基于矩阵差分进化的移动网络视觉覆盖无人机轨迹优化

IF 7.6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Riheng Jia , Hengchao Li , Peifa Sun , Zhonglong Zheng , Minglu Li
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引用次数: 0

摘要

在这项工作中,我们为无人机(UAV)开发了一种新的轨迹优化方法,以实现对地面移动节点的有效视觉覆盖。与大多数现有研究不同的是,我们考虑了节点的移动性,生成了一个连续平滑的无人机轨迹,更适合实际场景。我们的目标是通过适当设计无人机的三维飞行轨迹,在无人机的任务中最大化视觉覆盖节点的总数。为了处理无限解空间,我们首先利用bsamzier曲线方法将连续轨迹优化问题转化为离散控制点选择问题,在保持轨迹平滑的同时降低了计算复杂度。然后,采用基于矩阵的差分进化(MDE)框架,开发了一种新的无人机轨迹优化算法,该算法可以在降低计算复杂度的同时最大化视觉覆盖节点的数量。与现有方法相比,大量的仿真结果验证了该方法的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UAV trajectory optimization for visual coverage in mobile networks using matrix-based differential evolution
In this work, we develop a novel trajectory optimization approach for an unmanned aerial vehicle (UAV) to achieve efficient visual coverage of terrestrial mobile nodes. Unlike most existing studies, we consider the node mobility and generate a continuous and smooth UAV trajectory, which more suits the practical scenario. We aim to maximize the total number of visually covered nodes during the UAV’s mission, by appropriately designing the UAV’s three-dimensional (3D) flight trajectory. To handle the infinite solution space, we first leverage the Bézier curve method to transform the continuous trajectory optimization problem into a discrete control point selection problem, reducing the computational complexity while preserving the trajectory smoothness. Then, we develop a novel UAV trajectory optimization algorithm by employing the matrix-based differential evolution (MDE) framework, which can maximize the number of visually covered nodes with the reduced computational complexity. Extensive simulation results validate the effectiveness and superiority of our approach, compared with existing arts.
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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