基于多航线集群的蜂群无人机分布式协同控制

doi:10.16182/j.issn1004731x.joss.25-0848

摘要/Abstract

摘要：

针对大规模蜂群无人机协同控制以及队形保持问题，提出一种基于多航线集群的分布式无人机协同控制方法。利用多航线特性，将集群总体协同控制问题解耦为同航线内的协同控制与多航线间的一致性控制。在同航线内，设计前向邻居信息交互机制以确定邻居关系，并利用速度引导协同算法实现无人机期望的速度匹配与间距保持；在多航线间，设计邻航线头机信息交互机制，建立分布式头机选举策略，由头机根据航线类型设计队形一致量，并利用一致性算法维持集群期望队形。为定量评估集群协同控制效果，引入同航线邻居间距、序参量、航线跟踪误差及队形一致误差作为评价指标。仿真结果表明：该方法能使大规模蜂群无人机在集结阶段快速达成有序状态，并在任务飞行阶段有效维持期望队形，显著提高了协同效率，且具备优良的扩展性。

关键词: 蜂群无人机, 多航线集群, 信息交互机制, 分布式头机选举, 速度协同, 队形一致

Abstract:

To address the collaborative control and formation maintenance problems of large-scale swarm unmanned aerial vehicles, a distributed cooperative control method for unmanned aerial vehicles based on multi-route clustering was proposed. Leveraging the characteristics of multiple routes, the overall cooperative control problem of the swarm was decoupled into the cooperative control within the same route and the consensus control between multiple routes. Within the same route, a forward-neighbor information interaction mechanism was designed to determine the neighbor relationships, and a velocity-guided cooperative algorithm was utilized to achieve the desired velocity matching and spacing maintenance of unmanned aerial vehicles; between multiple routes, an adjacent-route leader unmanned aerial vehicle information interaction mechanism was designed; a distributed leader election strategy was established; formation consensus variables were designed by the leader according to the route type, and a consensus algorithm was utilized to maintain the desired formation of the swarm. To quantitatively evaluate the cooperative control effect of the swarm, the intra-route neighbor spacing, order parameter, route tracking error, and formation consensus error were introduced as evaluation indicators. Simulation results show that this method enables large-scale swarm unmanned aerial vehicles to quickly achieve an orderly state during the assembly phase and effectively maintains the desired formation during the mission flight phase, which significantly improves the cooperative efficiency and has excellent scalability.

Key words: swarm unmanned aerial vehicle, multi-route clustering, information interaction mechanism, distributed leader election, velocity coordination, formation consensus

中图分类号:

V279

熊雨阳,李春涛,邱文浩 . 基于多航线集群的蜂群无人机分布式协同控制[J]. 系统仿真学报, 2026, 38(7): 1832-1848.

Xiong Yuyang,Li Chuntao,Qiu Wenhao . Distributed Cooperative Control of Swarm Unmanned Aerial Vehicles Based on Multi-route Clustering[J]. Journal of System Simulation, 2026, 38(7): 1832-1848.

图/表 27

图1

图2

图3

图4

图5

图6

表1

仿真参数设置

参数	数值
无人机总数 $N$	72/120
邻居期望间距 $d r$ / $m$	100
位置协同衰减系数 $λ$	5
前向视野范围 $ω$ / $(°)$	60
速度项增益 $(k p, k v, k ξ, k α)$	(5,1,1,10)
水平速度限制 $(V m i n, V m a x)$ /( $m / s$ )	(50,200)
垂直速度限制 $(V z, m i n, V z, m a x)$ /( $m / s$ )	(-5,5)
目标速度 $v *$ /( $m / s$ )	120
自驾仪系数 $(τ V, τ ψ, τ z ˙, τ z)$	(2,1,1,5)

表1

表2

图7

图8

图9

图10

表3

表4

表5

任务飞行段参考圆弧航线

航段

圆心/m

半径/m

角度/rad

第2段

(10 000,

1 000,500)

1 300:100:2000

(- π / 2, π / 2)

第4段

(0,1 000,500)

1 300:100:2000

(π / 2, 3 π / 2)

表5

图11

图12

图13

图14

图15

图16

图17

表6

图18

图19

图20

表7

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航线	航点1	航点2
Ⅰ	(0,-300,500)	(5 000,-300,500)
Ⅱ	(0,-400,500)	(5 000,-400,500)
Ⅲ	(0,-500,500)	(5 000,-500,500)
Ⅳ	(0,-600,500)	(5 000,-600,500)
Ⅴ	(0,-700,500)	(5 000,-700,500)
Ⅵ	(0,-800,500)	(5 000,-800,500)
Ⅶ	(0,-900,500)	(5 000,-900,500)
Ⅷ	(0,-1 000,500)	(5 000,-1 000,500)

航线	航点	航段
航线	航点	第1段	第3段
Ⅰ	1	(0,-300,500)	(10 000,2 300,500)
Ⅰ	2	(10 000,-300,500)	(0,2 300,500)
Ⅱ	1	(0,-400,500)	(10 000,2 400,500)
Ⅱ	2	(10 000,-400,500)	(0,2 400,500)
Ⅲ	1	(0,-500,500)	(10 000,2 500,500)
Ⅲ	2	(10 000,-500,500)	(0,2 500,500)
Ⅳ	1	(0,-600,500)	(10 000,2 600,500)
Ⅳ	2	(10 000,-600,500)	(0,2 600,500)
Ⅴ	1	(0,-700,500)	(10 000,2 700,500)
Ⅴ	2	(10 000,-700,500)	(0,2 700,500)
Ⅵ	1	(0,-800,500)	(10 000,2 800,500)
Ⅵ	2	(10 000,-800,500)	(0,2 800,500)
Ⅶ	1	(0,-900,500)	(10 000,2 900,500)
Ⅶ	2	(10 000,-900,500)	(0,2 900,500)
Ⅷ	1	(0,-10 00,500)	(10 000,3 000,500)
Ⅷ	2	(10 000,-1 000,500)	(0,3 000,500)

算法	速度收敛时间	序参量收敛时间
本文	40	3
文献[14]	46	8