Adaptive Multiple Task Assignments for UAVs Using Discrete Particle Swarm Optimization

被引：12
|
作者：
Chen, Kun [1 ]
Sun, Qibo [1 ]
Zhou, Ao [1 ]
Wang, Shangguang [1 ]
机构：
[1] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing, Peoples R China
来源：
INTERNET OF VEHICLES: TECHNOLOGIES AND SERVICES TOWARDS SMART CITY (IOV 2018) | 2018年 / 11253卷
基金：
北京市自然科学基金;
关键词：
UAV; Forest firefighting; Task assignment; Particle swarm optimization; AERIAL VEHICLES;
D O I：
10.1007/978-3-030-05081-8_16
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The forest fire is an extremely dangerous natural disaster. The traditional fire-fighting equipment have great difficulty in performing firefighting in mountain terrain. Unmanned aerial vehicles (UAVs) are coming into a popular form in forest firefighting. In view of the suddenness of forest fires, the adaptive and dynamic firefighting task assignment for UAV is of great significance, and the current firefighting task assignment cannot address this issue. This paper proposed an adaptive and dynamic multiple task assignment method for UAVs. Firstly, the adaptive and dynamic firefighting task assignment is formulated as an optimization problem. Secondly, an assignment algorithm is proposed to solve the problem by extending the particle swarm optimization (PSO) algorithm. Finally, the experiment results verify the effectiveness of the proposed algorithm.
引用
收藏
页码：220 / 229
页数：10
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