Unmanned Aerial Vehicles Formation Research Articles

Formation Control of Quadrotor UAVs

In recent years, the rapid development of quadrotor unmanned aerial vehicles (UAVs) has resulted in their widespread applications across various scenarios. However, single UAVs encounter multiple challenges during mission execution. Consequently, an increasing number of tasks are being conducted in the form of UAV formations. This paper focuses on quadrotor UAVs and proposes a formation control scheme that can be validated through simulation and experimentation. The research begins with the dynamic modeling of quadrotor UAVs and the design of an experimental platform for UAVs. The formation structure of UAVs is then developed based on the Leader-Follower theory, and a multi-type PID controller is employed to design the formation control algorithm.

UAV formation control using enhanced behavior mechanism and artificial potential field

Inspired by formation flight of pigeon flock, this paper proposes a enhanced method of autonomous formation control of multiple Unmanned Aerial Vehicles (UAVs) that can maintain high symmetry based on pigeon flock behavior mechanism. Addressing the instability of formation in the original method, the follow improvements have been made. Firstly, improve leadership of top three UAVs, Secondly, modify artificial potential field strategies for top two followers. Finally, through a series of simulation experiments, it is verified that the UAVs can form the expected formation under the autonomous formation control, and can maintain the formation under the complex motion of leader UAV.

UAVs Path Planning based on Combination of Rapidly Exploring Random Tree and Rauch-Tung-Striebel Filter

Aiming at the problem of Unmanned Aerial Vehicle(UAV) formation path planning under complex constraints, a UAV formation path planning method based on the combination of Rapidly exploring Random Tree (RRT) and Rauch-Tung-Striebel (RTS) filter is proposed. Firstly, a path planning algorithm based on the improved RRT algorithm with adaptive step size is de-signed to solve the problem that the RRT algorithm is easy to fall into local optimum. Then, an RTS filter is introduced to smooth the trajectory planned by the improved RRT algorithm to achieve curvature continuity. Finally, taking the smooth trajectory as the reference, a UAV formation path planning algorithm over the Artificial Potential Field (APF) method is designed. The simulation results show that the designed UAV formation path planning algorithm can solve the planning problems of single trajectory and formation trajectories in complex constrained space, and can plan the formation trajectory with continuous curvature, to facilitate the UAV trajectory tracking control.

Extended State Observer-Based Sliding-Mode Control for Aircraft in Tight Formation Considering Wake Vortices and Uncertainty

The tight formation of unmanned aerial vehicles (UAVs) provides numerous advantages in practical applications, increasing not only their range but also their efficiency during missions. However, the wingman aerodynamics are affected by the tail vortices generated by the leading aircraft in a tight formation, resulting in unpredictable interference. In this study, a mathematical model of wake vortex was developed, and the aerodynamic characteristics of a tight formation were simulated using Xflow software. A robust control method for tight formations was constructed, in which the disturbance is first estimated with an extended state observer, and then a sliding mode controller (SMC) was designed, enabling the wingman to accurately track the position under conditions of wake vortex from the leading aircraft. The stability of the designed controller was confirmed. Finally, the controller was simulated and verified in mathematical simulation and semi-physical simulation platforms, and the experimental results showed that the controller has high tight formation accuracy and is robust.

Predictor-based constrained fixed-time sliding mode control of multi-UAV formation flight

In this work, a predictor-based fixed-time sliding mode control is designed to tackle the problem of achieving precise trajectory tracking control of multiple unmanned aerial vehicle formation flight. The proposed approach simultaneously addresses various practical requirements, such as optimality, constraints, fixed-time convergence, external interferences, and computational burden. Initially, using the Taylor series concept, we derive the predictive models for the fixed-time sliding surface and reaching law. Subsequently, we formulate a constrained performance index by integrating these predictions with the control input. Ultimately, the optimal control input is computed through solving the constrained quadratic optimization problem. An outstanding feature of this study is the theoretical guarantee of closed-loop fixed-time system stability in the presence of external disturbances and constraints. Furthermore, the comparative analysis with sliding mode predictive control law and fixed-time sliding mode control methods demonstrates that the proposed approach achieves a straightforward control method with improved performance and acceptable accuracy. Finally, an experimental test is conducted to demonstrate the algorithm's potential for hardware implementation by employing the Hardware-in-the-Loop test.

