A large-scale clustering and 3D trajectory optimization approach for UAV swarms  

作　　者：Ting MA Haibo ZHOU Bo QIAN Aiyong FU 

机构地区：[1]School of Electronic Science and Engineering,Nanjing University,Nanjing 210023,China

出　　处：《Science China(Information Sciences)》2021年第4期80-95,共16页中国科学（信息科学）（英文版）

基　　金：supported in part by National Natural Science Foundation of China(Grant No.61871211);Natural Science Foundation of Jiangsu Province Youth Project(Grant No.BK20180329);Innovation and Entrepreneurship of Jiangsu Province High-level Talent Program,Summit of the Six Top Talents Program of Jiangsu Province。

摘　　要：With the significant development of unmanned aerial vehicles(UAVs)technologies,a rapid increase on the use of UAV swarms in a wide range of civilian and emergency applications has been witnessed.However,how to efficiently network the large-scale UAVs and implement the swarms applications without infrastructure support in remote areas is challenging.In this paper,we investigate a hierarchal large-scale infrastructure-less UAV swarm scenario,where numerous UAVs surveil and collect data from the ground and a ferry UAV(Ferry UAV)is designated to carry back all their collected data.We can divide UAV swarms into different areas based on their geographic locations due to the wide range of surveillance.To improve data collection efficiency of Ferry UAV,we introduce a single super cluster head(Super-CH)UAV in each area which can be selected by the proposed modified k-means clustering algorithm with low latency.Then,we design an iterative approach to optimize the 3-dimensional(3D)trajectory of Ferry UAV such that its data collection mission completion time is minimized.Numerical results show the efficiency and low-latency of the proposed clustering algorithm,and the proposed 3D optimal trajectory design for large-scale UAV swarms data collection admits better performance than that with fixed altitude.

关 键 词：large-scale UAV swarms CLUSTERING super-CH selection 3D trajectory design 

分 类 号：V279[航空宇航科学与技术—飞行器设计] V249