A Swarm Intelligence Algorithm for UAV Path Planning in Urban Warfare

doi:10.12382/bgxb.2023.0869

Abstract

Abstract:

For the traditional path planning algorithm used to solve the tactical path planning problem of unmanned aerial vehicle, there are some problems, such as local optimum and slow convergence. In this paper, an improved grey wolf algorithm is proposed, which changes the updating mode of the initial population, improves the global optimization ability of grey wolf algorithm and speeds up its convergence speed. The Lévy flight random strategy and symbiotic search algorithm are introduced. The Lévy flight strategy isused to update the population individuals, and the symbiotic search algorithm is usedto avoid the local optimal problem. Threat modeling is an important prerequisite for UAV path planning, and the equivalent preprocessing of the threats is performed,and the algorithm is verifiedby combining the fitness function. And a simulation verification platform, incorporating virtual-to-real mapping, was designed to validate the algorithm’s effectiveness through a real-to-virtual approach. The experimental results show that the improved algorithm effectively improves the path planning problems such as local optimum and slow convergence of the traditional path planning algorithm, and has a certain reference value for the improvement of UAV combat capability.

Key words: unmanned aerial vehicle path planning, grey wolf algorithm, the Lévy flight random strategy, symbiotic search algorithm

CLC Number:

TP391

LU Ying, PANG Lichen, CHEN Yusi, SONG Wanying, FU Yanfang. A Swarm Intelligence Algorithm for UAV Path Planning in Urban Warfare[J]. Acta Armamentarii, 2023, 44(S2): 146-156.

Figures/Tables 21

Fig.1 Threat modelling

Fig.2 Two-dimensional schematic diagram of UAV path planning

Fig.3 Three-dimensional schematic diagram of UAV path planning

Fig.4 Calculation of threat measure

Fig.5 Calculation of three-dimensional threat measure

Fig.6 AirSim overall framework

Fig.7 Simulation platform frame diagram

Table 1 Threat area information

案例	威胁中心/m	威胁半径/m	高度/m
	(350,100)	80	500
	(200,600)	150	800
	(600,150)	100	500
案例1	(450,800)	75	300
	(680,400)	120	500
	(850,600)	70	750
	(150,250)	80	700
	(750,800)	100	1000
	(200,200)	100	1000
	(380,450)	150	1000
	(550,150)	125	650
案例2	(250,750)	150	800
	(750,450)	100	600
	(800,250)	150	850
	(500,700)	150	1000
	(800,850)	100	750

Fig.8 Case 1:three-dimensional path planning

Fig.9 Case 1:top view of three-dimensional path planning

Fig.10 Case 2:three-dimensional path planning

Fig.11 Case 2:top view of three-dimensional path planning

Fig.12 Case 1: convergence curves

Fig.13 Case 2: convergence curves

Fig.14 Comparison of motion trajectories

Table 2 Average error rate of motion trajectories of sampled real and virtual UAVs in each axis direction

x轴运动轨迹误差率平均值	y轴运动轨迹误差率平均值	z轴运动轨迹误差率平均值
0.2494	0.2392	0.2420

Fig.15 UE4 Scenario

Table 3 Task target point information

任务目标点	起始点/m	任务目标点/m	移动威胁运动方向/(m·s^-1)
1	(0, 0, 0)	(81, 25, 10)	(0, 1, 0)
2	(0, 0, 0)	(55, 57, 20)	(1, 0, 0)
3	(0, 0, 0)	(85, 70, 4)	(0, 0, 1)

Fig.16 UAV moving towards target point 1

Fig.17 Mean number of collisions

Fig.18 Mean flight time

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