Research on optimal deep learning‐based formation flight positioning of UAV

SummaryThe article examines the positional relationships and transmission/reception signal direction information within unmanned aerial vehicle (UAV) cluster formations. Considering the distinctive characteristics of various formation shapes (such as circle and cone), a study is conducted on the polar and Cartesian coordinate systems in neural network models to address position deviation issues arising in UAV formation flights. The passive receiving signal UAV positioning model is established by applying the triangular cosine theorem. Additionally, the position relationship model of the UAV is formulated using optimization theory. The article introduces a deep learning‐based deviated UAV adjustment algorithm designed to automatically adjust UAV positions. This is achieved by establishing a triangular relationship between the passive signal receiving UAV and other UAVs. The implementation involves C# programming and MATLAB visualization. The proposed method not only resolves UAV positioning and position adjustment challenges but also optimizes the adjustment strategy. This optimization leads to maximum savings in time and distance costs associated with sending and receiving signals among UAVs, thereby enhancing the overall performance of UAVs during flights.

Prescribed time formation synchronous tracking of unmanned aerial vehicles with denial of service attacks

This paper addresses the issue of robust prescribed time synchronous tracking control of unmanned aerial vehicles (UAVs) formation with denial of service attacks. A novel prescribed time synchronous tracking control scheme is proposed within a denial of service attack conversion mechanism. Firstly, on account of the UAV formation in the presence of aerodynamic damping and denial of service attacks, a formation cooperative control model is established under a second-order system. Secondly, to perform formation synchronous tracking within a settling time, a time-varying auxiliary function on account of synchronous state errors is designed for the convergence of prescribed time tracking. The control parameters are designed using a key eigenvalue scaling technique under the effect of denial of service attacks on network topology. The proposed scheme is also extended to UAV formation with a detailed-balanced graph. Finally, simulation results are presented to demonstrate the effectiveness of the proposed strategy.

Integrated the Artificial Potential Field with the Leader–Follower Approach for Unmanned Aerial Vehicles Cooperative Obstacle Avoidance

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.

Formation fault-tolerant control for multiple UAVs with external disturbances

PurposeThe purpose of this paper is to construct an event-triggered finite-time fault-tolerant formation tracking controller, which can achieve a time-varying formation control for multiple unmanned aerial vehicles (UAVs) during actuator failures and external perturbations.Design/methodology/approachFirst, this study developed the formation tracking protocol for each follower using UAV formation members, defining the tracking inaccuracy of the UAV followers’ location. Subsequently, this study designed the multilayer event-triggered controller based on the backstepping method framework within finite time. Then, considering the actuator failures, and added self-adaptive thought for fault-tolerant control within finite time, the event-triggered closed-loop system is subsequently shown to be a finite-time stable system. Furthermore, the Zeno behavior is analyzed to prevent infinite triggering instances within a finite time. Finally, simulations are conducted with external disturbances and actuator failure conditions to demonstrate formation tracking controller performance.FindingsIt achieves improved performance in the presence of external disturbances and system failures. Combining limited-time adaptive control and event triggering improves system stability, increase robustness to disturbances and calculation efficiency. In addition, the designed formation tracking controller can effectively control the time-varying formation of the leader and followers to complete the task, and by adding a fixed-time observer, it can effectively compensate for external disturbances and improve formation control accuracy.Originality/valueA formation-following controller is designed, which can handle both external disturbances and internal actuator failures during formation flight, and the proposed method can be applied to a variety of formation control scenarios and does not rely on a specific type of UAV or communication network.

A distributed containment tracking control of the UAV formation with prescribed performance subject to collision avoidance

SummaryIn this paper, the issue of finite‐time containment tracking control under collision avoidance constraints for unmanned aerial vehicle (UAV) formation is investigated. To tackle the challenge of collision avoidance in the context of formation containment tracking, combined with a prescribed performance function, a distributed containment tracking control scheme is proposed. Firstly, a UAV formation containment control model from a top view perspective is established. Next, to solve the internal collision avoidance within the formation, a restricted movement region is constructed for each UAV using a distributed prescribed performance function, which guarantees that each UAV's trajectory is maintained within a predefined area. Furthermore, invoking with the finite‐time control theory for the formation, a distributed containment tracking control scheme is developed to complete finite‐time tracking of the formation. Finally, simulation results are carried out to demonstrate the efficacy of the proposed approach.

