Unmanned Aerial Vehicles Formation Research Articles

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.

Distributed Navigation in Emergency Scenarios: A Case Study on Post-Avalanche Search and Rescue Using Drones

Unmanned aerial vehicles (UAVs) are increasingly employed in civil applications due to their ease of use and adaptability. This paper proposes a distributed navigation strategy for a formation of UAVs in post-avalanche search-and-rescue (SAR) operations. Formations offer a more efficient approach than single UAVs in dynamic and complex operational environments. Additionally, they can distribute different sensors, reducing payload and increasing robustness and overall efficiency. The proposed navigation algorithm relies on the Kalman filter (KF) based on consensus to distribute state estimation, and internodal transformation theory to improve system scalability, preserving the dynamic equivalence between the global and local models. The effectiveness of this approach was tested in two realistic scenarios, resulting in the ability to detect victims and maintain situational awareness while avoiding unsearched areas. The proposed approach offers a promising alternative to human-intensive SAR missions.

Robust Localization for Secure Navigation of UAV Formations Under GNSS Spoofing Attack

Nowadays, aerial formations are frequently employed in outdoor scenarios to cooperatively explore and monitor wide areas of interest. In these applications, the vehicles are often exposed to relevant security vulnerabilities, as, for instance, the alteration of navigation signals from an attacker with map counterfeiting (if not even hijacking) purposes. In this work, we focus on an Unmanned Aerial Vehicle (UAV) formation that monitors an area, wherein navigation spoofing attacks may occur. Letting the UAVs cooperate and exploiting the redundancy in the available sensing information, a distributed procedure is proposed to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i)$</tex-math> </inline-formula> detect spoofing attacks, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ii)$</tex-math> </inline-formula> support the navigation in adverse conditions. The validity of the designed approach is confirmed by numerical results. Aerial vehicles for outdoor operation are generally endowed with inertial measurements, relative ranging, and GNSS sensing capability. In this work, two cascaded estimation algorithms for concurrent GNSS spoofing detection and localization in a multi-UAV scenario is proposed, to attain robust navigation in areas subject to GNSS spoofing attacks. The attack detection leverages on information theoretic tools to provide a practical threshold test by checking the multimodal measurement consistency. The localization procedures exploit a decision logic relying on measurement reliability to combine information sources that are different in nature, for UAV self-localization in both safe and under-attack conditions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Aerial vehicles for outdoor operation are generally endowed with inertial measurements, relative ranging, and GNSS sensing capability. In this work, two cascaded estimation algorithms for concurrent GNSS spoofing detection and localization in a multi-UAV scenario is proposed, to attain robust navigation in areas subject to GNSS spoofing attacks. The attack detection leverages on information theoretic tools to provide a practical threshold test by checking the multimodal measurement consistency. The localization procedures exploit a decision logic relying on measurement reliability to combine information sources that are different in nature, for UAV self-localization in both safe and under-attack conditions.

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.

A Spatial Registration Method for Multi-UAVs Based on a Cooperative Platform in a Geodesic Coordinate Information-Free Environment

The satellite navigation system of Unmanned Aerial Vehicles (UAVs) is susceptible to external interference in a complex environment, resulting in the loss of their own geodetic coordinate information. A spatial registration method for multi-UAVs based on a cooperative platform in a geodesic coordinate information-free environment is proposed to solve this problem. The mutual observation information between UAVs is approximated by the observation information of the cooperative platform. Indirect observation information of the target can be obtained on account of mutual observation. On the basis of this, a close-range spatial registration algorithm without the geodetic coordinate information of UAVs is designed by means of the right-angle translation method. Finally, the Kalman filtering technique is used to track maritime targets. In this paper, the proposed method is verified by a simulation experiment and a practical experiment. The proposed method is 90% effective in reducing systematic errors. The tracking accuracy after alignment is significantly better than that of the original trajectory.

