Multi-UAV Regional Patrol Mission Planning Strategy

LU Xutao; ZHI Chaoqun; ZHANG Lina; QIN Yingwei; LI Jing; WANG Ying

doi:10.11999/JEIT210219

Volume 44 Issue 1

Jan. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(1): 187-194

LU Xutao, ZHI Chaoqun, ZHANG Lina, QIN Yingwei, LI Jing, WANG Ying. Multi-UAV Regional Patrol Mission Planning Strategy[J]. Journal of Electronics & Information Technology, 2022, 44(1): 187-194. doi: 10.11999/JEIT210219

Citation:

LU Xutao, ZHI Chaoqun, ZHANG Lina, QIN Yingwei, LI Jing, WANG Ying. Multi-UAV Regional Patrol Mission Planning Strategy[J]. Journal of Electronics & Information Technology, 2022, 44(1): 187-194. doi: 10.11999/JEIT210219

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Multi-UAV Regional Patrol Mission Planning Strategy

doi: 10.11999/JEIT210219

1.
School of Mechatronics Engineering, North University of China, Taiyuan 030051, China
2.
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
3.
School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China
4.
School of Energy and Power Engineering, North University of China, Taiyuan 030051, China

Funds: Shanxi Applied Basic Research Project (201701D221124), Shanxi Key R & D Project (201903D221025), Shanxi Youth Science and Technology Fund (201801D221236)

Received Date: 2021-03-15
Accepted Date: 2021-11-17
Rev Recd Date: 2021-11-17

Available Online: 2021-11-20

Publish Date: 2022-01-10

Abstract

Abstract

At present, the emergency search Unmanned Aerial Vehicle (UAV) cluster has problems such as low search efficiency, low coverage integrity, and poor stability of multi-unit network. In this regard, a terminal-routing UAV area search task planning strategy based on Optimized Fuzzy C-Means Algorithm (O-FCMA) combined with Optimize-Hybrid Particle Swarm Optimization (O-HPSO) algorithm is proposed. Based on the scope of UAV monitoring area, by establishing the spatial model of the search area, this paper further uses O-FCMA to divide the area geometrically, and uses O-HPSO to realize the path planning in the divided area, so as to realize the planning of the overall task of multi-UAV cluster search. The simulation experiment is performed. The use of O-HPSO combined with O-FCMA for passive UAV area search task is compared with ACO or simulated annealing algorithm combined with K clustering algorithm or FCMA, under the condition of ensuring the full coverage of the search area, active search compared with non-active UAV, the UAV decision time is reduced by 7%～21%, 16%～31%, and the search efficiency is increased by 7%～13%, 3%～7%. The method reduces effectively the UAV cluster decision time and improves the search efficiency.

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References

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