Статті в журналах: "Fixed-Wing UAV Formations"

1

Blasi, Luciano, Egidio D’Amato, Immacolata Notaro, and Gennaro Raspaolo. "Clothoid-Based Path Planning for a Formation of Fixed-Wing UAVs." Electronics 12, no. 10 (May 12, 2023): 2204. http://dx.doi.org/10.3390/electronics12102204.

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Unmanned aerial vehicles (UAVs) are playing an increasingly crucial role in many applications such as search and rescue, delivery services, and military operations. However, one of the significant challenges in this area is to plan efficient and safe trajectories for UAV formations. This paper presents an optimization procedure for trajectory planning for fixed-wing UAV formations using graph theory and clothoid curves. The proposed planning strategy consists of two main steps. Firstly, the geometric optimization of paths is carried out using graphs for each UAV, providing piece-wise linear paths whose smooth connections are made with clothoids. Secondly, the geometric paths are transformed into time-dependent trajectories, optimizing the assigned aircraft speeds to avoid collisions by solving a mixed-integer optimal control problem for each UAV of the flight formation. The proposed method is effective in achieving suboptimal paths while ensuring collision avoidance between aircraft. A sensitivity analysis of the main parameters of the algorithm was conducted in ideal conditions, highlighting the possibility of decreasing the length of the optimal path by about 4.19%, increasing the number of points used in the discretization and showing a maximum path length reduction of about 10% compared with the average solution obtained with a similar algorithm using a graph based on random directions. Furthermore, the use of clothoids, whose parameters depend on the UAV performance constraints, provides smoother connections, giving a significant improvement over traditional straight-line or circular trajectories in terms of flight dynamics compliance and trajectory tracking capabilities. The method can be applied to various UAV formation scenarios, making it a versatile and practical tool for mission planning.

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Zhan, Guang, Zheng Gong, Quanhui Lv, Zan Zhou, Zian Wang, Zhen Yang, and Deyun Zhou. "Flight Test of Autonomous Formation Management for Multiple Fixed-Wing UAVs Based on Missile Parallel Method." Drones 6, no. 5 (April 19, 2022): 99. http://dx.doi.org/10.3390/drones6050099.

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This paper reports on the formation and transformation of multiple fixed-wing unmanned aerial vehicles (UAVs) in three-dimensional space. A cooperative guidance law based on the classic missile-type parallel-approach method is designed for the multi-UAV formation control problem. Additionally, formation transformation strategies for multi-UAV autonomous assembly, disbandment, and special circumstances are formed, effective for managing and controlling the formation. When formulating the management strategy for formation establishment, its process is divided into three steps: (i) selecting and allocating target points, (ii) forming loose formations, and (iii) forming short-range formations. The management of disbanding the formation is formulated through reverse thinking: the assembly process is split and recombined in reverse, and a formation disbanding strategy that can achieve a smooth transition from close to lose formation is proposed. Additionally, a strategy is given for adjusting the formation transformation in special cases, and the formation adjustment is completed using the adjacency matrix. Finally, a hardware-in-the-loop simulation and measured flight verification using a simulator show the practicality of the guidance law in meeting the control requirements of UAV formation flight for specific flight tasks.

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Suo, Wenbo, Mengyang Wang, Dong Zhang, Zhongjun Qu, and Lei Yu. "Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network." Applied Sciences 12, no. 2 (January 6, 2022): 535. http://dx.doi.org/10.3390/app12020535.

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The formation control technology of the unmanned aerial vehicle (UAV) swarm is a current research hotspot, and formation switching and formation obstacle avoidance are vital technologies. Aiming at the problem of formation control of fixed-wing UAVs in distributed ad hoc networks, this paper proposed a route-based formation switching and obstacle avoidance method. First, the consistency theory was used to design the UAV swarm formation control protocol. According to the agreement, the self-organized UAV swarm could obtain the formation waypoint according to the current position information, and then follow the corresponding rules to design the waypoint to fly around and arrive at the formation waypoint at the same time to achieve formation switching. Secondly, the formation of the obstacle avoidance channel was obtained by combining the geometric method and an intelligent path search algorithm. Then, the UAV swarm was divided into multiple smaller formations to achieve the formation obstacle avoidance. Finally, the abnormal conditions during the flight were handled. The simulation results showed that the formation control technology based on distributed ad hoc network was reliable and straightforward, easy to implement, robust in versatility, and helpful to deal with the communication anomalies and flight anomalies with variable topology.

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4

Muslimov, Tagir Z., and Rustem A. Munasypov. "Consensus-based cooperative control of parallel fixed-wing UAV formations via adaptive backstepping." Aerospace Science and Technology 109 (February 2021): 106416. http://dx.doi.org/10.1016/j.ast.2020.106416.

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5

Yang, Jun, Arun Geo Thomas, Satish Singh, Simone Baldi, and Ximan Wang. "A Semi-Physical Platform for Guidance and Formations of Fixed-Wing Unmanned Aerial Vehicles." Sensors 20, no. 4 (February 19, 2020): 1136. http://dx.doi.org/10.3390/s20041136.

