- Book学术

发布求助

文献互助智能选刊最新文献

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-27 DOI:10.1109/TNSE.2025.3534623

Kai Ma;Hanying Zhao;Jian Wang;Yu Wang;Yuan Shen

引用次数: 0

摘要

高精度定位和编队对多无人机网络执行合作任务至关重要。然而，由于复杂的信息耦合效应，定位和运动规划的联合设计具有挑战性，这会导致编队精度的损失。在本文中，我们为多无人机网络建立了一种集成定位和运动规划方案。首先，我们推导了相对编队误差的边界，揭示了测量和运动噪声对编队精度的影响。然后，我们提出了一个双向过程框架来提高编队精度。前向过程提出了一种近乎最优的运动规划算法，该算法利用相对编队的等价关系来减轻定位不确定性的影响。后向过程涉及带宽分配和无人机激活，以最大限度地提高编队精度。数值结果验证了所提综合方案在编队精度方面的收益。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A Multi-UAV Network Formation Scheme via Integrated Localization and Motion Planning

High-accuracy localization and formation are essential for multi-UAV networks to perform cooperative tasks. However, the joint design of localization and motion planning is challenging due to complex information coupling effects, which leads to a loss of formation accuracy. In this paper, we establish an integrated localization and motion planning scheme for multi-UAV networks. First, we derive bounds for the relative formation error, which reveals how measurement and motion noises affect the formation accuracy. Then, we propose a bidirectional process framework to enhance the formation accuracy. The forward process presents a near-optimal motion planning algorithm that leverages the equivalence relation of relative formations to mitigate the impact of localization uncertainties. The backward process addresses bandwidth allocation and UAV activation to maximize formation accuracy. Numerical results verify the gains of the proposed integrated scheme in formation accuracy.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.