MADRL-based UAV swarm non-cooperative game under incomplete information

Unmanned Aerial Vehicles (UAVs) play increasing important role in modern battlefield. In this paper, considering the incomplete observation information of individual UAV in complex combat environment, we put forward an UAV swarm non-cooperative game model based on Multi-Agent Deep Reinforcement Learning (MADRL), where the state space and action space are constructed to adapt the real features of UAV swarm air-to-air combat. The multi-agent particle environment is employed to generate an UAV combat scene with continuous observation space. Some recently popular MADRL methods are compared extensively in the UAV swarm non-cooperative game model, the results indicate that the performance of Multi-Agent Soft Actor-Critic (MASAC) is better than that of other MADRL methods by a large margin. UAV swarm employing MASAC can learn more effective policies, and obtain much higher hit rate and win rate. Simulations under different swarm sizes and UAV physical parameters are also performed, which implies that MASAC owns a well generalization effect. Furthermore, the practicability and convergence of MASAC are addressed by investigating the loss value of Q-value networks with respect to individual UAV, the results demonstrate that MASAC is of good practicability and the Nash equilibrium of the UAV swarm non-cooperative game under incomplete information can be reached.

Leader–follower UAVs formation control based on a deep Q-network collaborative framework

This study examines a collaborative framework that utilizes an intelligent deep Q-network to regulate the formation of leader–follower Unmanned Aerial Vehicles (UAVs). The aim is to tackle the challenges posed by the highly dynamic and uncertain flight environment of UAVs. In the context of UAVs, we have developed a dynamic model that captures the collective state of the system. This model encompasses variables like as the relative positions, heading angle, rolling angle, and velocity of different nodes in the formation. In the subsequent section, we elucidate the operational procedure of UAVs in a collaborative manner, employing the conceptual framework of Markov Decision Process (MDP). Furthermore, we employ the Reinforcement Learning (RL) to facilitate this process. In light of this premise, a fundamental framework is presented for addressing the control problem of UAVs utilizing the DQN scheme. This framework encompasses a technique for action selection known as ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon$$\\end{document}-imitation, as well as algorithmic specifics. Finally, the efficacy and portability of the DQN-based approach are substantiated by numerical simulation validation. The average reward curve demonstrates a satisfactory level of convergence, and kinematic link between the nodes inside the formation satisfies the essential requirements for the creation of a controller.

UV communication cooperative formation UAV alliance capture algorithm.

The capture of target unmanned aerial vehicles (UAVs) by a UAV formation is one of the important and typical tasks in multi-UAV battlefield operations. In this paper, an ultraviolet (UV) light communication-assisted formation UAV alliance capture algorithm is proposed, which combines UV light communication technology with a capture algorithm. With the help of wireless UV light to assist UAV formation inter-UAV data confidentiality transmission and non-line-of-sight communication, the algorithm integrates the alliance generation algorithm with the region minimization strategy, solves the optimal alliance structure by using the dynamic programming method, and implements the aerial capture of the target UAVs by using the region minimization strategy, so as to complete the task of efficiently capturing multi-targets by the UAV formation in complex scenarios. Simulation comparisons were conducted between the region minimization strategy and the proposed UV communication-assisted formation UAV alliance capture algorithm. The results show that the proposed algorithm reduces energy consumption by 12.73% on average and decreases the average number of algorithm iterations by 27.49% during the UAV formation capture of multiple targets, which verifies its low energy consumption and high capture efficiency.

Cooperative navigation of unmanned aerial vehicle formation with delayed measurement

Abstract This paper focused on the problem of positioning accuracy degradation caused by delayed measurement information in Unmanned Aerial Vehicle (UAV) formation cooperative navigation under complex environments such as cities and hills, and presented a non-synchronous compensation algorithm based on kinematic constraints and constructed a distributed cooperative navigation filter based on the analysis of the basic operating characteristics of inertial devices, satellite receivers, and ranging sensors. In the UAV formation, the leader-UAV is equipped with Real-Time Kinematic (RTK) differential equipment and airborne data link to construct the airborne reference beacons and provide cooperative navigation services for the wingmen-UAV. Firstly, the navigation filtering framework with inertial sensors as the core is established. Secondly, the non-synchronous compensation filter is constructed by using the kinematic constraint model, which compensates and corrects the non-synchronous air-based position of the leader-UAV, and reduces the effect of delayed measurement on the positioning error of the system. Then the fault diagnosis algorithm is utilized to complete the identification and rejection of abnormal range values in the case of Non-Line-of-Sight (NOLS). Finally, the navigation parameters are solved by the Kalman filter. Simulation results show that the non-synchronous compensated filtering proposed in this paper can improve the absolute positioning accuracy by 55%, which effectively improves the cooperative navigation performance and robustness under the presence of random time delay in the measurement information.