Reconfiguration for UAV Formation: A Novel Method Based on Modified Artificial Bee Colony Algorithm

The flight formation of unmanned aerial vehicles (UAVs) needs to be reconfigured whenever necessary to cope with complex environments and varying tasks. However, the continuity, nonlinearity and high dimensionality of the UAV formation control parameters bring significant challenges to the efficiency and safety of UAV formation reconfiguration. To this end, this paper proposes a reconfiguration strategy of the UAV formation based on a modified Artificial Bee Colony (ABC) algorithm, which ensures superior efficiency and safety level simultaneously. Specifically, we first formulate the formation reconfiguration problem minimizing the time consumed for reconfiguration under the constraints of safety and connection. Then the continuous optimization problem is discretized by using the control parameterization and time discretization (CPTD) method. Finally, we use a modified ABC algorithm to find the solution of formation reconfiguration. Extensive performance evaluations are conducted to verify the superiority of the proposed method. It is concluded that the proposed algorithm achieves a better performance than the existing approaches in literature in solving the problem of 3-D formation reconfiguration.

Generalized projective control for leader-following multi-agent systems with predefined-time

Formation flight is the most typical collaborative scenario of Unmanned Aerial Vehicles (UAVs) clusters, which can be used to perform various complex tasks. The most fundamental issue in UAVs formation flying is the consistency problem. In this paper, we study the predefined-time group generalized projective synchronization of the leader-follower multi-agent systems with different dimensions of the states. A simple controller is designed based on a predefined-time stability theory. Regardless of the initial value, the controller can bring followers with different dimensions to the same state as the leader within a predefined-time. Furthermore, we explore the generalized projective synchronization and group generalized projective synchronization, where the predefined-time parameters in the controller can take arbitrary values. Finally, three simulations are given to demonstrate the validity of main results.

Event‐triggered cooperative control for moving target encirclement and tracking with time‐varying pattern by UAV formation

AbstractThis paper investigates an event‐triggered cooperative control policy of moving‐target encirclement and tracking with time‐varying radius commands for unmanned aerial vehicle (UAV) formation. Different from the existing works, this study takes into account the relative motion model between the UAVs and the target in order to design a guidance law specifically tailored for moving‐target encirclement and tracking. Additionally, a time‐varying radius command is introduced, allowing the radius of the circular orbit formed by the UAVs around the target to adjust according to the commands. This flexibility and adaptability enhance the UAV formation's performance in complex work scenes and unstructured geometric spaces. To address the concerns of communication and computing costs associated with time‐triggered guidance laws, an event‐triggered method is proposed for dynamic target encirclement and tracking, eliminating the occurrence of Zeno behaviour. The stability of the proposed guidance laws is mathematically proven using the interconnected systems lemma. Finally, the effectiveness of the controllers is verified by some simulations.

Distributed Adaptive Path-Following Control for Distance-Based Formation of Fixed-Wing UAVs under Input Saturation

This paper investigates the distance-based formation and cooperative path-following control problems for multiple fixed-wing unmanned aerial vehicles (UAVs). In this study, we design the distance-based formation control structure to achieve the virtual leader and followers pre-defined rigid formation pattern, ensuring simultaneously relative localization. A path-following control strategy based on adaptive dynamic surface and neural network control technology is proposed to approximate the uncertain disturbances of the environment and unmodeled dynamics. And the longitudinal and lateral subsystems’ adaptive fault-tolerant controllers are designed, respectively, to achieve the fault-tolerant control of UAVs’ formation in three-dimensional environments. Furthermore, the adaptive sliding mode controller with an auxiliary controller is designed to realize the UAVs path following with limited input saturation. Finally, simulation examples are given to clarify and verify the effectiveness of the theoretical results.

Heterogeneous Mission Planning for Multiple UAV Formations via Metaheuristic Algorithms

