基于视野和速度引导的无人机集群避障算法

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

摘要/Abstract

摘要：

在未来的多无人机(unmanned aerial vehicle, UAV)空中作战中，无人机集群在未知空域中安全飞行是集群研究中的重要内容。针对无人机集群避障以及集群形态保持问题，提出了一种基于视野和速度引导(visual field and velocity guidance, VFVG)的集群避撞算法。基于视野法设计集群自适应通讯拓扑机制，结合远吸近斥势力原则及一致性方法，在保持集群形态的同时，加速了集群无人机个体间的避障信息的传递。在此基础上，提出将极限环与人工势场法相结合构造避障速度引导项，解决了集群遇障分群困难、避障徘徊停滞等问题。引入避障时间指标，验证了算法的避障效率。仿真结果表明，该方法能够使多无人机以良好的集群形态安全快速平稳地通过复杂障碍区域，有效提高了集群避障成功率和避障效率。

关键词: 无人机集群, 视野法, 人工势场, 速度引导, 局部极值

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

中图分类号:

V279

桂雪琪,李春涛 . 基于视野和速度引导的无人机集群避障算法[J]. 系统仿真学报, 2024, 36(3): 545-554.

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.

图/表 15

图1

图 2

图 3

图 4

图 5

图 6

图 7

图 8

表1

表2

无人机参数设置

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]

表2

图9

表3

图 10

图 11

图 12

参考文献 17

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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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