复杂低空无人机飞行冲突网络建模与精细管理

doi:10.7527/S1000-6893.2023.28226

Abstract

Abstract:

The large-scale application and intensive flight of Unmanned Aerial Vehicle （UAV） in the future bring about safety problems. In view of the complex low-altitude system formed by the interweaving of diversified airspace， high-density traffic and high dynamic evolution， the problem of network modeling and refined management of complex low-altitude UAV flight conflicts is studied. Firstly， the Gilbert-Johnson-Keerthi （GJK） algorithm is used to construct a UAV flight conflict network model based on the dynamic identification of conflicting edges， and the characteristic parameters of the UAV flight conflict network are characterized by the average number of conflicts， the average operation risk and the average clustering coefficient. Then， based on the spatial raster coding theory， a raster segmentation and coding design method for low-altitude airspace system is proposed， and a digital raster-based UAV flight conflict detection algorithm for discrete airspace environment is established. Finally， a multi-objective mixed integer optimization model for UAV conflict resolution considering priority is constructed with the optimization objectives of the minimum number of UAV conflicts and the lowest operation cost. Moreover， an intelligent UAV conflict resolution algorithm based on Non-Dominated Sorting Genetic Algorithm-Ⅱ（NSGA-Ⅱ） algorithm is designed to realize the optimal resolution of UAV flight conflicts under multi-task scenarios and different priorities. The optimal resolution of UAV flight conflicts under multi-mission scenarios and different priorities is then achieved. Simulation experiments show that compared with the traditional 3D coordinate-based conflict detection method， the proposed digital raster-based conflict detection method can reduce the total detection time by 78.4%， and reduce the UAV conflict detection efficiency from “exponential growth level” to “linear growth level” in multi-model hybrid scenarios. When the average conflict risk level is used as the deconfliction priority principle， the UAV conflict deconfliction model can obtain non-dominated solutions with better performance and reduce the average adjustment values of altitude layer， horizontal trajectory and speed per UAV by 32.8%， 21.4% and 14.6% compared with the principle without considering the priority. It is verified that the fine management method of UAV flight conflict proposed in this paper is effective and can provide theoretical support and methodological guidance for the safety management of low-altitude airspace and UAV risk prevention and control.

Key words: UAV, air traffic management, airspace grids, conflict network, conflict detection, conflict resolution

CLC Number:

V355

Hua XIE, Fangzheng SU, Jianan YIN, Site HAN, Xinjue ZHANG. Network modeling and refined management of UAV flight conflicts in complex low altitude airspace[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(18): 328226-328226.

Figures/Tables 25

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Table 1

Fig.9

Fig.10

Table 2

Airspace grid classification hierarchy

剖分层级	栅格大小	赤道附近大致尺/km
第1级	$15 ° × 15 °$	1 669
第2级	$1 ° × 1 °$	111
第3级	$30' × 30'$	56
第4级	$10' × 10'$	9
第5级	$1' × 1'$	1
第6级	$6 " × 6 "$	0.2
第7级	$3 " × 3 "$	0.1
第8级	$1 " × 1 "$	0.03

Table 2

Fig. 11

Fig.12

Fig. 13

Fig.14

Fig.15

Fig.16

Fig.17

Table 3

Parameter of UAVs conflict resolution model

参数	取值
离散时间步长 $Δ t$ /s	1
权重系数 $η i$ 最大允许航线长度扩展系数 $l i$	（0， 1］ 0.25
允许设置的虚拟航路点数量 $M$	2
虚拟航路点自定义参数 $α i$	0.125
虚拟航路点自定义参数 $β i$	0.1
最大高度层改变量 $h i, m a x$	5
最大飞行速度 $v i, m a x$ /（m∙s^-1）	30
最小飞行速度 $v i, m i n$ /（m∙s^-1）	1

Table 3

Fig.18

Fig.19

Fig.20

Table 4

Fig.21

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演化次数	冲突数量	冲突风险等级	聚集系数
1	3.175 89	6.149 88	0.535 68
2	3.207 09	6.341 41	0.652 10
⋮	⋮	⋮	⋮
60	2.535 58	7.405 40	0.509 48

指标	原则1	原则2	原则3
初始冲突数量	93	93	93
初始飞行成本	5 916	5 916	5 916
总运行成本最小的非支配解的冲突数量	65	46	43
总运行成本最小的非支配解的运行成本	4 940.5	4 455.7	4 832.6
总冲突数量最小的非支配解的冲突数量	14	0	0
总冲突数量最小的非支配解的运行成本	5 613.7	5 363.1	5 647.4
算法求解时间/s	18.4	14.1	15.3

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Network modeling and refined management of UAV flight conflicts in complex low altitude airspace

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