Unmanned aerial vehicle (UAV) formation has been widely applied in various aspects, both for military and civil purposes. In this paper, multiple UAV formations are deployed to perform different types of tasks, and the heterogeneous mission planning problem is studied. The problem is divided into two subproblems, i.e., task assignment and path planning. First, the task assignment is formulated into a combinatorial optimization problem. Different from the existing studies, appropriate UAVs need to be selected to build the formation and perform specific types of tasks. To obtain the task assignment scheme, a hybrid genetic and simulated annealing (HGSA) algorithm is proposed. In this algorithm, a feasible solution is guaranteed by the designed task-based strategy, and the diversity of solutions is ensured by the group-based solution remaining (GBSR) approach. In the path planning problem, an obstacle avoidance-enabled consensus (OAEC) algorithm is developed to form the UAV formation, which extends the application of the standard consensus algorithm. A multi-step particle swarm optimization (MPSO) algorithm is combined with the consensus algorithm to generate a path for the UAV formation to reach each task point. The conflict among multiple UAV formations is resolved by adjusting their departure time. Furthermore, the task assignment scheme is modified by utilizing real path information on the flying distance between the UAVs and the task points. The simulation results demonstrate that the HGSA algorithm can build the UAV formations and assign the tasks to them while satisfying all the complicated constraints. The advantages of the OAEC algorithm and the MPSO algorithm in path planning are verified by comparison with relevant algorithms.

Multi-Agent Bifurcation Consensus-Based Multi-Layer UAVs Formation Keeping Control and Its Visual Simulation

This paper focuses on the mult-layer unmanned aerial vehicles (UAVs) formation keeping control issue based on multi-agent bifurcation consensus. Taking the two-layer formation as an example, due to delays in receiving and responding to signals, the intra-layer and inter-layer control schemes under the dynamic properties need to be considered. Firstly, the UAV heading autopilot is designed to fully consider the control of the angle of attack and yaw angle, which is utilized to deal with the translational and angular motion of UAV. Based on the heading autopilot, the stability and Hopf bifurcation conditions of UAVs heading dynamic system are discussed to get real-time state information and generate control input. Secondly, the leader-following formation combined with the virtual control structure to obtain a virtual hierarchical control strategy, and then a bifurcation consensus criterion. The results shown that when UAVs heading control system achieves bifurcation consensus. The formation maintains the shape while showing interesting periodic oscillation motion behavior. Finally, a numerical example is presented to demonstrate the effectiveness of the presented control strategy. Particularly, visualization simulation using 3ds Max software makes the results more intuitive and flexible.

Dynamic Mission Planning Algorithm for UAV Formation in Battlefield Environment

The limited time duration and spatial instability of TSTs increase the difficulty of mission planning for unmanned aerial vehicle (UAV) formations, which in turn affects the task execution abilities of UAVs. Aiming at this problem, a multi-UAV dynamic mission planning algorithm based on the time window mechanism is proposed under a distributed formation structure. The proposed algorithm comprehensively considers a variety of complex constraints involved in actual application scenarios and constructs a UAV formation mission planning model with minimum task execution cost and path planning cost as the optimization goal. In this work, particle swarm optimization algorithm is improved by introducing a specific particle coding method and a competitive co-evolutionary population updating strategy, to enhance the solution speed and accuracy of the mission planning model. In this way, the optimal task allocation strategy and task execution path are obtained to meet the mission requirements, and to achieve the multi-UAV cooperative reconnaissance, strike and evaluation of multiple TSTs. Furthermore, the effectiveness of the proposed algorithm is validated via simulations.

Cooperative attack based on small-unit UAV swarms formation with trajectory tracking

Cooperative attack with unmanned aerial vehicles (UAVs) plays a critical role in modern military warfare. To achieve multi-swarm cooperative attack with obstacle avoidance of formation, this paper proposes a cooperative control strategy that integrates flight control and autonomous marshaling. Firstly, an improved dynamics model with virtual leader-following mode is constructed to achieve obstacle avoidance of the formation. And an improved interference fluid dynamic system (IIFDS) is applied to improve path selectivity during multi-swarm attack. Secondly, a two-layer attack framework based on distributed swarm coordinated trajectory tracking with heading angle constraints is designed to achieve autonomous clustering of the UAVs and target striking. Finally, the proposed improved dynamics model is compared with the particle swarm optimization (PSO) algorithm and artificial potential field (APF) method in terms of obstacle avoidance of formation to demonstrate its superiority, which can obtain better benefits. Furthermore, two simulations of multi-swarm cooperative attack are conducted to validate the effectiveness of the control strategy. The proposed method expands the application of UAVs attack with obstacle avoidance of formation and provides a valuable reference for modern military operations.

