Autonomous Cooperative Flocking for Heterogeneous Unmanned Aerial Vehicle Group

Wu, Jiehong; Yu, Yuanzhe; Ma, Jian; Wu, Jinsong; Han, Guangjie; Shi, Junling; Gao, Lijun

doi:10.1109/tvt.2021.3124898

Cited by 34 publications

(7 citation statements)

References 41 publications

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“…We consider that there are N agents in two-dimensional Euclidean space. In the general flocking control models [21][22][23], agents always update their states by integer-order dynamics, that is…”

Section: Dynamics Model Of Multi-agent Systemsmentioning

confidence: 99%

A Discrete-Time Fractional-Order Flocking Control Algorithm of Multi-Agent Systems

Chen,

He,

Han

et al. 2024

Fractal Fract

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In this paper, a discrete-time fractional flocking control algorithm of multi-agent systems is put forward to address the slow convergence issue of multi-agent systems. Firstly, by introducing Grünwald-Letnikov (G-L) fractional derivatives, the algorithm allows agents to utilize historical information when updating their states. Secondly, based on the Lyapunov stability theory, the convergence of the algorithm is proven. Finally, simulations are conducted to verify the effectiveness of the proposed algorithm. Comparisons are made between the proposed algorithm and other methods. The results show that the proposed algorithm can effectively improve the convergence speed of multi-agent systems.

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Section: Dynamics Model Of Multi-agent Systemsmentioning

confidence: 99%

A Discrete-Time Fractional-Order Flocking Control Algorithm of Multi-Agent Systems

Chen,

He,

Han

et al. 2024

Fractal Fract

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show abstract

“…There will be some unnecessary turns in the originally decided path as UAV move to the goal.Therefore, we use a way smoothing algorithm 'UAVPathSmoothing' to simplify 3-D Dubins paths [8]. The smooth ways made though this process enhance the tracking characteristics of fixedwing simulation models.…”

Section: Motion Planning For Fixed-wing Uavsmentioning

confidence: 99%

Motion Planning of Unmanned Aerial Vehicle Based on Rapid-exploration Random Tree Algorithm

Cai

Liu

Yuan

et al. 2022

J. Phys.: Conf. Ser.

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In the study of route planning problems in complex environments, in order to reduce the flight cost of unmanned aerial vehicles (UAVs), it is necessary to achieve a better balance between planning time and path quality. This paper utilizes the Rapid-exploration Random Tree (RRT) algorithm for motion planning of a fixed-wing UAV and a multi-rotor UAV (i.e., a quad-rotor UAV), and gives the origin and destination locations on a 3-D map. By following aerodynamic constraints such as maximum roll angle, flight path angle, and airspeed, a collision-free and flight-friendly path is found through simulation. In addition, this paper employs a path smoothing algorithm to simplify the 3-D Dubins path and generates the shortest trajectory. The simulation results show that the RRT and the optimization method have faster convergence speed and shorter search time, reduce redundant planning points, shorten the planning track, and improve the track planning efficiency.

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“…The basis of a UAV swarm is to control all the individual UAVs during the planned path. To solve the reconstruction, anti-collision, search, and tracking issues in the swarm formations the development of proper control system frameworks and controllers is required [28]. Centralized and distributed are the two major control platforms for the automation-equipped clusters.…”

Section: Swarm Challenges 41 Swarm Controlmentioning

confidence: 99%

Motion Planning of UAV Swarm: Recent Challenges and Approaches

Iqbal¹,

Ali²,

Khan³

et al. 2022

Aeronautics - New Advances

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The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work.

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Autonomous Cooperative Flocking for Heterogeneous Unmanned Aerial Vehicle Group

Cited by 34 publications

References 41 publications

A Discrete-Time Fractional-Order Flocking Control Algorithm of Multi-Agent Systems

A Discrete-Time Fractional-Order Flocking Control Algorithm of Multi-Agent Systems

Motion Planning of Unmanned Aerial Vehicle Based on Rapid-exploration Random Tree Algorithm

Motion Planning of UAV Swarm: Recent Challenges and Approaches

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