UAV Formation Flight Research Articles

Research on Purely Azimuth Passive Localization Model for UAV Formation Flight

Research on Purely Azimuth Passive Localization Model for UAV Formation Flight

Multi-UAV adaptive super-twisting integral terminal sliding mode formation control

This paper addresses issues such as model uncertainties, actuator failures, and gust disturbances in multi-quadrotor UAV formation flight. An adaptive super-twisting integral terminal sliding mode control method is proposed, incorporating a ‘Leader-Follower’ formation control strategy. The approach includes designing an adaptive super-twisting integral terminal sliding mode controller and a formation adaptive cooperative controller. Additionally, adaptive gains are utilized to manage the effects of actuator failures, gust disturbances, and ground station interference, ensuring safe formation flight. Simulation and experimental results indicate that the proposed controller can track the target trajectory within a finite time and effectively maintain formation flight.

Research on pure azimuth passive localization in UAV formation flight

Research on pure azimuth passive localization in UAV formation flight

Study of UAVs in attempted formation flight based on pure azimuth passive positioning

As a great crystallization of human technology in the past hundred years, many western countries have applied high technology to the research and development of UAVs since the 1990s. In this paper, the pure azimuth passive positioning problem in attempted formation flight of UAVs is explored through the intersection of different straight lines to determine the position of the target aircraft, and the location information of the predicted and accurate points is used to optimize the target analysis. And the conclusion is drawn in minimizing the emission signal scenario of electromagnetic waves. Then, according to the knowledge of graph theory, the UAV array is fitted to a graph, a coordinate system is established with the position of one of the UAVs, and a database with standard positions is created for comparison.

Piecewise-potential-field-based path planning method for fixed-wing UAV formation

The multi-UAV path planning method based on artificial potential field (APF) has the advantage of rapid processing speed and the ability to deal with dynamic obstacles, though some problems remain—such as a lack of consideration of the initial heading constraint of the UAVs, making it easy to fall into a local minimum trap, and the path not being sufficiently smooth. Consequently, a fixed-wing UAV formation path planning method based on piecewise potential field (PPF) is proposed, where the problem of UAV formation flight path planning in different states can be solved by suitable design of the PPF function. Firstly, the potential field vector can be used to represent the potential field functions of obstacles and target points to meet the kinematic constraints of the UAV. Secondly, the local minimum region can be detected, the additional potential field vector being set to break away from this region. Finally, the change rules of the potential field vector of a UAV in the formation reconstruction scene can be designed, a smooth formation flight track being assured by adjusting the corresponding speed of each UAV track point. Considering the path planning of a five-UAV formation as an example, we conducted simulation experiments. The results showed that—compared with the existing methods based on APF—the results obtained using the PPF-based method considered the initial heading limits of the UAVs, the planned path being considerably smoother. Moreover, the proposed method could plan multiple UAV tracks, satisfying the known constraints without conflict in complex scenarios.

UAV Formation Trajectory Planning Algorithms: A Review

With the continuous development of UAV technology and swarm intelligence technology, the UAV formation cooperative mission has attracted wide attention because of its remarkable function and flexibility to complete complex and changeable tasks, such as search and rescue, resource exploration, reconnaissance and surveillance. The collaborative trajectory planning of UAV formation is a key part of the task execution. This paper attempts to provide a comprehensive review of UAV formation trajectory planning algorithms. Firstly, from the perspective of global planning and local planning, a simple framework of the UAV formation trajectory planning algorithm is proposed, which is the basis of comprehensive classification of different types of algorithms. According to the proposed framework, a classification method of existing UAV formation trajectory planning algorithms is proposed, and then, different types of algorithms are described and analyzed statistically. Finally, the challenges and future research directions of the UAV formation trajectory planning algorithm are summarized and prospected according to the actual requirements. It provides reference information for researchers and workers engaged in the formation flight of UAVs.

Bearings only passive location for UAV in formation flight

With the increasing development of UAV technology, UAV formation flight has also changed from single target task execution to multi-target cooperative task execution. When UAV is developing rapidly in the direction of clustering, there are too many external interference factors. Therefore, electromagnetic silence should be maintained for a long time in the whole formation flight, and the number of times when transmitting electromagnetic wave signals to the outside should be reduced as much as possible. Therefore, this paper uses the sine cosine theorem to adjust the position of UAV to the optimal position by using bearings only passive location method to maintain the formation of UAV multi-target flight. In this paper, the cosine theorem and sine theorem are used to locate and optimize the other UAVs that receive signals passively; By enumerating, the corresponding linear equations are listed by using the triangle formed by all UAVs; The position of each UAV shall be adjusted step by step according to the above conclusions, and the remaining UAVs receiving signals passively shall be adjusted to the optimal position to ensure the uniform distribution of nine UAVs on the circumference with a radius of 100m.

