations, and is able to ignore occluded agents. Based on the FPV model, a neighbor selection method based on the acute angle test (AAT) is proposed, which overcomes the limitation that the traditional neighbor selection mechanism can only interact with the nearest neighboring agents. A large number of Monte Carlo simulation comparison experiments show that the proposed FPV+AAT neighbor selection mechanism can reduce the redundant communication burden between large-scale self-organized UAV swarms, and outperforms the traditional neighbor selection method in terms of order, safety, union, connectivity, and noise resistance.

Enhancing UAV Swarm Tactics with Edge AI: Adaptive Decision Making in Changing Environments

This paper presents a drone system that uses an improved network topology and MultiAgent Reinforcement Learning (MARL) to enhance mission performance in Unmanned Aerial Vehicle (UAV) swarms across various scenarios. We propose a UAV swarm system that allows drones to efficiently perform tasks with limited information sharing and optimal action selection through our Efficient Self UAV Swarm Network (ESUSN) and reinforcement learning (RL). The system reduces communication delay by 53% and energy consumption by 63% compared with traditional MESH networks with five drones and achieves a 64% shorter delay and 78% lower energy consumption with ten drones. Compared with nonreinforcement learning-based systems, mission performance and collision prevention improved significantly, with the proposed system achieving zero collisions in scenarios involving up to ten drones. These results demonstrate that training drone swarms through MARL and optimized information sharing significantly increases mission efficiency and reliability, allowing for the simultaneous operation of multiple drones.

Data-Driven Fault Detection and Diagnosis for UAV Swarm Systems

With the widely application and promotion of Unmanned aerial vehicle (UAV) technology, UAV swarms are widely used in military and civilian fields by taking advantage of group collaboration. It has huge economic and national defense value. The safety and reliability requirements of the UAV swarm system are extremely strict, and real-time fault detection and diagnosis is one of its important supporting technologies. In this paper, a fault diagnosis method based on statistical model and improved broad learning system (BLS) is proposed. The behavior characteristics of the UAV swarm system under normal and different failure modes are characterized by multivariate data statistical analysis, and the improved BLS model is adopted to achieve accurate and fast fault diagnosis. And a high-fidelity simulation verification platform is developed to verify the rationality and effectiveness of the proposed method.

Heterogeneous unmanned swarm formation containment control based on reinforcement learning

In response to the problem of time-varying spherical formation control for a heterogeneous unmanned aerial vehicle (UAV) swarm system with dynamic uncertainty in the system model, this paper proposes an optimal distributed formation containment control method based on reinforcement learning. By combining the time-varying formation containment vector design with the distributed predefined time observer and constructing the augmented system of a multi-quadrotor UAV system and an observer, the problem of time-varying formation containment control in heterogeneous swarm systems is transformed into a stabilization problem. By introducing a value function with a discount factor, the stabilization problem of a heterogeneous UAV swarm system is transformed into an optimal control problem. Using the “actor-critic” neural network, combined with an off-policy reinforcement learning algorithm and distributed control methods, the solution to the formation containment controller is achieved in a data-driven manner. The stability of distributed observer and formation containment controller as well as the convergence of reinforcement learning algorithms are demonstrated using Lyapunov and related theory. The numerical simulation results demonstrate that the observation error of the designed distributed observer converges within a predefined time of 0.1 s, while the overall formation tracking error of the heterogeneous swarm converges within 2.74 s, thereby further validating the effectiveness and superiority of the designed control scheme.

Grouping Formation and Obstacle Avoidance Control of UAV Swarm Based on Synchronous DMPC

This paper focuses on the grouping formation control problem of unmanned aerial vehicle (UAV) swarms in obstacle environments. A grouping formation and obstacle avoidance control algorithm based on synchronous distributed model predictive control (DMPC) is proposed. First, the UAV swarm is divided into several groups horizontally and into a leader layer and a follower layer vertically. Second, tracking is regarded as the objective, and collision avoidance and obstacle avoidance are considered as constraints. By combining the velocity obstacle method with synchronous DMPC and providing corresponding terminal components, a leader layer control law is designed. The control law can enable the UAV swarm to track the target while avoiding collisions and dynamic obstacles. Then, considering the formation maintenance term, based on different priorities, member-level obstacle avoidance and group-level obstacle avoidance strategies are proposed, and the corresponding follower layer control laws are provided. Furthermore, the stability of the UAV swarm system under the control algorithm is demonstrated based on the Lyapunov theory. Finally, the effectiveness of the designed algorithm and its superiority in obstacle avoidance are verified through simulations.