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Unmanned Aerial Vehicles (UAVs) have multi-domain applications, fixed-wing UAVs being a widely used class. Despite the ongoing research on the topics of guidance and formation control of fixed-wing UAVs, little progress is known on implementation of semi-physical validation platforms (software-in-the-loop or hardware-in-the-loop) for such complex autonomous systems. A semi-physical simulation platform should capture not only the physical aspects of UAV dynamics, but also the cybernetics aspects such as the autopilot and the communication layers connecting the different components. Such a cyber-physical integration would allow validation of guidance and formation control algorithms in the presence of uncertainties, unmodelled dynamics, low-level control loops, communication protocols and unreliable communication: These aspects are often neglected in the design of guidance and formation control laws for fixed-wing UAVs. This paper describes the development of a semi-physical platform for multi-fixed wing UAVs where all the aforementioned points are carefully integrated. The environment adopts Raspberry Pi’s programmed in C++, which can be interfaced to standard autopilots (PX4) as a companion computer. Simulations are done in a distributed setting with a server program designed for the purpose of routing data between nodes, handling the user inputs and configurations of the UAVs. Gazebo-ROS is used as a 3D visualization tool.

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6

Wang, Yuanzhe, Mao Shan, and Danwei Wang. "Motion Capability Analysis for Multiple Fixed-Wing UAV Formations With Speed and Heading Rate Constraints." IEEE Transactions on Control of Network Systems 7, no. 2 (June 2020): 977–89. http://dx.doi.org/10.1109/tcns.2019.2929658.

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7

Yan, Jiarun, Yangguang Yu, Yinbo Xu, and Xiangke Wang. "A Virtual Point-Oriented Control for Distance-Based Directed Formation and Its Application to Small Fixed-Wing UAVs." Drones 6, no. 10 (October 12, 2022): 298. http://dx.doi.org/10.3390/drones6100298.

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This paper proposes a new algorithm to solve the control problem for a special class of distance-based directed formations, namely directed-triangulated Laman graphs. The central idea of the algorithm is to construct a virtual point for the agents who have more than two neighbors by employing the information of the desired formation. Compared with the existing methods, the proposed algorithm can make the distance error between the agents converge faster and the path consumption is less. Furthermore, the proposed algorithm is modified to be operable for the small fixed-wing UAV model with nonholonomic and input constraints. Finally, the effectiveness of the proposed method is verified by a series of simulation experiments.

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8

Kownacki, Cezary, and Leszek Ambroziak. "Adaptation Mechanism of Asymmetrical Potential Field Improving Precision of Position Tracking in the Case of Nonholonomic UAVs." Robotica 37, no. 10 (April 10, 2019): 1823–34. http://dx.doi.org/10.1017/s0263574719000286.

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SummaryPosition-tracking problems in the structures of rigid formations of nonholonomic mobile robots, such as fixed-wing unmanned aerial vehicle (UAVs), must reconcile tracking precision and flight stability, which usually exclude each other due to nonholonomic motion constraints. Therefore, a position-tracking control that is based on distance and position displacement, defined as inputs of control loops, requires the application of dead zones around target positions, which are the points of instability. For this reason, the control becomes sensitive to any external disturbance causing oscillations of control signals and so it becomes difficult to maintain a zero value of position displacement over a long time horizon. Thus, we propose an approach based on the adaptive mechanism of an asymmetrical local artificial potential field, which is defined by a local frame of reference whose origin is located in the tracked position of a UAV in the formation frame. It couples controls of both airspeed and heading angle into a nonlinear potential function of relative position and orientation with respect to the tracked position and adapts it according to heading rate of the leader. The function splits the area around the tracked position longitudinally into two zones of acceleration and deceleration; therefore, velocity vectors are longer (higher airspeed) only when a UAV is behind the tracked position and shorter (lower airspeed) when it is ahead. The area is laterally symmetrical, and orientations of velocity vectors align asymptotically to the longitudinal direction accordingly with the decrease in the lateral error. Finally, velocity vectors are rotated proportionally to the heading rate of the leader, which improves the tracking precision during turns. If we assumed that a UAV’s tracked position is in motion, it could easily be proven that the position control based on the adaptive asymmetrical potential function becomes asymptotically stable in the tracked position. Numerical simulation verifies this thesis and presents more precise and stable position tracking due to the adaptation mechanism.

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9

Muslimov, T. Z., and R. A. Munasypov. "Decentralized Nonlinear Group Control of Fixed-Wing UAV Formation." Mekhatronika, Avtomatizatsiya, Upravlenie 21, no. 1 (January 14, 2020): 43–50. http://dx.doi.org/10.17587/mau.21.43-50.

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The article proposes a control method for autonomous unmanned aerial vehicles (UAVs) group of a fixed-wing type intended to both implement and support flight information with predetermined relative distances between the vehicles. The suggested approach provides any selected geometric formation shape construction and further preservation when UAVs enter a straight-line trajectory described by a given course with arbitrary initial positions of UAVs in the horizontal plane. The proposed method feature is "autopilot—UAV" system’s nonlinear structure consideration, manifesting itself in both the autopilot input commands restrictions existence as well as nonholonomic UAV dynamics. In addition, there is an unlimited multi-UAV system scalability available due to decentralization. We take into account the need to maintain a minimum flight speed of not less than the stall speed and the final speed of the formation equal to the cruising speed of this type of UAV. The nonlinear group control laws synthesized using Lyapunov’s direct method are based on the decentralized consensus interaction topology, initially developed for linear agents, which implies each vehicle to interact with its neighboring vehicles only. Global asymptotic stability for the current control laws has been proved. As a result, proposed control laws determine a non-uniform path-following vector field for each vehicle in the whole UAV group flight space (currently two-dimensional space). The suggested field vector norm at a certain space point is the airspeed command for the vehicle at that point while the vector direction is the course angle command. The proposed approach effectiveness has been successfully tested in the MATLAB/Simulink while using realistic nonlinear six degree-of-freedom (DOF) 12-states fixed-wing UAV models. High fidelity simulation results confirm the suggested approach effectiveness.

