三维动态环境下的无人机集群双层航迹规划

doi:10.12305/j.issn.1001-506X.2026.01.13

Abstract

Abstract:

To solve the path planning problem in complex three-dimensional environment, a method for two-layered path planning of unmanned areial vehicle （UAV） swarm is proposed, integrating differential evolution （DE） and model predictive control （MPC）. Firstly, two levels of global and local levels are constructed to plan the framework, and local planning is corrected in real time through track tracking to enhance robustness. Secondly, when the global environment is known, environmental information is established, and the DE algorithm is used to perform global optimization search planning to obtain reference planning. Finally, a distributed MPC algorithm is proposed, the discretized UAV kinematic characteristics are used as a prediction model, and an objective function is formulated to achieve obstacle avoidance and collision avoidance control. The feasibility of the proposed algorithm is verified through simulation experiment. The proposed method can rapidly plan a safe and stable flyable planning for individual UAVs within a cluster to reach target points in complex mountainous environments, where dynamic and static obstacles are combined.

Key words: unmanned aerial vehicle (UAV) swarm, path planning, three-dimensional dynamic, differential evolution (DE), model predictive control (MPC)

CLC Number:

Zeyu LU, Yao WANG, Weinan WU, Yiming SUN, Chunlin GONG. Two-layered path planning for unmanned aerial vehicle swarm in three-dimensional dynamic environment[J]. Systems Engineering and Electronics, 2026, 48(1): 132-143.

Figures/Tables 12

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References 33

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参数	数值
质量$ m $/kg	9298.60
翼展$b$/m	9.14
机翼面积$S$/m²	27.87
空速${V_T}$/(m/s)	220~260
主惯性矩${J_{xx}}$/(kg·m²)	12820.61
主惯性矩${J_{yy}}$/(kg·m²)	75673.62
主惯性矩${J_{zz}}$/(kg·m²)	85552.11
惯性积${J_{xz}}$/(kg·m²)	1331.41
副翼最大偏转${\delta _a}$/(°)	$ \pm 25.0$
升降舵最大偏转${\delta _e}$/(°)	$ \pm 21.5 $
最大舵偏转${\delta _r}$/(°)	$ \pm 30.0 $
通信范围${R_{{\text{comm}}}}$/km	10

参数	数值
最大飞行高度${H_{\max }}$/m	6000
最小飞行高度${H_{\min }}$/m	10.0
最大俯仰角${\theta _{\max }}$/(°)	20
最大水平转弯角${\alpha _{\max }}$/(°)	60
高度安全裕度$\Delta h$/m	260.0
安全距离$D$/m	60.0
种群数${N_{{\mathrm{size}}}}$	50
最大迭代次数${G_{\max }}$	300
预测时域${N_p}$	25
控制时域${N_c}$	3
仿真步长	1

集群	无人机编号	起点			初始航向角/rad
集群	无人机编号	x/km	y/km	z/km	初始航向角/rad
1	1	8	14.5	3.7	0
	2	8	15.5	3.7	0
	3	8	16.5	3.7	0
	4	8	17.5	3.7	0
	5	8	18.5	3.7	0
	6	8	19.5	3.7	0
	7	8	20.5	3.7	0
	8	8	21.5	3.7	0
	9	8	22.5	3.7	0
	10	8	23.5	3.7	0
2	1	10.2	8.5	2.1	$\dfrac{\text{π} }{2}$
	2	11.2	8.5	2.1	$\dfrac{\text{π} }{2}$
	3	12.2	8.5	2.1	$\dfrac{\text{π} }{2}$
	4	13.2	8.5	2.1	$\dfrac{\text{π} }{2}$
	5	14.2	8.5	2.1	$\dfrac{\text{π} }{2}$
	6	15.2	8.5	2.1	$\dfrac{\text{π} }{2}$
	7	16.2	8.5	2.1	$\dfrac{\text{π} }{2}$
	8	17.2	8.5	2.1	$\dfrac{\text{π} }{2}$
	9	18.2	8.5	2.1	$\dfrac{\text{π} }{2}$
	10	19.2	8.5	2.1	$\dfrac{\text{π} }{2}$
	11	20.2	8.5	2.1	$\dfrac{\text{π} }{2}$
	12	21.2	8.5	2.1	$\dfrac{\text{π} }{2}$
	13	22.2	8.5	2.1	$\dfrac{\text{π} }{2}$

集群	目标点
集群	$ x $	$ y $	$ z $
1	64.6	18	2.8
2	16.2	53.9	3

仿真序号	单层规划运行时间	双层规划运行时间
1	1.7728	1.4342
2	1.6812	1.4260
3	1.6663	1.4251
4	1.6279	1.4113
5	1.7299	1.3883

Two-layered path planning for unmanned aerial vehicle swarm in three-dimensional dynamic environment

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