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

系统工程与电子技术 ›› 2026, Vol. 48 ›› Issue (1): 132-143.doi: 10.12305/j.issn.1001-506X.2026.01.13

三维动态环境下的无人机集群双层航迹规划

陆则宇¹(), 王瑶², 吴蔚楠¹^,*(), 孙亦鸣³, 龚春林¹

1. 西北工业大学航天学院，陕西西安 710072
2. 湖北航天技术研究院总体设计所，湖北武汉 430048
3. 西北机电工程研究所，陕西咸阳 712099

收稿日期:2024-05-15 出版日期:2026-01-25 发布日期:2026-02-11
通讯作者: 吴蔚楠 E-mail:zeyulu@mail.nwpu.edu.cn;wuweinan@nwpu.edu.cn
作者简介:陆则宇（2000—），男，博士研究生，主要研究方向为多飞行器智能规划与集群决策
王　瑶（1998—），女，助理工程师，硕士，主要研究方向为飞行器总体设计、多飞行器智能规划
孙亦鸣（2000—），男，研究实习员，硕士，主要研究方向为多飞行器智能规划与集群决策
龚春林（1980—），男，教授，博士，主要研究方向为飞行器总体设计，飞行器设计理论、方法与运用
基金资助:
国家自然科学基金（72001173）资助课题

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

Zeyu LU¹(), Yao WANG², Weinan WU¹^,*(), Yiming SUN³, Chunlin GONG¹

1. School of Astronautics，Northwestern Polytechnical University，Xi’an 710072，China
2. The Overall Design Institute of Academy of Hubei Aerospace Technology，Wuhan 430048，China
3. Northwest Institute of Mechanical & Electrical Engineering，Xianyang 712099，China

Received:2024-05-15 Online:2026-01-25 Published:2026-02-11
Contact: Weinan WU E-mail:zeyulu@mail.nwpu.edu.cn;wuweinan@nwpu.edu.cn

摘要/Abstract

摘要：

针对复杂三维动态环境下的航迹规划问题，提出基于差分进化(differential evolution, DE)与模型预测控制(model predictive control, MPC)的无人机集群双层航迹规划方法。首先，构建全局和局部两个层次来规划框架，并通过航迹跟踪实时修正局部航迹以增强鲁棒性。然后，在全局环境已知的情况下建立环境信息，采用DE算法进行全局优化搜索规划出参考航迹。最后，提出分布式MPC算法，以离散化的无人机运动学特性作为预测模型，并制定目标函数以实现避障避碰控制。仿真实验验证了所提算法的可行性。所提方法能在动、静障碍物相结合的复杂山地环境中，为集群中的单无人机快速规划出一条安全、稳定抵达目标点的可飞航迹。

关键词: 无人机集群, 航迹规划, 三维动态, 差分进化, 模型预测控制

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)

中图分类号:

陆则宇, 王瑶, 吴蔚楠, 孙亦鸣, 龚春林. 三维动态环境下的无人机集群双层航迹规划[J]. 系统工程与电子技术, 2026, 48(1): 132-143.

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.

图/表 12

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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

三维动态环境下的无人机集群双层航迹规划

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

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