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10

Xu, Dan, Yunxiao Guo, Zhongyi Yu, Zhenfeng Wang, Rongze Lan, Runhao Zhao, Xinjia Xie, and Han Long. "PPO-Exp: Keeping Fixed-Wing UAV Formation with Deep Reinforcement Learning." Drones 7, no. 1 (December 31, 2022): 28. http://dx.doi.org/10.3390/drones7010028.

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Flocking for fixed-Wing Unmanned Aerial Vehicles (UAVs) is an extremely complex challenge due to fixed-wing UAV’s control problem and the system’s coordinate difficulty. Recently, flocking approaches based on reinforcement learning have attracted attention. However, current methods also require that each UAV makes the decision decentralized, which increases the cost and computation of the whole UAV system. This paper researches a low-cost UAV formation system consisting of one leader (equipped with the intelligence chip) with five followers (without the intelligence chip), and proposes a centralized collision-free formation-keeping method. The communication in the whole process is considered and the protocol is designed by minimizing the communication cost. In addition, an analysis of the Proximal Policy Optimization (PPO) algorithm is provided; the paper derives the estimation error bound, and reveals the relationship between the bound and exploration. To encourage the agent to balance their exploration and estimation error bound, a version of PPO named PPO-Exploration (PPO-Exp) is proposed. It can adjust the clip constraint parameter and make the exploration mechanism more flexible. The results of the experiments show that PPO-Exp performs better than the current algorithms in these tasks.

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11

Yan, Jiarun, Yangguang Yu, and Xiangke Wang. "Distance-Based Formation Control for Fixed-Wing UAVs with Input Constraints: A Low Gain Method." Drones 6, no. 7 (June 27, 2022): 159. http://dx.doi.org/10.3390/drones6070159.

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Due to the nonlinear and asymmetric input constraints of the fixed-wing UAVs, it is a challenging task to design controllers for the fixed-wing UAV formation control. Distance-based formation control does not require global positions as well as the alignment of coordinates, which brings in great convenience for designing a distributed control law. Motivated by the facts mentioned above, in this paper, the problem of distance-based formation of fixed-wing UAVs with input constraints is studied. A low-gain formation controller, which is a generalized gradient controller of the potential function, is proposed. The desired formation can be achieved by the designed controller under the input constraints of the fixed-wing UAVs with proven stability. Finally, the effectiveness of the proposed method is verified by the numerical simulation and the semi-physical simulation.

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12

Zhu, Disha, and Jianying Yang. "Formation Control of Fixed Wing UAV with a Novel Transition Function." IFAC-PapersOnLine 55, no. 3 (2022): 154–59. http://dx.doi.org/10.1016/j.ifacol.2022.05.027.

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13

Chen, Qijie, Taoyu Wang, Yuqiang Jin, Yao Wang, and Bei Qian. "A UAV Formation Control Method Based on Sliding-Mode Control under Communication Constraints." Drones 7, no. 4 (March 27, 2023): 231. http://dx.doi.org/10.3390/drones7040231.

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The problem of vision-based fixed-wing UAV formation control under communication limitations and the presence of measurement errors was investigated. In the first part of this paper, the single UAV motion model and the process of estimating the neighboring UAV states using the Extended Kalman Filter are introduced. The second part describes how we designed a sliding mode controller considering the sensor measurement errors and discusses the sufficient conditions for the stability and formation system in the presence of state transfer time delays in the formation. The main motivation of this paper was to develop a hierarchical, globally stable sliding mode controller that could enable the considered vision-based multiple fixed-wing UAVs to achieve and maintain formation in the presence of measurement errors. To this end, the selected problem was first transformed into a state-tracking problem for UAVs in the neighborhood, and then the stability criterion was established using the Lyapunov stability theory. Finally, the effectiveness of the proposed control method was illustrated using three numerical arithmetic examples.

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14

Chen, Qi-jie, Yu-qiang Jin, Ting-long Yan, Tao-yu Wang, and Yao Wang. "UAV Formation Control under Communication Constraints Based on Distributed Model Predictive Control." Mathematical Problems in Engineering 2022 (September 20, 2022): 1–17. http://dx.doi.org/10.1155/2022/7316009.

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A distributed model predictive control method is used to transform the formation and maintenance problem of fixed-wing UAV formation during flight into an online rolling optimization problem to solve in this paper. Firstly, the state estimation model of the neighborhood UAV is established according to the relative information of the UAV. Secondly, the error state model in the three-dimensional inertial coordinate frame of the UAV is established without considering the time delay, sensor error, and external interference. Thirdly, a cost function is designed by introducing the error state of the UAV in the neighborhood. Finally, four UAVs are used to verify that under the action of the controller, the UAVs can quickly form and maintain the desired formation while tracking the reference line.