Fault prediction of unmanned aerial vehicles based on entropy weight fusion and temporal graph convolutional network with non-Euclidian structure

Predicting weak and hidden faults in unmanned aerial vehicles (UAVs) is challenging due to their variable operation conditions and complex mechanisms. Conventional neural network models process the multisensory data in the form of Euclidean structure, the intrinsic connections among the individual data points are easy to be disregarded. Additionally, multisensory data are always directly fed into the model without adequately considering the importance or contribution of each sensor. Hence, an UAV fault prediction method is proposed by combining entropy weight fusion with a temporal graph convolutional network (GCN) to address the above problems. Firstly, the importance of multisensory data of UAVs are evaluated by each entropy value, and the multisensory data fusion is further realized by multiplying corresponding signal and entropy weight. Secondly, the multisensory data combined with fusion data are transferred together into graph-structure by adjacent matrix based on the node connection between different sensor data. Finally, the graph-structure data with non-Euclidian distance properties are input into temporal GCN to both capture the spatial and temporal relationship of the data, achieving better fault prediction results of UAVs. It is demonstrated that the proposed method is both applicable and superior in characterizing and predicting fault time series information of UAVs through parameter analysis and comparison studies with various existing algorithms.

Determination of multi-UAVs formation shape: Using a requirement satisfaction and spherical fuzzy ANP based TOPSIS approach

Multiple unmanned aerial vehicles (multi-UAVs) formation shape refers to the geometric shape when multi-UAVs fly in formation and describes their relative positions. It plays a necessary role in multi-UAVs collaboration to improve performance, avoid collision, and provide reference for control. This study aims to determine the most appropriate multi-UAVs formation shape in a specific mission to meet different and even conflicting requirements. The proposed approach introduces requirement satisfaction and spherical fuzzy analytic network process (SFANP) to improve the technique for order preference by similarity to ideal solution (TOPSIS). First, multi-UAVs capability criteria and their evaluation models are constructed. Next, performance data are transformed into requirement satisfaction of capability and unified into a same scale. Qualitative judgments are made and quantified based on spherical fuzzy sets and nonlinear transformation functions are developed for benefit, cost, and interval metrics. Then, SFANP is used to handle interrelationships among criteria and determine their global weights, which takes decision vagueness and hesitancy into account and extends decision-makers’ preference domain onto a spherical surface. Finally, alternative formation shapes are ranked by their distances to the positive and negative ideal solution according to the TOPSIS. Furthermore, a case study of 9 UAVs performing a search-attack mission is set up to illustrate the proposed approach, and a comparative analysis is conducted to verify the applicability and credibility.

Distributed Dynamic Event-Triggered Consensus Control for Multiagent Systems With Guaranteed Performance and Positive Inter-Event Times

This paper addresses the distributed consensus issue of linear multiagent systems (MASs) subject to disturbances. First, a fully distributed adaptive control law is established for each agent, removing the assumption that global information should be known prior. Additionally, unlike existing consensus studies, the restrictions on continual communication among agents and constant controller updates are eliminated through the designed state observers and event-triggered conditions (ETCs). Moreover, an innovative event-triggered condition (ETC) is created that, via the inclusion of a dynamic parameter, captures mixed triggering and time-varying thresholds as special cases. Particularly, distinct from some dwell time methods that enforce inter-event time (IET) greater than zero at the expense of asymptotic stability, positive inter-event times (IETs) and asymptotic stability can be guaranteed simultaneously by the suggested event-triggered conditions (ETCs) in the absence of disturbances. Furthermore, positive minimum inter-event times (MIETs) can be maintained even in the presence of disturbances, which indicates the robustness of the proposed event-triggered mechanism (ETM). It is shown that the suggested technique performs disturbances suppression for all agents in a completely distributed manner while omitting Zeno behavior. Finally, a simulation example demonstrates the suggested scheme’s efficacy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The multiagent consensus problem is considered in this paper, which can be applied to some practical systems, e.g., the unmanned aerial vehicles formation problems, as the control objective in these applications can be converted into the consensus problem of MASs. Different from existing results, a novel ETM is proposed, reducing agents’ communication and preventing continuous controller updates. In addition, disturbances widely exist in engineering. The proposed method ensures that the interval between two consecutive event triggers is greater than a small positive constant. Therefore, it is helpful for applying the proposed method in practical engineering. Finally, the established method is applied to a two-mass-spring control problem, and the effectiveness is illustrated. It is anticipated that the proposed approach can be extended to MASs with more realistic network-induced constraints and applied to more practical engineering systems.

