Feedback formation control of UAV swarm with multiple implicit leaders

He, Lei; Bai, Peng; Liang, Xiaolong; Zhang, Jiaqiang; Wang, Weijia

doi:10.1016/j.ast.2017.11.020

Cited by 121 publications

(49 citation statements)

References 21 publications

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“…In this paper, the grouping and flight direction of drones is determined by a linear equation based on the Lyapunov-based method. The method proposed in this paper does not require the selection of leaders around to determine the direction of flight [32]. When the trajectory is low (x<0.15), the performance of all the schemes has similar trends.…”

Section: Numerical Results and Analysismentioning

confidence: 99%

“…Some relevant network parameters are listed in Table 1. Recently, the schemes in [32] have been published and introduced unique challenges for UAV swarm formation control protocols. As mentioned earlier, we compare the performance of the proposed scheme with these existing schemes to confirm the superiority of the proposed approach Fig.…”

Section: Numerical Results and Analysismentioning

confidence: 99%

“…4 shows the performance comparison for trajectories of UAVs, note that the axes scale of X and Y are different. We compared it with the proposed method using the path measurement and error rate proposed by [32]. In this paper, the grouping and flight direction of drones is determined by a linear equation based on the Lyapunov-based method.…”

Section: Numerical Results and Analysismentioning

confidence: 99%

“…The mathematical algorithms for formulating the flocking problem and some preliminary concepts are introduced in Section 3. In Section 4, The numerical experiments are performed and performance evaluation results are presented along with comparisons with the multiple implicit leaders aided schemes proposed in [32]. Finally, in section 5, we conclude our paper and indicate future work to be done.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical modeling for flocking flight of autonomous multi-UAV system, including environmental factors

2020

KSII TIIS

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In this study, we propose a decentralized mathematical model for predictive control of a system of multi-autonomous unmanned aerial vehicles (UAVs), also known as drones. Being decentralized and autonomous implies that all members make their own decisions and fly depending on the dynamic information received from other unmanned aircraft in the area. We consider a variety of realistic characteristics, including time delay and communication locality. For this flocking flight, we do not possess control for central data processing or control over each UAV, as each UAV runs its collision avoidance algorithm by itself. The main contribution of this work is a mathematical model for stable group flight even in adverse weather conditions (e.g., heavy wind, rain, etc.) by adding Gaussian noise. Two of our proposed variance control algorithms are presented in this work. One is based on a simple biological imitation from statistical physical modeling, which mimics animal group behavior; the other is an algorithm for cooperatively tracking an object, which aligns the velocities of neighboring agents corresponding to each other. We demonstrate the stability of the control algorithm and its applicability in autonomous multi-drone systems using numerical simulations.

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Section: Numerical Results and Analysismentioning

confidence: 99%

Section: Numerical Results and Analysismentioning

confidence: 99%

Section: Numerical Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mathematical modeling for flocking flight of autonomous multi-UAV system, including environmental factors

2020

KSII TIIS

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“…There were many studies related to the coordination between drones in L-F formation. For instance, the implementation of various communication topologies to facilitate the distributed formation control [7,8], the consensus of motion using the feedback formation control [6] and the model predictive control [3].…”

Section: Leader-follower Formationmentioning

confidence: 99%

Leader–follower formation control of two quadrotor UAVs

et al. 2019

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This paper reports the design and implementation of a leader-follower (L-F) formation control of two Parrot AR Drone 2.0 quadrotor UAVs (drones). In order to implement such control, three control elements were developed. These were the flight-dynamic characteristics, the position tracking and L-F formation control, and the data communication system between drones and ground-control station. The dynamic characteristics were identified as the first-order process with time delay models, which were asymmetrical to x − , x + , x − and x + directions. Accordingly, dedicated Proportional-Derivative (PD) controllers were designed for the respective models. A Pole-Placement strategy was applied to design the PD controllers in order to achieve a non-oscillatory position tracking. These controllers were combined with five operating modes to form an F-L formation control. The controls were implemented on two-drones and one-ground-control station. The ground-control station dictated a prescribed path for the leader drone. Subsequently, the leader position became follower set-point. During the implementation test, these drones completed their L-F formation. The follower drone tracked the y-axis coordinate of the leader's drone and maintained a safe distance to the respective x-axis coordinate. The accuracy of the controls was measured in terms of the root mean squares errors, and these were within 50-115 cm.

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

Lu,

2024

Concurrency and Computation

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

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Feedback formation control of UAV swarm with multiple implicit leaders

Cited by 121 publications

References 21 publications

Mathematical modeling for flocking flight of autonomous multi-UAV system, including environmental factors

Mathematical modeling for flocking flight of autonomous multi-UAV system, including environmental factors

Leader–follower formation control of two quadrotor UAVs

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

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