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Academic literature on the topic 'Fixed-Wing UAV Formations'
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Journal articles on the topic "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 (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.
2
Zhan, Guang, Zheng Gong, Quanhui Lv, et al. "Flight Test of Autonomous Formation Management for Multiple Fixed-Wing UAVs Based on Missile Parallel Method." Drones 6, no. 5 (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 (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.
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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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 (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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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 (2020): 977–89. http://dx.doi.org/10.1109/tcns.2019.2929658.
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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 (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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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 (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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Muslimov, T. Z., and R. A. Munasypov. "Decentralized Nonlinear Group Control of Fixed-Wing UAV Formation." Mekhatronika, Avtomatizatsiya, Upravlenie 21, no. 1 (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.
10
Xu, Dan, Yunxiao Guo, Zhongyi Yu, et al. "PPO-Exp: Keeping Fixed-Wing UAV Formation with Deep Reinforcement Learning." Drones 7, no. 1 (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.