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15

Zhao, Weiwei, Hairong Chu, Xikui Miao, Lihong Guo, Honghai Shen, Chenhao Zhu, Feng Zhang, and Dongxin Liang. "Research on the Multiagent Joint Proximal Policy Optimization Algorithm Controlling Cooperative Fixed-Wing UAV Obstacle Avoidance." Sensors 20, no. 16 (August 13, 2020): 4546. http://dx.doi.org/10.3390/s20164546.

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Multiple unmanned aerial vehicle (UAV) collaboration has great potential. To increase the intelligence and environmental adaptability of multi-UAV control, we study the application of deep reinforcement learning algorithms in the field of multi-UAV cooperative control. Aiming at the problem of a non-stationary environment caused by the change of learning agent strategy in reinforcement learning in a multi-agent environment, the paper presents an improved multiagent reinforcement learning algorithm—the multiagent joint proximal policy optimization (MAJPPO) algorithm with the centralized learning and decentralized execution. This algorithm uses the moving window averaging method to make each agent obtain a centralized state value function, so that the agents can achieve better collaboration. The improved algorithm enhances the collaboration and increases the sum of reward values obtained by the multiagent system. To evaluate the performance of the algorithm, we use the MAJPPO algorithm to complete the task of multi-UAV formation and the crossing of multiple-obstacle environments. To simplify the control complexity of the UAV, we use the six-degree of freedom and 12-state equations of the dynamics model of the UAV with an attitude control loop. The experimental results show that the MAJPPO algorithm has better performance and better environmental adaptability.

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Rice, Caleb, Yu Gu, Haiyang Chao, Trenton Larrabee, Srikanth Gururajan, Marcello Napolitano, Tanmay Mandal, and Matthew Rhudy. "Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds." International Journal of Aerospace Engineering 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/9517654.

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Autonomous formation flight is a key approach for reducing energy cost and managing traffic in future high density airspace. The use of Unmanned Aerial Vehicles (UAVs) has allowed low-budget and low-risk validation of autonomous formation flight concepts. This paper discusses the implementation and flight testing of nonlinear dynamic inversion (NLDI) controllers for close formation flight (CFF) using two distinct UAV platforms: a set of fixed wing aircraft named “Phastball” and a set of quadrotors named “NEO.” Experimental results show that autonomous CFF with approximately 5-wingspan separation is achievable with a pair of low-cost unmanned Phastball research aircraft. Simulations of the quadrotor flight also validate the design of the NLDI controller for the NEO quadrotors.

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Ali, Zain Anwar, and Han Zhangang. "Multi-unmanned aerial vehicle swarm formation control using hybrid strategy." Transactions of the Institute of Measurement and Control 43, no. 12 (April 19, 2021): 2689–701. http://dx.doi.org/10.1177/01423312211003807.

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This study proposes a novel hybrid strategy for formation control of a swarm of multiple unmanned aerial vehicles (UAVs). To enhance the fitness function of the formation, this research offers a three-dimensional formation control for a swarm using particle swarm optimization (PSO) with Cauchy mutant (CM) operators. We use CM operators to enhance the PSO algorithm by examining the varying fitness levels of the local and global optimal solutions for UAV formation control. We establish the terrain and the fixed-wing UAV model. Furthermore, it also models different control parameters of the UAV as well. The enhanced hybrid algorithm not only quickens the convergence rate but also improves the solution optimality. Lastly, we carry out the simulations for the multi-UAV swarm under terrain and radar threats and the simulation results prove that the hybrid method is effective and gives better fitness function.

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Chen, Jintao, Wenlong Yang, Zongying Shi, and Yisheng Zhong. "Robust horizontal-plane formation control for small fixed-wing UAVs." Aerospace Science and Technology 131 (December 2022): 107958. http://dx.doi.org/10.1016/j.ast.2022.107958.

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Yu, Deyu, Pingfang Zhou, and Yuhao Jing. "Optimal obstacle avoidance consensus formation control method for fixed-wing UAV with variable topology." Aerospace Systems 5, no. 1 (January 20, 2022): 75–84. http://dx.doi.org/10.1007/s42401-021-00119-5.

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20

Zhang, Jialong, Jianguo Yan, Pu Zhang, and Xiangjie Kong. "Collision Avoidance in Fixed-Wing UAV Formation Flight Based on a Consensus Control Algorithm." IEEE Access 6 (2018): 43672–82. http://dx.doi.org/10.1109/access.2018.2864169.

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21

Kalra, Arti, Sreenatha Anavatti, and Radhakant Padhi. "Aggressive Formation Flying of Fixed-Wing UAVs with Differential Geometric Guidance." Unmanned Systems 05, no. 02 (April 2017): 97–113. http://dx.doi.org/10.1142/s2301385017500078.

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Анотація:

A nonlinear differential geometric guidance scheme is presented in this paper for aggressive autonomous formation flying of fixed-wing unmanned aerial vehicles (UAVs) in the leader-follower framework. It is assumed that the desired location of the followers are known in the velocity frame of the leader. It is also assumed that the followers can also access the position, velocity and acceleration parameters of the leader as necessary auxiliary information. By utilizing this information and manipulating their own dynamics, the proposed logic autonomously guides the followers to their respective desired positions. Depending on the leader’s velocity and acceleration information as well as the intended relative location, the formulation also ensures an appropriate direction of the velocity vectors of the followers at the desired relative locations, including its rate of change if any. This leads to minimal transient effects while maintaining the formation even under maneuvering conditions. Usage of quaternions and other innovations ensure that the formulation is singularity-free and hence formation flying is ensured even under aggressive maneuvers of the leader without any restriction on its velocity vector direction. The desired thrust, angle of attack and bank angle are generated using a nonlinear point mass model of a vehicle. The generated guidance commands are then realized using a nonlinear six-DOF model, making the formulation practically more relevant. Extensive simulation studies demonstrate that the proposed approach is capable of bringing the UAVs from arbitrary initial locations to the desired formation and then maintaining the formation even under highly agile motion of the leader.

