A fast formation obstacle avoidance algorithm for clustered UAVs based on artificial potential field

The aim of this paper is to improve the rapid obstacle avoidance control of UAVs cluster in a complex obstacle environment, primarily utilizing the finite-time consistent formation control algorithm and the improved artificial potential field algorithm to design the fast obstacle avoidance control s...

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Vydané v:Aerospace science and technology Ročník 147; s. 108974
Hlavní autori: Liu, Yunping, Chen, Cheng, Wang, Yan, Zhang, Tingting, Gong, Yiguang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Masson SAS 01.04.2024
Predmet:
Artificial potential field
Auxiliary potential field
Finite time consistency
Formation
Auxiliary potential field
Finite time consistency
Artificial potential field
Formation
ISSN:1270-9638, 1626-3219
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Popis
Shrnutí:The aim of this paper is to improve the rapid obstacle avoidance control of UAVs cluster in a complex obstacle environment, primarily utilizing the finite-time consistent formation control algorithm and the improved artificial potential field algorithm to design the fast obstacle avoidance control strategy. Firstly, a finite-time consistent formation control algorithm is adopted to address the problems of slow formation speed and low control accuracy of UAVs clusters for establishing the formation model and control of UAVs cluster. Then, taking static and dynamic obstacles as obstacle avoidance targets, the improved artificial potential field algorithm is utilized, and the auxiliary potential field and dynamic situation field range of obstacle velocity are also introduced. The algorithm enhances obstacle avoidance speed and efficiency from the two aspects: time optimization and space optimization. Meanwhile, dynamic perturbation is introduced to address the local minimum problem of traditional artificial potential field. Finally, the effectiveness of the algorithm is confirmed through simulation on a verification platform and testing on a physical prototype verification platform.
ISSN:1270-9638
1626-3219
DOI:10.1016/j.ast.2024.108974