Unmanned Aerial Vehicle Path Planning for Improved Target Positioning Accuracy

doi:10.12382/bgxb.2023.0776

Abstract

Abstract:

An online cooperative path planning method for maneuvering target positioning in two typical mission scenarios is presented to improving the accuracy of maneuvering target positioning. Firstly, a direction-finding cross-target positioning model is established. The factors affecting the positioning accuracy are determined based on the geometric dilution of precision, and the effect of the influencing factors on the positioning accuracy is analyzed. Secondly, for the positioning of low-speed maneuvering target, an online cooperative track generation method based on variable curvature Dubins curve is proposed with a geometric accuracy factor as the evaluation index, and the fast track generation of the optimal configuration of cooperative positioning is realized. For the cooperative positioning of high-speed maneuvering targets, an optimal control model for target positioning accuracy is established, and a penalty function cooperative path planning method based on interior point method is proposed. Finally, the digital and semi-physical simulation verification is carried out. The results show that the target cooperative positioning accuracy is improved by 36.9% and 23.5%, respectively, in two typical task scenarios, which verifies the effectiveness of the proposed method. This method has the engineering application value for cluster cooperative target positioning.

Key words: unmanned aerial vehicle, path planning, collaborative positioning, positioning accuracy, positioning configuration, optimal control

CLC Number:

V249

FU Jinbo, ZHANG Dong, WANG Mengyang, ZHAO Junmin. Unmanned Aerial Vehicle Path Planning for Improved Target Positioning Accuracy[J]. Acta Armamentarii, 2023, 44(11): 3394-3406.

Figures/Tables 19

Fig.1 Schematic diagram of direction-finding cross-positioning

Fig.2 Simulated scenario diagram

Fig.3 Relationship between target position and GDOP at different altitudes

Fig.4 Low-speed target go-around co-positioning strategy

Fig.5 Schematic diagram of fly-by-wire co-positioning model

Fig.6 4 basic forms of Dubins curves

Fig.7 Path planning process

Fig.8 Schematic diagram of high-speed maneuvering target co-positioning scenario

Fig.9 Flowchart of the proposed algorithm

Table 1 Simulation parameters

参数	数值
无人机1初始状态	P₁=[ $993 m - 843 m - 0.3602 r a d$ ]
无人机2初始状态	P₂=[ $- 786 m 924 m - 3.1125 r a d$ ]
无人机3初始状态	P₃=[ $550 m 635 m 2.3166 r a d$ ]
无人机4初始状态	P₄=[ $- 831 m - 200 m - 1.5088 r a d$ ]
目标初始位置/m	P_T=[0 0]
基准点速度方向/rad	$π 2$
最低飞行高度/m	100
最小转弯半径/m	100

Table 1 Simulation parameters

参数	数值
无人机1初始状态	P₁=[ $993 m - 843 m - 0.3602 r a d$ ]
无人机2初始状态	P₂=[ $- 786 m 924 m - 3.1125 r a d$ ]
无人机3初始状态	P₃=[ $550 m 635 m 2.3166 r a d$ ]
无人机4初始状态	P₄=[ $- 831 m - 200 m - 1.5088 r a d$ ]
目标初始位置/m	P_T=[0 0]
基准点速度方向/rad	$π 2$
最低飞行高度/m	100
最小转弯半径/m	100

Fig.10 Comparison of cooperative path planning result paths

Fig.11 Comparison of errors of different methods

Table 2 Comparison of positioning errors

航迹规划方法	平均误差/m	航迹规划方法	平均误差/m
Dubins	1.2845	直线圆弧联合	2.7979
本文方法	0.8101	自适应等角螺线	1.7824

Table 3 Online planning simulation parameters

仿真参数	数值
无人机1初始位置/m	P₁=[ $10000$ ]
无人机2初始位置/m	P₂=[ $000$ ]
无人机3初始位置/m	P₃=[ $- 100 00$ ]
目标初始位置/m	P_T=[ $0100000$ ]
无人机初始速度/(m·s^-1)	30
目标速度大小/(m·s^-1)	30
无人机速度范围/(m·s^-1)	[20,40]
加速度范围/(m·s^-2)	[-1,1]
视线角范围/(°)	-15≤λ≤15
最大通信距离/m	3000

Table 3 Online planning simulation parameters

仿真参数	数值
无人机1初始位置/m	P₁=[ $10000$ ]
无人机2初始位置/m	P₂=[ $000$ ]
无人机3初始位置/m	P₃=[ $- 100 00$ ]
目标初始位置/m	P_T=[ $0100000$ ]
无人机初始速度/(m·s^-1)	30
目标速度大小/(m·s^-1)	30
无人机速度范围/(m·s^-1)	[20,40]
加速度范围/(m·s^-2)	[-1,1]
视线角范围/(°)	-15≤λ≤15
最大通信距离/m	3000

Fig.12 Comparison of paths of different methods

Fig.13 Comparison of positioning errors with different online methods

Fig.14 Semi-physical simulation experiment platform with combinination of virtuality and reality

Fig.15 Simulation of online planning for three-UAV cooperative positioning

Fig.16 UAV control outputs

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