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22

Gong, Zheng, Zan Zhou, Zian Wang, Quanhui Lv, Jinfa Xu, and Yunpeng Jiang. "Coordinated Formation Guidance Law for Fixed-Wing UAVs Based on Missile Parallel Approach Method." Aerospace 9, no. 5 (May 18, 2022): 272. http://dx.doi.org/10.3390/aerospace9050272.

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Анотація:

This paper presents a classic missile-type parallel-approach guidance law for fixed-wing UAVs in coordinated formation flight. The key idea of the proposed guidance law is to drive each follower to follow the virtual target point. Considering the turning ability of each follower, the formation form adopts the semi-perfect rigid form, which does not require the vehicle positions form a rigid formation, and the orientations keep consensus. According to the mission characteristics of the follower following a leader and the leader following a route, three guidance laws for straight, turning, and circling flight are designed. A series of experiments demonstrate the proposed guidance law’s improved response and maneuvering stability. The results of hardware-in-the-loop simulations and real flight tests prove that the proposed guidance law satisfies the practical UAV formation flight control demands.

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Muslimov, Tagir Z., and Rustem A. Munasypov. "Multi-UAV cooperative target tracking via consensus-based guidance vector fields and fuzzy MRAC." Aircraft Engineering and Aerospace Technology 93, no. 7 (August 7, 2021): 1204–12. http://dx.doi.org/10.1108/aeat-02-2021-0058.

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Purpose This paper aims to propose a multi-agent approach to adaptive control of fixed-wing unmanned aerial vehicles (UAVs) tracking a moving ground target. The approach implies that the UAVs in a single group must maintain preset phase shift angles while rotating around the target so as to evaluate the target’s movement more accurately. Thus, the controls should ensure that the UAV swarm follows a moving circular path whose center is the target while also attaining and maintaining a circular formation of a specific geometric shape; and the formation control system is capable of self-tuning because the UAV dynamics is uncertain. Design/methodology/approach This paper considers two interaction architectures: an open-chain where each UAV only interacts with its neighbors; and a cooperative leader, where the leading UAV is involved in attaining the formation. The cooperative controllers are self-tuned by fuzzy model reference adaptive control (MRAC). Findings Using open-chain decentralized architecture allows to have an unlimited number of aircraft in a formation, which is in line with the swarm behavior concept. The approach was tested for efficiency and performance in various scenarios using complete nonlinear flying-wing UAV models equipped with configured standard autopilot models. Research limitations/implications Assume the target follows a rectilinear trajectory at a constant speed. The speed is supposed to be known in advance. Another assumption is that the weather is windless. Originality/value In contrast to known studies, this one uses Lyapunov guidance vector fields that are direction- and magnitude-nonuniform. The overall cooperative controller structure is based on a decentralized and centralized consensus.

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Mirzaee Kahagh, A., F. Pazooki, and S. Etemadi Haghighi. "Obstacle avoidance in V-shape formation flight of multiple fixed-wing UAVs using variable repulsive circles." Aeronautical Journal 124, no. 1282 (October 23, 2020): 1979–2000. http://dx.doi.org/10.1017/aer.2020.81.

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ABSTRACTA formation control and obstacle avoidance algorithm has been introduced in this paper for the V-shape formation flight of fixed-wing UAVs (Unmanned Aerial Vehicles) using the potential functions method. An innovative vector approach has been suggested to fix the conventional challenge in employing the artificial potential field (APF) approach (the creation of local minimums). A method called variable repulsive circles (VRC) has been then presented aimed at designing proper flight paths tailored with functional limitations of fixed-wing UAVs in facing obstacles. Finally, the efficiency of the designed algorithm has been examined and evaluated for different flight scenarios.

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Zhang, Jialong, Jianguo Yan, and Pu Zhang. "Fixed-Wing UAV Formation Control Design With Collision Avoidance Based on an Improved Artificial Potential Field." IEEE Access 6 (2018): 78342–51. http://dx.doi.org/10.1109/access.2018.2885003.

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Zhao, Hongbo, Sentang Wu, Yongming Wen, Wenlei Liu, and Xiongjun Wu. "Modeling and Flight Experiments for Swarms of High Dynamic UAVs: A Stochastic Configuration Control System with Multiplicative Noises." Sensors 19, no. 15 (July 25, 2019): 3278. http://dx.doi.org/10.3390/s19153278.

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UAV Swarm with high dynamic configuration at a large scale requires a high-precision mathematical model to fully exploit its boundary performance. In order to instruct the engineering application with high confidence, uncertainties induced from either systematic measurement or the environment cannot be ignored. This paper investigates the I t o ^ stochastic model of the UAV Swarm system with multiplicative noises. By combining the cooperative kinematic model with a simplified individual dynamic model of fixed-wing-aircraft for the first time, the configuration control model is derived. Considering the uncertainties in actual flight, multiplicative noises are introduced to complete the I t o ^ stochastic model. Following that, the estimator and controller are designed to control the formation. The mean-square uniform boundedness condition of the proposed stochastic system is presented for the closed-loop system. In the simulation, the stochastic robustness analysis and design (SRAD) method is used to optimize the properties of the formation. More importantly, the effectiveness of the proposed model is also verified using real data of five unmanned aircrafts collected in outfield formation flight experiments.