Delay-Informed Intelligent Formation Control for UAV-Assisted IoT Application.

Multiple unmanned aerial vehicles (UAVs) have a greater potential to be widely used in UAV-assisted IoT applications. UAV formation, as an effective way to improve surveillance and security, has been extensively of concern. The leader-follower approach is efficient for UAV formation, as the whole formation system needs to find only the leader's trajectory. This paper studies the leader-follower surveillance system. Owing to different scenarios and assignments, the leading velocity is dynamic. The inevitable communication time delays resulting from information sending, communicating and receiving process bring challenges in the design of real-time UAV formation control. In this paper, the design of UAV formation tracking based on deep reinforcement learning (DRL) is investigated for high mobility scenarios in the presence of communication delay. To be more specific, the optimization UAV formation problem is firstly formulated to be a state error minimization problem by using the quadratic cost function when the communication delay is considered. Then, the delay-informed Markov decision process (DIMDP) is developed by including the previous actions in order to compensate the performance degradation induced by the time delay. Subsequently, an extended-delay informed deep deterministic policy gradient (DIDDPG) algorithm is proposed. Finally, some issues, such as computational complexity analysis and the effect of the time delay are discussed, and then the proposed intelligent algorithm is further extended to the arbitrary communication delay case. Numerical experiments demonstrate that the proposed DIDDPG algorithm can significantly alleviate the performance degradation caused by time delays.

Intelligent deep learning‐aided future beam and proactive handoff prediction model in Unmanned Aerial Vehicle‐assisted anti‐jamming Terahertz communication system

SummaryWireless communications often suffer from legitimate transmissions regarding malicious jamming attacks launched through the smart jammer. The drone or unmanned aerial vehicle (UAV) communication networks derived with reconfigurable intelligent surfaces (RIS) increase the issues of beam selection and proactive handoff in terahertz (THz). Thus, a new heuristic strategy is designed for efficient and incorporated optimization of the beamforming vector and anti‐jamming transmit power allocation in undefined environments. Here, the transmit power allocation and beamforming matrix of UAV are optimized with the developed hybrid heuristic algorithm of the Hybrid Crow Black Widow Search Optimization (HCBWSO) algorithm for maximizing the system achievable rate. Here, the HCBWSO algorithm is implemented to integrate with the Crow search algorithm (CSO) and Black Widow Optimization (BWO). The second contribution is to adopt RIS into THz–UAV communications, a new Enhanced Deep Temporal Convolutional Network (EDTCN) for predicting the future beam and proactive handoff of UAVs based on their prior analysis of the UAV locations, where the HCBWSO algorithm is utilized for recommending EDTCN. Here, the training of the EDTCN needs to be done with the collection of UAV information from the DEEPMIMO dataset for predicting the future beams and, also, tracking the location of the UAV. EDTCN helps in increasing the possibility of expanding the UAV coverage and also increases the consistency of the THz communication system. Thus, the prediction of the future beam increases the coverage area of the UAV and also maximizes the system rate in the THz communication system.

Angle-Constrained Formation Maneuvering of Unmanned Aerial Vehicles

In a global positioning system (GPS)-denied environment, unmanned aerial vehicles (UAVs) rely on local sensing-based formation maneuvering approaches for collective motion. To improve mission efficiency by reducing the total sensing requirements on all UAVs, this article proposes a leader–follower formation maneuvering framework with two leaders, where the followers will track the two leaders and maintain a desired angle-constrained formation with respect to the leaders using direction-only measurements, i.e., removing the need for inter-UAV distance measurements for the followers. To enable the UAV formation to maneuver in translation, rotation, and scaling simultaneously, the desired formation shape is specified by a set of interior angle constraints. By assigning each UAV’s yaw angle and position as the four controllable variables, an estimation-based attitude control algorithm is designed. For the UAVs’ position control, the designed formation maneuvering algorithm consists of a velocity tracking part and a formation shape control part that enables the first leader UAV to control the translational maneuvering, the second leader UAV to control the rotational and scaling maneuvering, and all the follower UAVs to maintain the formation shape. Simulations and experiments on UAVs’ formation maneuvering are conducted to illustrate the effectiveness of the proposed approach.