Distributed dynamic task allocation for unmanned aerial vehicle swarm systems: A networked evolutionary game-theoretic approach

Task allocation is a key aspect of Unmanned Aerial Vehicle (UAV) swarm collaborative operations. With an continuous increase of UAVs’ scale and the complexity and uncertainty of tasks, existing methods have poor performance in computing efficiency, robustness, and real-time allocation, and there is a lack of theoretical analysis on the convergence and optimality of the solution. This paper presents a novel intelligent framework for distributed decision-making based on the evolutionary game theory to address task allocation for a UAV swarm system in uncertain scenarios. A task allocation model is designed with the local utility of an individual and the global utility of the system. Then, the paper analytically derives a potential function in the networked evolutionary potential game and proves that the optimal solution of the task allocation problem is a pure strategy Nash equilibrium of a finite strategy game. Additionally, a PayOff-based Time-Variant Log-linear Learning Algorithm (POTVLLA) is proposed, which includes a novel learning strategy based on payoffs for an individual and a time-dependent Boltzmann parameter. The former aims to reduce the system’s computational burden and enhance the individual’s effectiveness, while the latter can ensure that the POTVLLA converges to the optimal Nash equilibrium with a probability of one. Numerical simulation results show that the approach is optimal, robust, scalable, and fast adaptable to environmental changes, even in some realistic situations where some UAVs or tasks are likely to be lost and increased, further validating the effectiveness and superiority of the proposed framework and algorithm.

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.

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.

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.

Large-Scale Fixed-Wing UAV Swarm System Control With Collision Avoidance and Formation Maneuver

In this article, a distributed control algorithm for a large-scale fixed-wing unmanned aerial vehicle (UAV) swarm system of collision avoidance and formation maneuver is proposed, which can meet the demands of a dense swarm with collaborative autonomy. First, a novel structure of a swarm is introduced, which can provide the reserved maneuverable space for UAVs during the flight. Besides, inspired by the movement of bird flocks, the light transmission model is proposed to improve the conventional potential field functions, which can realize the UAV swarm with the capability of repulsion in the short-range, speed and position cooperation in the middle range, and attraction in the remote range. Then, the distributed control architecture and the control algorithm of UAVs are designed, and the stability of the swarm is proved correspondingly. In the end, several numerical simulation results show the effectiveness and superiority of the proposed method, which illustrates the distributed control architecture of the swarm system to be more adaptable, stable, and scalable. Therefore, the proposed control algorithm provides strong theoretical support for the actual application in a large-scale fixed-wing UAV swarm system collaborative autonomy.

Active Disturbance Rejection Formation Tracking Control for Uncertain Nonlinear Multi-Agent Systems With Switching Topology via Dynamic Event-Triggered Extended State Observer

In this paper, the time-varying formation tracking (TVFT) control problem is concerned for multi-agent systems (MASs). The primary objective is to achieve asymptotical convergence for formation tracking error subject to nonparametric and nonvanishing uncertainties. Firstly, in order to estimate lumped unmodeled dynamics and external disturbances, an event- triggered fuzzy extended state observer (FESO) inspired by our previous works is established for follower group, in which efficient nonlinear observation pattern and convenient linear numerical tractability are integrated. For rationally scheduling transmission, a dynamic event-triggered mechanism is applied to form adaptively regulating strategy. Secondly, a distributed TVFT control law is developed by utilizing neighborhood formation tracking errors. Different from conventional disturbance-tolerant methodologies, total disturbance compensation is introduced to attenuate uncertainty influence in real time. Consequently, active disturbance rejection formation tracking control architecture is systematically constructed for uncertain MASs. Moreover, considering switching topology, the controller design algorithm is presented by piecewise Lyapunov analysis. Finally, the effectiveness and advantages of the proposed event-triggered FESO-based active disturbance rejection control protocol is illustrated by an numerical example on unmanned aerial vehicle swarm system.