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Wan, Yu, Jun Tang, and Songyang Lao. "Research on the Collision Avoidance Algorithm for Fixed-Wing UAVs Based on Maneuver Coordination and Planned Trajectories Prediction." Applied Sciences 9, no. 4 (February 25, 2019): 798. http://dx.doi.org/10.3390/app9040798.

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This paper presents a novel collision avoidance (CA) algorithm for a cooperative fixed-wing unmanned aerial vehicle (UAV). The method is based on maneuver coordination and planned trajectory prediction. Each aircraft in a conflict generates three available maneuvers and predicts the corresponding planned trajectories. The algorithm coordinates planned trajectories between participants in a conflict, determines which combination of planned trajectories provides the best separation, eventually makes an agreement on the maneuver for collision avoidance and activates the preferred maneuvers when a collision is imminent. The emphasis is placed on providing protection for UAVs, while activating maneuvers late enough to reduce interference, which is necessary for collision avoidance in the formation and clustering of UAVs. The CA has been validated with various simulations to show the advantage of collision avoidance for continuous conflicts in multiple, high-dynamic, high-density and three-dimensional (3D) environments. It eliminates the disadvantage of traditional CA, which has high uncertainty, and takes the performance parameters of different aircraft into consideration and makes full use of the maneuverability of fixed-wing aircraft.

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28

Yang, Jun, Ximan Wang, Simone Baldi, Satish Singh, and Stefano Fari. "A software-in-the-loop implementation of adaptive formation control for fixed-wing UAVs." IEEE/CAA Journal of Automatica Sinica 6, no. 5 (September 2019): 1230–39. http://dx.doi.org/10.1109/jas.2019.1911702.

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29

Yu, Ziquan, Youmin Zhang, Bin Jiang, Xiang Yu, Jun Fu, Ying Jin, and Tianyou Chai. "Distributed adaptive fault-tolerant close formation flight control of multiple trailing fixed-wing UAVs." ISA Transactions 106 (November 2020): 181–99. http://dx.doi.org/10.1016/j.isatra.2020.07.005.

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30

Ambroziak, Leszek, and Zdzisław Gosiewski. "Preliminary UAV Autopilot Integration and In-Flight Testing." Solid State Phenomena 198 (March 2013): 232–37. http://dx.doi.org/10.4028/www.scientific.net/ssp.198.232.

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The paper presents integration process of commercial autopilot. The autopilot was integrated with a fixed wing airframe. The main aim of this work was an experimental study of the autopilot integrated with a micro aircraft. A few manual and autonomous mode flights were performed. During the field trials the autopilot PID parameters were tuned and, as a result, the process of PID gains selection was described. Selected PID gains were presented. Certain telemetry parameters such as longitudinal and lateral position of aircraft, orientation angles, and angular velocities were logged during flight and analyzed. Maximum and minimum airspeeds at a desired altitude were measured and presented. Moreover, Received Signal Strange Indicator between ground station and UAV equipped with special antenna was measured and logged for radio communication quality and range checking. Presented analysis of the autopilots work and obtained results were used to assess the applicability of this hardware to next formation flight operations.

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31

Wu, Junfeng, Huan Wang, Shanshan Li, and Shuguang Liu. "Distributed Adaptive Path-Following Control for Distance-Based Formation of Fixed-Wing UAVs under Input Saturation." Aerospace 10, no. 9 (August 30, 2023): 768. http://dx.doi.org/10.3390/aerospace10090768.

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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.

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32

Kownacki, Cezary, and Leszek Ambroziak. "Local and asymmetrical potential field approach to leader tracking problem in rigid formations of fixed-wing UAVs." Aerospace Science and Technology 68 (September 2017): 465–74. http://dx.doi.org/10.1016/j.ast.2017.05.040.

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33

Ye, Fang, Jie Chen, Yuan Tian, and Tao Jiang. "Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm." Electronics 9, no. 4 (April 23, 2020): 687. http://dx.doi.org/10.3390/electronics9040687.

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The cooperative multiple task assignment problem (CMTAP) is an NP-hard combinatorial optimization problem. In this paper, CMTAP is to allocate multiple heterogeneous fixed-wing UAVs to perform a suppression of enemy air defense (SEAD) mission on multiple stationary ground targets. To solve this problem, we study the adaptive genetic algorithm (AGA) under the assumptions of the heterogeneity of UAVs and task coupling constraints. Firstly, the multi-type gene chromosome encoding scheme is designed to generate feasible chromosomes that satisfy the heterogeneity of UAVs and task coupling constraints. Then, AGA introduces the Dubins car model to simulate the UAV path formation and derives the fitness value of each chromosome. In order to comply with the chromosome coding strategy of multi-type genes, we designed the corresponding crossover and mutation operators to generate feasible offspring populations. Especially, the proposed mutation operators with the state-transition scheme enhance the stochastic searching ability of the proposed algorithm. Last but not least, the proposed AGA dynamically adjusts the number of crossover and mutation populations to avoid the subjective selection of simulation parameters. The numerical simulations verify that the proposed AGA has a better optimization ability and convergence effect compared with the random search method, genetic algorithm, ant colony optimization method, and particle search optimization method. Therefore, the effectiveness of the proposed algorithm is proven.

