Aircraft Flight Corridor Conflict Detection Method Based on Multi-view Geometry

doi:10.12382/bgxb.2024.0806

Abstract

Abstract:

The path planning and dynamic collision avoidance techniques for aircraft are effective means and key capabilities to prevent collisions caused by flight path conflicts. The three-dimensional collision detection is used for flight conflict judgment, and an aircraft flight corridor conflict detection method based on multi-view geometry is proposed. The flight route information is obtained through the automatic dependent surveillance-broadcast (ADS-B) system, and an aircraft flight corridor model is designed to construct a local situation of potential conflict area. Then, the conflict situation in the aircraft flight corridor is detected based on the geometric relationships in the three views of the local situation. Simulated results show that the proposed method can intuitively and accurately judge the conflict situation in aircraft flight corridor. Compared to the traditional spatial grid analysis method, the efficiency of solving the conflict situation is improved by 66.35%, the efficiency of solving nin-conflict situation is improved by 98.17%, and the overall solving efficiency is increased by 84.38%.

Key words: aircraft, multi-view geometry, computer vision, flight corridor, orthogonal projection, conflict detection

CLC Number:

XU Tongle, WANG Zhaochen, LIU Fang, XIAO Yujie, HE Yi, DOU Jihua. Aircraft Flight Corridor Conflict Detection Method Based on Multi-view Geometry[J]. Acta Armamentarii, 2025, 46(5): 240806-.

Figures/Tables 16

Fig.1 Elliptical cylinder shaped flight protection zone model

Fig.2 Schematic diagram of aircraft flight corridor conflict

Fig.3 Schematic diagram of perspective projection and orthogonal projection

Fig.4 Design flowchart of three-dimensional space conflict detection program based on multi-view geometry

Table 1 Position information of aircraft in Scenario 1

编号	型号	x轴坐标/m	y轴坐标/m	z轴坐标/m	2H_c/m	2R_c/m	方向角/(°)	俯仰角/(°)
Aircraft_A	DJI Mavic Air 2	30	30	80	60	100	45	30
Aircraft_B		100	20	60	60	100	190	50

Fig.5 Conflict detection result display interface based on multi-view geometry (Scenario 1)

Table 2 Position information of aircraft in Scenario 2

编号	型号	x轴坐标/m	y轴坐标/m	z轴坐标/m	2H_c/m	2R_c/m	方向角/(°)	俯仰角/(°)
Aircraft_A	DJI Mavic Air 2	20	0	80	60	100	5	15
Aircraft_B		150	20	70	60	100	8	-5

Fig.6 Conflict detection result display interface based on multi-view geometry (Scenario 2)

Fig.7 Conflict detection results using grid sampling method

Table 3 Collision detection results for two aircraft scenarios

方法分类	结果为不冲突次数	结果为冲突次数	结果错误次数	错误率/%
多视图几何法	85	65	2	1.33
网格化离散采样	88	62	3	2

Fig.8 Statistical comparison of solution time of experiment sequences without conflict

Fig.9 Statistical comparison of solution timr of experiment sequences with conflict

Table 4 Solution time and efficiency

情况类别	基于多视图几何法耗时/s	基于网格化离散采样法耗时/s	解算效率提升/%	实验次数
存在冲突	0.035	0.104	66.35	65
不存在冲突	0.035	1.917	98.17	85
整体			84.38	150

Fig.10 Conflict detection result display interface based on multi-view geometry (3 aircrafts)

Fig.11 Conflict detection result display interface based on multi-view geometry (4 aircrafts)

Fig.12 Relationship between solution time of multi-view geometry method and number of aircrafts

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