Stylized Crowd Formation Transformation Through Spatiotemporal Adversarial Learning

Achieving crowd formation transformations has wide‐ranging applications in fields such as unmanned aerial vehicle formation control, crowd simulation, and large‐scale performances. However, planning trajectories for hundreds of agents is a challenging and tedious task. When modifying crowd formation change schemes, adjustments are typically required based on the style of formation change. Existing methods often involve manual adjustments at each crucial step, leading to a substantial amount of physical labor. Motivated by these challenges, this study introduces a novel generative adversarial network (GAN) for generating crowd formation transformations. The proposed GAN learns specific styles from a series of crowd formation transformation trajectories and can transform a new crowd with an arbitrary number of individuals into the same styles with minimal manual intervention. The model incorporates a space–time transformer module to aggregate spatiotemporal information for learning distinct styles of formation transformation. Furthermore, this article investigates the relationship between the distribution of training data and the length of trajectory sequences, providing insights into the preprocessing of training data.

Fixed-Wing UAV Formation Path Planning Based on Formation Control: Theory and Application

Formation path planning is a significant cornerstone for unmanned aerial vehicle (UAV) swarm intelligence. Previous methods were not suitable for large-scale UAV formation, which suffered from poor formation maintenance and low planning efficiency. To this end, this paper proposes a novel millisecond-level path planning method appropriate for large-scale fixed-wing UAV formation, which consists of two parts. Instead of directly planning paths independently for each UAV in the formation, the proposed method first introduces a formation control strategy. It controls the chaotic UAV swarm to move as a single rigid body, so that only one planning can obtain the feasible path of the entire formation. Then, a computationally lightweight Dubins path generation method with a closed-form expression is employed to plan feasible paths for the formation. During flight, the aforementioned formation control strategy maintains the geometric features of the formation and avoids internal collisions within the formation. Finally, the effectiveness of the proposed framework is exemplified through several simulations. The results show that the proposed method can not only achieve millisecond-level path planning for the entire formation but also excellently maintain formation during the flight. Furthermore, simple formation obstacle avoidance in a special case also highlights the application potential of the proposed method.

B-UAVM: A Blockchain-Supported Secure Multi-UAV Task Management Scheme

The advent of unmanned aerial vehicle (UAV) swarm technology brings possibilities to help humans complete tasks in no man’s land, such as deserts, and rainforests. However, UAV network faces many cyber threats, where attackers can impersonate legitimate entities or tamper with UAV task data. For identity security, most of the existing methods use centralized authentication schemes, which have a single point of failure problem. For data security, the existing methods only secure the task data in the ground system, ignoring the data security in the air network. Therefore, the existing methods are not suitable for ubiquitous UAV scenarios. Blockchain secures data security while eliminating the single point of failure problem, and has been widely used in distributed scenarios. In this paper, to secure entity identity and task data, we propose a Blockchain-supported Secure Multi UAV Task Management Scheme (B-UAVM). Specifically, a three-layer blockchain structure is constructed to secure multi-tasks, and achieve ubiquitous control of UAV formations. Besides, six types of blocks and three types of transactions are designed to achieve safe processing and storage of task data and entity information. Furthermore, an improved Practical Byzantine Fault Tolerance (IPBFT) consensus mechanism and a UAV-formation-action-considered ground station consensus mechanism (UFAGS) are introduced in the Server Network and Ground Control Network, respectively, to accelerate the consensus. The experimental results show that the number of transactions generated per second (TPS) of B-UAVM is about 0.5× and 3.7× of the existing method when the block size or the number of blockchain nodes increases, respectively.