Collision-Free Maneuvering for a UAV Swarm Based on Parallel Control

IF 15.3 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Jiacheng Li;Wenhui Ma;YangWang Fang;Dengxiu Yu;C. L. Philip Chen
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Abstract

The maneuvering of a large-scale unmanned aerial vehicle (UAV) swarm, notable for flexible flight with collision-free, is still challenging due to the significant number of UAVs and the compact configuration of the swarm. In light of this problem, a novel parallel control method that utilizes space and time transformation is proposed. First, the swarm is decomposed based on a grouping-hierarchical strategy, while the distinct flight roles are assigned to each UAV. Then, to achieve the desired configuration (DCF) in the real world, a bijection transformation is conducted in the space domain, converting an arbitrarily general configuration (GCF) into a standard configuration (SCF) in the virtual space. Further, to improve the flexibility of the swarm, the time scaling transformation is adopted in the time domain, which ensures the desired prescribed-time convergence of the swarm independent of initial conditions. Finally, simulation results demonstrate that collision-free maneuvering, including formation changes and turning, can be effectively and rapidly achieved by the proposed parallel control method. Overall, this research contributes a viable solution for enhancing cooperation among large-scale UAV swarms.
基于并行控制的无人机群无碰撞机动
大型无人机(UAV)群以无碰撞灵活飞行著称,但由于无人机数量众多且群结构紧凑,其机动仍然具有挑战性。针对这一问题,提出了一种利用时空变换的并联控制方法。首先,基于分组分层策略对蜂群进行分解,并为每架无人机分配不同的飞行角色;然后,在空间域中进行双射变换,将任意通用配置(GCF)转换为虚拟空间中的标准配置(SCF),以实现现实世界中的期望配置(DCF)。进一步,为了提高群的灵活性,在时域上采用了时间尺度变换,保证了群在不受初始条件影响的情况下达到预定时间的收敛性。仿真结果表明,所提出的并联控制方法能够有效、快速地实现编队变换和转向等无碰撞机动。总体而言,本研究为大规模无人机群间的协作提供了可行的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Ieee-Caa Journal of Automatica Sinica
Ieee-Caa Journal of Automatica Sinica Engineering-Control and Systems Engineering
CiteScore
23.50
自引率
11.00%
发文量
880
期刊介绍: The IEEE/CAA Journal of Automatica Sinica is a reputable journal that publishes high-quality papers in English on original theoretical/experimental research and development in the field of automation. The journal covers a wide range of topics including automatic control, artificial intelligence and intelligent control, systems theory and engineering, pattern recognition and intelligent systems, automation engineering and applications, information processing and information systems, network-based automation, robotics, sensing and measurement, and navigation, guidance, and control. Additionally, the journal is abstracted/indexed in several prominent databases including SCIE (Science Citation Index Expanded), EI (Engineering Index), Inspec, Scopus, SCImago, DBLP, CNKI (China National Knowledge Infrastructure), CSCD (Chinese Science Citation Database), and IEEE Xplore.
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