Decentralized Hybrid Flocking Guidance for a Swarm of Small UAVs

Lim, Seunghan; Song, Yeongho; Choi, Joonwon; Myung, Hyunsam; Lim, Heungsik; Oh, Hyondong

doi:10.1109/reduas47371.2019.8999710

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…UAV communication channels and data exchange protocols are not considered in the present paper, and it is assumed that the information exchange necessary to implement the proposed algorithms is feasible in one form or another. Results regarding data transfer between UAVs in formation can be found in [23,37,[59][60][61][62][63][64][65][66][67][68][69][70][71][72]. Particularly, in [59,63,66,67], adaptive coding procedures for navigation data exchanged between the UAVs in formation was studied in depth.…”

Section: Problem Descriptionmentioning

confidence: 99%

Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation

et al. 2022

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The paper is devoted to the problem of the decentralized control of unmanned aerial vehicle (UAV) formation in the case of parametric uncertainty. A new version of the feedback linearization approach is proposed and used for a point mass UAV model transformation. As result, a linear model is obtained containing an unknown value of the UAV mass. Employing the speed-gradient design method and the implicit reference model concept, a combined adaptive control law is proposed for a single UAV, including the UAV’s mass estimation and adaptive tuning of the controller parameters. The obtained new algorithms are then used to address the problem of consensus-based decentralized control of the UAV formation. Rigorous stability conditions for control and identification are derived, and simulation results are presented to demonstrate the quality of the closed-loop control system for various conditions.

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Section: Problem Descriptionmentioning

confidence: 99%

Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation

et al. 2022

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“…13. Because of J time term in (7), the impact point optimization can reduce the arrival time without significant loss of the coverage ratio (CR). This can be verified from Fig.…”

Section: Population Size 200mentioning

confidence: 99%

“…For instance, the cooperative hazardous source searching method was studied using multiple mobile agents in [1] while an attack strategy of multiple UAVs against an enemy ship was investigated in [2]. The recent achievement on the MAS also can be found in the formation control [3][4][5][6], flocking [7][8][9], cooperative tactics and planning [10][11][12][13][14], or standoff target tracking [15].…”

Section: Introductionmentioning

confidence: 99%

Adversarial Swarm Defence Using Multiple Fixed-Wing Unmanned Aerial Vehicles

Choi¹,

Seo

Shin

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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This paper proposes a novel coverage-based adversarial swarm defence algorithm. The defender swarm composed of fixed-wing unmanned aerial vehicles (UAVs) is assumed to have explosives onboard to intercept an adversarial swarm. The proposed approach consists of two steps: i) impact point optimization and ii) model predictive control (MPC)-based impact time control. The impact point optimization periodically optimizes impact points for the corresponding UAVs to maximize the coverage within the hostile swarm while minimizing the common impact time. The optimization domain is limited to a physically reachable area of UAVs with the common impact time. Besides, the MPC-based impact time controller is designed to ensure the multiple UAVs to arrive the generated time-varying impact points simultaneously. Numerical simulations are performed to prove the feasibility and efficiency of the proposed swarm defence algorithm.

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“…We extend the above inactiveness concept to the flocking task of fixed-wing UAVs. To find the best inactiveness level for each flock member, social learning particle swarm optimization (SL-PSO) [8] is applied as the first step by using the initial condition of the flock and building upon our previous work [29,18].…”

Section: Introductionmentioning

confidence: 99%

“…It is worthwhile noting that the collision avoidance among agents cannot be explicitly guaranteed by using the above flocking model in the transient period before converging to the final flock state depending on the initial configuration or communication topology. Although there are some recent approaches on collision avoidance for flocking using the potential field-based reactive control [18], distributed model predictive control [33] or reinforcement learning [35],…”

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confidence: 99%

Using Lazy Agents to Improve the Flocking Efficiency of Multiple UAVs

Song

Choi

et al. 2021

J Intell Robot Syst

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A group of agents can form a flock using the augmented Cucker-Smale (C-S) model. The model autonomously aligns them to a common velocity and maintains a relative distance among the agents in a distributed manner by sharing the information among neighbors. This paper introduces the concept of inactiveness to the augmented C-S model for improving the flocking performance. It involves controlling the energy and convergence time required to form a stable flock. Inspired by the natural world where a few lazy (or inactive) workers are helpful to the group performance in social insect colonies. In this study, we analyzed different levels of inactiveness as a degree of control input effectiveness for multiple fixed-wing UAVs in the flocking algorithm. To find the appropriate inactiveness level for each flock member, the particle swarm optimization-based approach is used as the first step, based on the initial condition of the flock.However, as the significant computational burden may cause difficulties in implementing the optimization-based approach in real time, we also propose a

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Decentralized Hybrid Flocking Guidance for a Swarm of Small UAVs

Cited by 5 publications

References 12 publications

Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation

Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation

Adversarial Swarm Defence Using Multiple Fixed-Wing Unmanned Aerial Vehicles

Using Lazy Agents to Improve the Flocking Efficiency of Multiple UAVs

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