A novel cooperative searching architecture for multi-unmanned aerial vehicles under restricted communication

被引:5
作者
Ran, Huanhuan [1 ]
Sun, Liang [2 ]
Cheng, Shu [3 ]
Ma, Yunfeng [3 ]
Yan, Shan [4 ]
Meng, Shunkai [5 ]
Shi, Kaibo [6 ]
Wen, Shiping [7 ]
机构
[1] Univ Elect Sci & Technol China, Sch Elect Sci & Engn, Natl Exemplary Sch Microelect, Chengdu, Peoples R China
[2] Nankai Univ, Chern Inst Math, Tianjin, Peoples R China
[3] TengDen Technol Co Ltd, Chengdu, Peoples R China
[4] Second Res Inst Civil Aviat Adm China CAAC, Chengdu, Peoples R China
[5] Chengdu Creat Technol Co Ltd, Chengdu, Peoples R China
[6] Chengdu Univ, Sch Informat Sci & Engn, Chengdu, Peoples R China
[7] Univ Technol Sydney, Australian Artificial Intelligence Inst, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia
基金
中国国家自然科学基金;
关键词
Bayesian formulation; cooperative searching architecture; multiple unmanned aerial vehicles (UAVs); physical search; MEMRISTIVE NEURAL-NETWORKS; PROBABILISTIC FRAMEWORK; SYNCHRONIZATION; OPTIMIZATION; UAVS;
D O I
10.1002/asjc.2517
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Searching for a distinguishable lost target in a bounded area automatically with unmanned aerial vehicle (UAV) is a fundamental problem in the theory of physical search. This paper studies the problem in detail, presents a new and more realistic Bayesian formula representing the problem by taking communication capability of UAVs into consideration, and focuses on the case of applying a team of multiple cooperative UAVs in the field for the search, where each UAV can autonomously search the area for the target using Bayesian filtering algorithm. The perturbation of the searching performance by the different prior belief probability distribution functions is investigated. Empirical evidence findings illustrate that our approach yields improved accuracy.
引用
收藏
页码:510 / 516
页数:7
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