Pure azimuth passive positioning in attempted UAV formation flight

In order to maintain the formation of UAVs during flight, pure azimuthal passive positioning is generally used to adjust the position of UAVs. In order to accurately perform UAV positioning, this paper establishes a single-objective optimization model of UAV with passively received signals based on the polar coordinates of UAVs, combined with geometric knowledge such as the sine theorem and triangle properties. And then the model is optimized and improved, i.e., when the position information of two UAVs is known, how many additional UAVs are needed to perform UAV localization. Therefore, it was found that two additional UAVs with unknown numbers were needed to determine the UAV position by establishing a new set of equations to achieve the positioning of the UAVs with slightly deviated positions. The model is important to achieve effective positioning of UAVs and adjust the solution.

Resilient Path Planning of UAV Formation Flight Against Covert Attacks on Ultra-Wideband Sensors

Resilient Path Planning of UAV Formation Flight Against Covert Attacks on Ultra-Wideband Sensors

Joint Optimization of Control Strategy and Energy Consumption for Energy Harvesting WSAN.

With the rapid development of wireless sensor technology, recent progress in wireless sensor and actuator networks (WSANs) with energy harvesting provide the possibility for various real-time applications. Meanwhile, extensive research activities are carried out in the fields of efficient energy allocation and control strategy design. However, the joint design considering physical plant control, energy harvesting, and consumption is rarely concerned in existing works. In this paper, in order to enhance system control stability and promote quality of service for the WSAN energy efficiency, a novel three-step joint optimization algorithm is proposed through control strategy and energy management analysis. First, the optimal sampling interval can be obtained based on energy harvesting, consumption, and remaining conditions. Then, the control gain for each sampling interval is derived by using a backward iteration. Finally, the optimal control strategy is determined as a linear function of the current plant states and previous control strategies. The application of UAV formation flight system demonstrates that better system performance and control stability can be achieved by the proposed joint optimization design for all poor, sufficient, and general energy harvesting scenarios.

Circular Formation Guidance of Fixed-Wing UAVs Using Mesh Network

This paper presents the guidance algorithm for the circular formation flight of multiple fixed-wing UAVs. The formation guidance law aims at reducing both the radial and the phase angle error with respect to the moving reference point located at a fixed distance from the leader UAV. To this end, the proposed guidance law generates the desired course angle and the inertial speed commands for each follower UAV. The control commands are then converted to the roll angle and the airspeed commands for the autopilot. The theoretical stability proof is rigorously analyzed via the Lyaupunov method. The performance of the proposed control law is compared with the existing vector-field formation guidance scheme via numerical simulation. After the extensive validation using the hardware-in-the-loop simulation environment which includes the mesh network communication system, the flight test are performed to demonstrate not only the performance of the proposed algorithm but also the robustness to the wind disturbance.

Multi UAV Cluster Control Method Based on Virtual Core in Improved Artificial Potential Field

In this paper, a method of multi UAV cluster control based on improved artificial potential field (APF) is proposed. The k-means method is used to integrate and optimize the attractive force between UAVs, and the concept of virtual core is introduced to realize the cluster control and adaptive formation flight of multiple UAVs. The attractive disturbance component of the target point is introduced and the backtracking-filling method is proposed to solve the local minimum problem in the APF. The repulsion force in the APF can realize obstacle avoidance and collision avoidance, and the virtual core can control the UAV cluster to fly to the target point under the attractive force of potential field, so as to realize the track planning and multi aircraft cooperative task. In the process of cluster flight when the UAV fails, merges or dispatches, the method can realize cluster reconfiguration and the cluster control effect and task execution success rate can be improved. The simulation experiments in virtual APF and urban environment APF show the effectiveness of this method.

틸트로터 무인기 편대비행 시뮬레이션 연구

한국항공우주연구원에서 개발된 틸트로터 무인기의 임무비행 능력을 높이기 위하여, 무인기의 자율화 기술 5단계 수준의 편대비행에 대한 연구가 수행되었다. 편대비행은 선행기와 추종기로 구성된 중앙 집중형 방식이 적용되었다. 편대비행 제어기는 3대의 추종기를 이용한 수치 시뮬레이션과 1대의 추종기를 이용한 하드웨어 기반 시뮬레이션을 통해 검증되었다. 본 논문에서는 제어기 설계 방법, 하드웨어 기반 시뮬레이션 시험, 그리고 시뮬레이션을 통한 성능 검증에 대해서 기술하였다.

Study on the High-Speed and Close of the UVA Cooperative Formation Controller Design

Aiming at the high-speed flight of the UAVs cooperative formation, when a single UAV has occurred, need to exit the formation flight and be close or super close to form of the formation quickly. A fast close cooperative formation controller design method is proposed to make up for low the fighting robustness, and be shortcomings of timeliness poorly and analyze the dynamic characteristic of UAV formation flight. Taking the external factors known into consideration, setting up for the longitude maneuver of nonlinear thrust vector and unsteady aerodynamic model, according to the formation velocity, flat tail rudder angle and thrust vector and pitch angle velocity for corresponding input commend signals for the controller to research the dynamic characteristic of UAV formation flight. Meanwhile, the formation flight distance error is the convergence to a fixed value, and the stability of the cooperative formation flight is good. The simulation of results show that the controller can effectively improve the speed of the close or super close to formation, and maintain the stability of the formation flight, which provides a method of the close or super close formation flight controller design.

