UAV Swarm Obstacle Avoidance Algorithm Based on Visual Field and Velocity Guidance

doi:10.16182/j.issn1004731x.joss.22-1287

Abstract

Abstract:

In the future aerial combat of multiple unmanned aerial vehicles (UAVs), the safe flight of UAV swarm in unknown airspace is an important content of swarm research. In view of avoiding obstacles and maintaining behavior in the UAV swarm system, this paper presents a UAV swarm collision avoidance algorithm based on visual field and velocity guidance (VFVG). The swarm adaptive communication topology mechanism is designed based on the visual field method. Combined with the principle of far attraction and near repulsion and the consensus method, the mechanism can accelerate the transmission of obstacle avoidance information among UAV swarms while maintaining thebehavior. On this basis, the limit cycle is combined with the artificial potential field method to construct the obstacle avoidance velocity guidance term, which solves problems such as the difficulty of swarm separation, obstacle avoidance hovering, and stagnation. To evaluate the obstacle avoidance efficiency of the proposed algorithm, an obstacle avoidance time index is introduced. The simulation results show that the proposed method can make multiple UAVs pass through complex obstacle areas safely, quickly, and smoothly with good swarm behavior and effectively improve the success rate and efficiency of swarm obstacle avoidance.

Key words: UAV swarm, visual field method, artificial potential field, velocity guidance, local extremum

CLC Number:

V279

Gui Xueqi, Li Chuntao. UAV Swarm Obstacle Avoidance Algorithm Based on Visual Field and Velocity Guidance[J]. Journal of System Simulation, 2024, 36(3): 545-554.

Figures/Tables 15

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Fig. 8

Table 1

Table 2

UAV parameter setting

参数	数值	参数	数值
$v m a x$ /(m/s)	5	$u m a x$ /(m/s²)	10
[ $d p$ , $d o$ ]	[5,10]	$r δ / m$	10
$ω$	[-π/2, π/2]	$v γ$ /(m/s)	[5,0]
$p γ$ /m	[80,0]	[ $c 1 γ$ , $c 2 γ$ ]	[0.05,0.3]
[ $c 1 α$ , $c 2 α$ ]	[0.8,1.4]	[ $c 1 β$ , $c 2 β$ ]	[0.5,0.7]

Table 2

Fig. 9

Table 3

Fig. 10

Fig. 11

Fig. 12

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无人机序列	位置/m	速度/(m/s)
UAV0	[2.9,0.7]	[3.4,-0.3]
UAV1	[16.7,-4.6]	[3.3,-0.4]
UAV2	[7.5,-1.1]	[3.5,-0.2]
UAV3	[10.4,7.2]	[3.3-0.27]
UAV4	[8.3,-6.1]	[3.6,0.05]
UAV5	[6.6,4.0]	[3.5,-0.26]
UAV6	[3.6,-4.3]	[3.3,-0.2]
UAV7	[11.3,2.2]	[3.6,-0.2]
UAV8	[12,-2.8]	[2.5,-0.1]
UAV9	[15.9,0.4]	[3.5,-0.34]

障碍物坐标/m	算法	避障耗时/s
[40,0]	VFVG	13.8
[40,0]	Flocking	避障失败
[40,-5]	VFVG	12.18
[40,-5]	Flocking	避障失败
[40,10]	VFVG	10.48
[40,10]	Flocking	15.60
[40,-10]	VFVG	11.23
[40,-10]	Flocking	14.95

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