Weight Factor Graph Co-Location Method for UAV Formation Based on Navigation Performance Evaluation

At present, multiple unmanned aerial vehicles (UAVs) collaboration have attracted people’s more and more attention. The abundant relative distance information in UAV cooperative network can be employed to further improve UAV’s positioning accuracy. However, the traditional distance-based cooperative localization (CL) algorithm does not consider the performance of cooperative navigation information. Cooperative information with poor navigation performance will affect follower UAV’s state estimation accuracy. Thus, this paper proposes a weight factor graph CL method based on navigation performance evaluation. A novel navigation performance evaluation strategy based on Fisher information and relative entropy is proposed. The distance observations and cooperative nodes’ positions with higher estimation accuracy and contribution are selected to estimate follower UAV’s position. Considering the difference contributions of the selected cooperative information to follower UAV’s state estimation, we employ the information weight processing to achieve higher state estimation accuracy. Based on the selected cooperative information and weight coefficient, a multi-UAVs cooperative optimization graph model based on navigation performance is constructed. The global optimization cost function is constructed using relative distance constraints from the current time to historical time in the sliding window. The global optimal navigation solution of follower UAV is achieved using iterative weighted least squares method. Simulation and experiment results show that the proposed navigation performance evaluation strategy can effectively screen cooperative information with higher estimation accuracy and contribution for CL. Using the selected high-quality cooperative information, the system computational complexity is reduced without significantly affecting system positioning accuracy.

A Cooperative Localization Method for Leader–Follower Multiple UAVs Using Continuous Relative Ranging Information

Cooperative localization (CL) utilizing multiple unmanned aerial vehicles (UAVs) has become the current development trend and research hotspot. The traditional distance-based CL method typically uses principle of the spherical intersection to achieve UAV high-precision positioning. However, this distance-based CL strategy requires several leader UAVs at the known positions. Thus, a cooperative localization method for leader–follower multiple UAVs using continuous relative ranging information is proposed in this paper. Aiming at the UAV formation consists of a leader UAV node with the known position and at least a follower UAV that needs to be located. Using the Inertial short-time high-precision relative position constraint and the continuous relative distance constraints with the leader UAV nodes, the closed-form solution of follower UAV’s three-dimensional position can be obtained using spherical intersection principle. On this basis, we derive and construct follower UAV’s position observation equation. The developed strategy requires a single known position leader UAV node to realize high-precision CL, which can effectively solve the traditional distance-based collaborative location problem in case the number of leader UAVs is small. The simulation and experimental results demonstrate that the proposed method significantly improves follower UAV’s positioning accuracy, and neglects the limitation of the traditional distance-based CL method on the number of leader UAV nodes at known positions.

A warm-started trajectory planner for fixed-wing unmanned aerial vehicle formation

In this paper, an optimal control-based method with efficient warm-start is proposed to solve the trajectory planning problem for fixed-wing unmanned aerial vehicle formations. We first formulate the trajectory planning problem as an optimal control problem by analyzing the motion equations and various constraints. Secondly, an improved artificial potential field method is designed to meet the requirements of eliminating local oscillations, inter-formation coordination, and physical constraints with the objective of a high-quality homotopic warm-start. Last, a loose formation constraint strategy is employed to expand the solution space and thus facilitate the convergence process. Numerical simulations at different complexity levels show that the proposed planner is efficient and robust and that the designed warm-start strategy contributes significantly to the convergence of the method. Also, the proposed planner exhibits promising performance in solving the problem with multiple formations.

Distributed control of target cooperative encirclement and tracking using range‐based measurements

AbstractThis paper investigates a distributed coordinate‐free control method of ground target cooperative encirclement and tracking for an unmanned aerial vehicle (UAV) formation. Different from the existing methods, the relevant information for the circumnavigation can be measured by the UAV sensors rather than directly from the tracked target. Based on these distance measurements, a multi‐UAV encirclement guidance law that satisfies both target coordination and tracking is designed. According to the proposed lemma of interconnected system, the formation system under the guidance law is globally uniformly asymptotically stable. The effectiveness and advantages of the proposed controller are validated via simulations.