Formation flight and collision avoidance for multiple UAVs based on modified tentacle algorithm in unstructured environments.

This paper presents a method for formation flight and collision avoidance of multiple UAVs. Due to the shortcomings such as collision avoidance caused by UAV’s high-speed and unstructured environments, this paper proposes a modified tentacle algorithm to ensure the high performance of collision avoidance. Different from the conventional tentacle algorithm which uses inverse derivation, the modified tentacle algorithm rapidly matches the radius of each tentacle and the steering command, ensuring that the data calculation problem in the conventional tentacle algorithm is solved. Meanwhile, both the speed sets and tentacles in one speed set are reduced and reconstructed so as to be applied to multiple UAVs. Instead of path iterative optimization, the paper selects the best tentacle to obtain the UAV collision avoidance path quickly. The simulation results show that the method presented in the paper effectively enhances the performance of flight formation and collision avoidance for multiple high-speed UAVs in unstructured environments.

Aerodynamic Stability Analysis of Two Heterogeneous UAVs in Close Formation Flight System

The close formation flight of UAVs has many significant advantages over single vehicle flight. The aerodynamic stability analysis of two heterogeneous UAVs in close formation flight is detailed in the present paper. The issues of altitude changes and the associated shifts or changes in centre of gravity or moments, the equivalent actuator control surface deflections etc. are explained with the help of simulations. The short period frequency variations with these changes are also correlated with the help of pole-zero diagrams.

COORDINATED FLIGHT OF UNMANNED AIRCRAFTS GROUP

This paper proposes a leader following autonomous approach for UAV formation flight. The objective of UAV formation flight is that UAVs have to fly to a specified region while maintaining the distances and spatial angles between UAVs. To design the control system algorithms, the coupled dynamics of multiple UAVs is considered with assumption that each UAV follows (after) by the leader UAV. For the leader following approach algorithms of guidance system and control system algorithms are created. To verify the performance of the proposed algorithms, numerical simulation is performed for a two UAVs flight mission.The approach of autonomous flight control of unmanned aerial systems (UAS) is proposed. For the development of UAS group flight control system algorithms, is necessary to combine the dynamics of several unmanned aerial vehicles with the assumption, that each of UAV’s goes the leader. In order to check the proposed algorithms effectiveness, the numerical simulation of the flight task for two unmanned aerial vehicles was carried out.This work is confirmed, an opportunity implementation of multiple UAVs control has been provided, especially for control of UAVs formation flying, using the leader following approach. Based on this obtained algorithms most appropriative methods of virtual leader behavior method will be researched and developed.

IMPLEMENTATION FOR FLIGHT GROUPS OF UNMANNED AERIAL VEHICLES

This paper proposes a leader following decentralized, autonomous approach for UAV formation flight. The objective of UAV formation flight is that UAVs have to fly to a specified region while maintaining the distances and spatial angles between UAVs. To design the control system algorithms, the coupled dynamics of multiple UAVs is considered with assumption that each UAV follows (after) by the leader UAV. For the leader following approach algorithms of guidance system and control system algorithms are created. To verify the performance of the proposed algorithms, numerical simulation is performed for a two UAVs flight mission.This work is confirmed, an opportunity implementation of multiple UAVs control has been provided, especially for control of UAVs formation flying, using the leader following approach. Based on this obtained algorithms most appropriative methods of virtual leader behavior method will be researched and developed.For the creation neighbor-to-neighbor communication and synchronization such algorithm with using of electro optical or infrared cameras and pattern recognition approaches has been proposed and realized. Also, their approach for implementation of such control system are given, as well as algorithms and principles of works, based on appropriate mathematical models.

Research on Visual Simulation of UAV Formation Flight Control Based on Vega

This paper proposes a control method of UAV formation reconfiguration, and accomplishes simulation of the transformation of UAV formation from a defensive formation to an offensive one using MATLAB/Simulink. Then, a visual simulation platform is built to display the simulation process in the form of animation and make the results more intuitive. The platform is built by means of MFC, Vega API and MATLAB engine.

무인기의 편대비행을 위한 트랙유도 알고리즘 설계

This paper presents a modified track guidance algorithm for formation flight of multiple UAVs. The suggested guidance algorithm is the spatial version of the first order dynamic characteristics for a time-dependent system so the algorithm is able to generate a path without overshoot to track the desired line. A crucial design parameter is a spatial constant that controls the shape of the convergence to an assigned flight path similarly to a time constant. Reference flight trajectories are designed based on a two-dimensional vehicle model, and the performance of the proposed guidance law is verified by numerical simulation using rigid body UAV dynamics with MATLAB/Simulink Aerosim Blockset.