Consensus Control of Large-Scale UAV Swarm Based on Multi-Layer Graph

An efficient control of large-scale unmanned aerial vehicle (UAV) swarm to establish a complex formation is one of the most challenging tasks. This paper investigates a novel multi-layer topology network and consensus control approach for a large-scale UAV swarm moving under a stable configuration. The proposed topology can make the swarm remain robust in spite of the large number of UAVs. Then a potential function-based controller is developed to control the UAVs in realizing autonomous configuration swarming under the consideration of mutual collision, and the stability of the controller from the individual UAV to the entire swarm system is analyzed by a Lyapunov approach. Afterwards, a yaw angle adjustment approach for the UAVs to reach consensus is developed for the multi-layer swarm, then the direction state of each UAV converges with a fast rate. Finally, simulations are performed on the large-scale UAV swarm system to demonstrate the effectiveness of the proposed scheme.

Low Latency Reliable Data Sharing Mechanism for UAV Swarm Missions

The use of Unmanned Aerial Vehicle (UAV) swarms is increasing in many commercial applications as well as military applications (such as reconnaissance missions, search and rescue missions). Autonomous UAV swarm systems rely on multi-node interhost communication, which is used in coordination for complex tasks. Reliability and low latency in data transfer play an important role in the maintenance of UAV coordination for these tasks. In these applications, the control of UAVs is performed by autonomous software and any failure in data reception may have catastrophic consequences. On the other hand, there are lots of factors that affect communication link performance such as path loss, interference, etc. in communication technology (WIFI, 5G, etc.), transport layer protocol, network topology, and so on. Therefore, the necessity of reliable and low latency data sharing mechanisms among UAVs comes into prominence gradually. This paper examines available middleware solutions, transport layer protocols, and data serialization formats. Based on evaluation results, this research proposes a middleware concept for mobile wireless networks like UAV swarm systems.

Ultraviolet-Based UAV Swarm Communications: Potentials and Challenges

Unmanned aerial vehicle (UAV) swarm communication has received increasing attention thanks to its intriguing advantages, such as low costs, high mobility, and deployment convenience. However, this technology is also facing certain new challenges, for example, RF spectrum scarcity, interference, and network connectivity. To this end, ultraviolet (UV) communication is introduced in this article to tackle these problems due to its distinctive benefits, such as license-free operation, immunity to electromagnetic interference, and the ability of non-line-of-sight transmission. First, a comparison between UV communication and other alternatives is presented. Then the structural designs of the high-capability UAV and the low-capability UAV, the system communication protocol and mutual interference, and the network topology of the UV-based UAV swarm are investigated. Further, the bit error rate performance, data rate, and communication range are analyzed. In the end, an artificial-intelligence-aided UV-based UAV swarm system is put forward to tackle users' complicated requirements. Simultaneously, the challenges are discussed to direct future research work.

Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm

The conventional direction of arrival (DOA) estimation methods are performed with multiple receiving channels. In this paper, a changeling DOA estimation problem is addressed in a different scenario with only one full-functional receiving channel. A new unmanned aerial vehicle (UAV) swarm system using multiple lifted reconfigurable intelligent surface (RIS) is proposed for the DOA estimation. The UAV movement degrades the DOA estimation performance significantly, and the existing atomic norm minimization (ANM) methods cannot be used in the scenario with array perturbation. Specifically, considering the position perturbation of UAVs, a new atomic norm-based DOA estimation method is proposed, where an atomic norm is defined with the parameter of the position perturbation. Then, a customized semi-definite programming (SDP) method is derived to solve the atomic norm-based method, where different from the traditional SDP method, an additional transforming matrix is formulated. Moreover, a gradient descent method is applied to refine the estimated DOA and the position perturbation further. Simulation results show that the proposed method achieves much better DOA estimation performance in the RIS-aided UAV swarm system with only one receiving channel than various benchmark schemes.