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34

Price, George A. J., Chris Moate, Daniel Andre, and Peter Yuen. "Sidelobe Suppression Techniques for Near-Field Multistatic SAR." Sensors 23, no. 2 (January 9, 2023): 732. http://dx.doi.org/10.3390/s23020732.

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Multirotor Unmanned Air Systems (UAS) represent a significant improvement in capability for Synthetic Aperture Radar (SAR) imaging when compared to traditional, fixed-wing, platforms. In particular, a swarm of UAS can generate significant measurement diversity through variation of spatial and frequency collections across an array of sensors. In such imaging schemes, the image formation step is challenging due to strong extended sidelobe; however, were this to be effectively managed, a dramatic increase in image quality is theoretically possible. Since 2015, QinetiQ have developed the RIBI system, which uses multiple UAS to perform short-range multistatic collections, and this requires novel near-field processing to mitigate the high sidelobes observed and form actionable imagery. This paper applies a number of algorithms to assess image reconstruction of simulated near-field multistatic SAR with an aim to suppress sidelobes observed in the RIBI system, investigating techniques including traditional SAR processing, regularised linear regression, compressive sensing. In these simulations presented, Elastic net, Orthogonal Matched Pursuit, and Iterative Hard Thresholding all show the ability to suppress sidelobes while preserving accuracy of scatterer RCS. This has also lead to a novel processing approach for reconstructing SAR images based on the observed Elastic net and Iterative Hard Thresholding performance, mitigating weaknesses to generate an improved combined approach. The relative strengths and weaknesses of the algorithms are discussed, as well as their application to more complex real-world imagery.

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35

Chen, Hao, Xiangke Wang, Lincheng Shen, Zhongkui Li, Zhihong Liu, and Yangguang Yu. "Formation Reconfiguration for Fixed-Wing UAVs." Journal of Intelligent & Robotic Systems 102, no. 1 (April 30, 2021). http://dx.doi.org/10.1007/s10846-021-01384-4.

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36

Fang, Yuxuan, Yiping Yao, Feng Zhu, and Kai Chen. "Piecewise-potential-field-based path planning method for fixed-wing UAV formation." Scientific Reports 13, no. 1 (February 8, 2023). http://dx.doi.org/10.1038/s41598-023-28087-0.

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AbstractThe 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.

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37

Lizzio, Fausto Francesco, Elisa Capello, and Giorgio Guglieri. "A Review of Consensus-based Multi-agent UAV Implementations." Journal of Intelligent & Robotic Systems 106, no. 2 (October 2022). http://dx.doi.org/10.1007/s10846-022-01743-9.

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AbstractIn this paper, a survey on distributed control applications for multi Unmanned Aerial Vehicles (UAVs) systems is proposed. The focus is on consensus-based control, and both rotary-wing and fixed-wing UAVs are considered. On one side, the latest experimental configurations for the implementation of formation flight are analysed and compared for multirotor UAVs. On the other hand, the control frameworks taking into account the mobility of the fixed-wing UAVs performing target tracking are considered. This approach can be helpful to assess and compare the solutions for practical applications of consensus in UAV swarms.

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38

Wang, Ximan, Simone Baldi, Xuewei Feng, Changwei Wu, Hongwei Xie, and Bart De Schutter. "A Fixed-Wing UAV Formation Algorithm Based on Vector Field Guidance." IEEE Transactions on Automation Science and Engineering, 2022, 1–14. http://dx.doi.org/10.1109/tase.2022.3144672.

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39

CHEN, Hao, Xiangke WANG, Lincheng SHEN, and Yirui CONG. "Formation flight of fixed-wing UAV swarms: A group-based hierarchical approach." Chinese Journal of Aeronautics, April 2020. http://dx.doi.org/10.1016/j.cja.2020.03.006.

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40

He, Mo, Xiaogang Wang, and Naigang Cui. "Modified vector field and nonlinear guidance law for low-cost UAV path following." Aircraft Engineering and Aerospace Technology, June 22, 2022. http://dx.doi.org/10.1108/aeat-03-2019-0045.

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Анотація:

Purpose The purpose of this paper is to present a high accuracy path following method for low-cost fixed-wing UAVs. Design/methodology/approach The original vector field (VF) algorithm is condensed. A spatial integration mechanism is added to the existing VF and nonlinear guidance law, aiming to decrease steady-state cross-track-error and cope with long-term disturbance. Findings Numerical simulations show the proposed method could diminish steady-state cross-track-error effectively. Test flights show the proposed method is applicable on low-cost fixed-wing UAVs. Practical implications The path following accuracy shown in simulations and test flights indicates the proposed method could be deployed in scenarios including inflight rendezvous, formation, trafficway take-off and landing. Originality/value This paper provides an improved high-accuracy path following method for low-cost fixed-wing UAVs.

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41

Li, Jiacheng, Yangwang Fang, Haoyu Cheng, Zhikai Wang, and Shuaiqi Huangfu. "Unmanned aerial vehicle formation obstacle avoidance control based on light transmission model and improved artificial potential field." Transactions of the Institute of Measurement and Control, June 28, 2022, 014233122211003. http://dx.doi.org/10.1177/01423312221100340.

