Energy-Efficient 3-D UAV Ground Node Accessing Using the Minimum Number of UAVs

被引:0
|
作者
Gong, Hao [1 ]
Huang, Baoqi [1 ]
Jia, Bing [1 ]
机构
[1] Inner Mongolia Univ, Minist Educ, Engn Res Ctr Ecol Big Data, Coll Comp Sci,Inner Mongolia Key Lab Wireless Netw, Hohhot 010021, Peoples R China
基金
中国国家自然科学基金;
关键词
Autonomous aerial vehicles; Energy consumption; Optimization; Minimization; Trajectory; Task analysis; Energy efficiency; Multiple UAVs; energy consumption; number of UAVs; GN-accessing; MPSO; GPA; TRAJECTORY OPTIMIZATION; DATA-COLLECTION; MINIMIZATION;
D O I
10.1109/TMC.2024.3405494
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cooperative multiple unmanned aerial vehicles (UAVs) have been widely exploited in various applications, including data collection, forest monitoring, edge computing, and so on. Due to limited onboard storage and expensive hardware costs, reducing both the energy consumption and the number of UAVs is critical for these multi-UAV applications. However, existing studies primarily revolved around energy minimization in two-dimensional (2-D) scenarios, given a sufficient but fixed number of UAVs, and most of them considered specific application scenarios, resulting in poor generality. In contrast, this paper defines a generalized application scenario, in which multiple ground nodes (GNs) are accessed by multiple UAVs in three-dimensional (3-D) scenarios, and aims to minimize the energy consumption by employing the necessary (or equivalently minimum) number of UAVs and formulating a mix-integer nonconvex problem. To this end, this paper decomposes the problem into two subproblems: energy consumption minimization for a single UAV consecutively accessing any two GNs and energy-efficient multi-UAV GN-accessing path planning employing the minimum number of UAVs. The first subproblem is solved by applying the successive convex approximation (SCA) technique and the path discretization method, while the second subproblem is addressed by designing a three-stage approximation framework based on modified particle swarm optimization (MPSO) and greedy path assignment (GPA). Comprehensive simulations demonstrate the superior performance of the proposed method in terms of optimality and efficiency compared to several other counterparts.
引用
收藏
页码:12046 / 12060
页数:15
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