Robust leaderless time-varying formation control for unmanned aerial vehicle swarm system with Lipschitz nonlinear dynamics and directed switching topologies

This paper tackles the robust leaderless Time-Varying Formation (TVF) control problem for the Unmanned Aerial Vehicle (UAV) swarm system with Lipschitz nonlinear dynamics, external disturbances and directed switching topologies. In comparison with the previous achievements on formation control problems, the UAV swarm system with Lipschitz nonlinear dynamics can accomplish the pre-designed TVF while tracking a pre-given trajectory which is produced by a virtual leader UAV in the presence of external disturbances. Firstly, by applying the consensus theory, a TVF controller is developed with the local neighborhood status information, the errors of real time status of all UAVs, the expected formation configuration and the pre-given trajectory under directed switching topologies. Secondly, through a certain matrix variable substitution, the UAV swarm system formation control issue is transformed into a lower dimensional asymptotically stable control issue. Thirdly, by introducing the minimum dwell time, the design steps of formation control algorithm are further acquired. In the meantime, the stability of the UAV swarm system is analyzed through the construction of a piecewise continuous Lyapunov functional and via the Linear Matrix Inequalities (LMIs) method. Finally, the comparison results of a numerical simulation are elaborated to verify the validity of the proposed approach.

Fault-Tolerant Time-Varying Formation Tracking Control for Unmanned Aerial Vehicle Swarm Systems with Switching Topologies

This paper investigates fault-tolerant time-varying formation tracking control problems for unmanned aerial vehicle (UAV) swarm systems with switching topologies. Actuator faults such as loss of effectiveness and bias fault are mainly considered. Firstly, based on graph theory, an adaptive fault-tolerant time-varying formation tracking control protocol is constructed with adaptive updating parameters and the relative information of the neighboring UAVs, and the feasibility condition for formation tracking is given. The control protocol does not depend on the information of the actuator fault boundary by using adaptive technology. Then, by constructing a reasonable Lyapunov function and solving the algebraic Riccati equation, the stability of the designed controller is proved. For UAV swarm systems with switching topologies and actuator faults, the formation tracking control protocol designed is adopted to enable the followers form the desired time-varying formation and track the leader’s status at the same time. Finally, the simulation examples are given to illustrate the effectiveness of the theoretical results.

Bio-inspired adaptive formation tracking control for swarm systems with application to UAV swarm systems

Adaptive formation tracking problems for swarm systems with multiple leaders and switching topologies are studied in this paper. It is required that the followers form time-varying formations and track the positions of the leaders simultaneously, where the bio-inspired adaptive formation tracking control method is originated from the grey wolves with strict hierarchy and flexible communication structure. First, the grey wolf tracking strategy with four steps, including of level division, locating the prey, following the leaders, and encircling, is applied to swarm systems. Second, the adaptive formation tracking control protocol using neighboring relative state information is designed for swarm systems to achieve the adaptive formation tracking with switching topologies. Finally, the experiments of Unmanned Aerial Vehicle (UAV) swarm systems are performed using the visible simulation platform based on the Robot Operating System (ROS) and Gazebo to verify the adaptive formation tracking method. Inspired from the grey wolf tracking strategy, the adaptive formation tracking control method has been proposed, which improves the system stability and precision of the formation tracking.

Formation control of a second-order UAVs system under switching topology with obstacle/collision avoidance

In this paper, the formation control problem of a second-order unmanned aerial vehicle swarm system with switching topology and collision/obstacle avoidance has been studied. First, the linear consensus protocol for formation control with switching topology is designed using graph theory. The conditions for information consensus are given. Second, the problem of collision/obstacle avoidance is also considered. And the consensus protocol with switching topology and collision/obstacle avoidance is proposed. The improved artificial potential approach is applied to realize collision/obstacle avoidance, which can avoid potential fields falling into the local minimum. Third, system stability under the proposed protocol is proved using the Lyapunov theory. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed protocol.

Verification of a UAV swarm flight simulating the passive inertial emergency obstacle avoidance behavior of a pigeon flock

In this paper, the passive inertial emergency obstacle avoidance behavior of pigeons is studied and applied to the coordinated flight verification of an unmanned aerial vehicle (UAV) swarm. Pigeons in nature dynamically adjust their routes during flight. When they encounter obstacles, emergency behaviors are generated; i.e., they change the current direction passively, get cross obstacles toward the largest gap between obstacles, and thus complete obstacle avoidance. Prior to the flight verification, the design of the UAV swarm system architecture is explained. Further, two scenarios of static emergency obstacle avoidance on the ground and with moving obstacles in the air are set up. The corresponding timing sequence diagrams and flight path diagrams are also given.