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To overcome the limitations of the conventional artificial potential field (APF) method, which is commonly used for unmanned aerial vehicle (UAV) formation obstacle avoidance control. A novel UAV formation obstacle avoidance control method based on a light transmission model (LTM) and an improved APF method is proposed. First, inspired by the flight of bird flocks, we combine the LTM with an APF function to present an improved APF model which can help UAV find feasible free space to maneuver. From this, UAV can overcome the drawbacks of non-reachable and local minima under the action of LTM. Then, the obstacle avoidance strategy based on the fixed-wing UAV motion model is proposed, and the obstacle avoidance control algorithm for UAV formation is designed. Finally, simulation results show the effectiveness and superiority of the proposed method, which can result in a dramatic improvement in the performance of UAV formation to obstacle avoidance under the complex and non-deterministic environment.

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42

Mirzaee Kahagh, A., F. Pazooki, S. Etemadi Haghighi, and D. Asadi. "Real-time formation control and obstacle avoidance algorithm for fixed-wing UAVs." Aeronautical Journal, February 23, 2022, 1–23. http://dx.doi.org/10.1017/aer.2022.9.

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Abstract This paper proposes a novel real-time formation control and obstacle avoidance algorithm for multiple fixed-wing UAVs. A formation control algorithm is designed by a combination of the virtual structure, leader-follower, and artificial potential fields methods and harnessing the advantages of those approaches. The kinematic and dynamic constraints of fixed-wing UAVs are considered in the path planning. The performance of the proposed algorithm is examined through simulation in Matlab software by applying the translational dynamics of fixed-wing UAVs. Simulations of different complex scenarios demonstrate the effectiveness of the presented formation flight algorithm through generating multiple efficient paths, which are fully consistent with the functional constraints of the UAVs in the presence of obstacles.

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43

Kim, Suhyeon, Hyeongjun Cho, and Dongwon Jung. "Circular Formation Guidance of Fixed-wing UAVs using Mesh Network." IEEE Access, 2022, 1. http://dx.doi.org/10.1109/access.2022.3218673.

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44

Li, Jiacheng, Yangwang Fang, Haoyun Cheng, Zhikai Wang, Zihao Wu, and Mengjie Zeng. "Large-Scale Fixed-Wing UAV Swarm System Control With Collision Avoidance and Formation Maneuver." IEEE Systems Journal, 2022, 1–12. http://dx.doi.org/10.1109/jsyst.2022.3212068.

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45

Zhang, Jialong, and Jianguo Yan. "A Novel Control Approach for Flight-Stability of Fixed-Wing UAV Formation With Wind Field." IEEE Systems Journal, 2020, 1–11. http://dx.doi.org/10.1109/jsyst.2020.3002809.

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46

Wang, Qipeng, Shulong Zhao, Yangguang Yu, and Xiangke Wang. "Distributed control for coordinated tracking of fixed-wing unmanned aerial vehicles subject to velocity constraints." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, July 19, 2022, 095441002110636. http://dx.doi.org/10.1177/09544100211063669.

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This paper considers a coordinated tracking problem of fixed-wing unmanned aerial vehicles (UAVs), whose kinematics is described as unicycle type with the constraints of both saturated angular speed and bounded linear velocity. A distributed velocity controller based on the consensus theory is designed such that UAVs can converge to the predefined formation and track the target vehicle, while the velocity constraints are satisfied. Moreover, due to the limitation of measurement range of sensors and communication bandwidth of UAV, an observer is designed when the information of neighbors’ velocity cannot be obtained. The stability of the closed-loop system is proved by using a technique lemma, which is developed based on the cascaded system. Finally, the results of numerical simulation compared with another algorithm show the effectiveness of the proposed algorithm, and a hardware-in-loop (HIL) simulation is carried out to prove the practical of the algorithm.

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47

Zhi, Yongran, Lei Liu, Bin Guan, Bo Wang, Zhongtao Cheng, and Huijin Fan. "Distributed robust adaptive formation control of fixed-wing UAVs with unknown uncertainties and disturbances." Aerospace Science and Technology, May 2022, 107600. http://dx.doi.org/10.1016/j.ast.2022.107600.

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48

Zhang, Yuwei, Shaoshi Li, Shaoping Wang, Xingjian Wang, and Haibin Duan. "Distributed bearing-based formation maneuver control of fixed-wing UAVs by finite-time orientation estimation." Aerospace Science and Technology, March 2023, 108241. http://dx.doi.org/10.1016/j.ast.2023.108241.

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49

Liu, Xuzan, Yu Han, and Jian Chen. "Discrete pigeon-inspired optimization-simulated annealing algorithm and optimal reciprocal collision avoidance scheme for fixed-wing UAV formation assembly." Unmanned Systems, December 31, 2020. http://dx.doi.org/10.1142/s230138502141003x.

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50

Yu, Ziquan, Youmin Zhang, Bin Jiang, and Xiang Yu. "Fault-Tolerant Time-Varying Elliptical Formation Control of Multiple Fixed-Wing UAVs for Cooperative Forest Fire Monitoring." Journal of Intelligent & Robotic Systems 101, no. 3 (February 18, 2021). http://dx.doi.org/10.1007/s10846-021-01320-6.

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