面向未知拒止环境的分布式自适应多无人机协同航迹规划

doi:10.12305/j.issn.1001-506X.2026.01.16

摘要/Abstract

摘要：

针对无人机（unmanned aerial vehicle, UAV）集群在未知环境下的航迹规划问题，考虑通信拒止环境下UAV无法使用全局定位系统等通信手段的场景，提出一种面向未知拒止环境的分布式自适应多无人机协同航迹规划方法。在UAV集群航迹规划的过程中结合导航模块以及避障模块并采用自适应目标导向策略（adaptive destination-oriented strategy, ADOS），通过建立评价体系来保证集群速度在整体运行时的一致性、避免机间碰撞，进而建立适应度函数并使用麻雀搜索算法构建分布式的优化框架，最终通过最大化适应度函数值确定导航模块和避障模块的最优参数完成整个航迹规划的优化过程。此外，在避障模块中，引入了“UAV视线”的概念，并基于此提出一种高效的避障方法，该策略通过将多条视线作为备选避障方向提高集群航迹规划的效率。最后, 通过在构建的不同测试平台中进行避障飞行仿真实验并与现有的算法进行性能对比，验证所提算法的有效性。

关键词: 无人机集群, 碰撞避免, 麻雀搜索算法, 分布式控制

Abstract:

Aiming at the path planning problem of unmanned aerial vehicle （UAV） swarm in unknown environments, a distributed adaptive multi-UAV collaborative path planning method, especially in communication denial environments where UAV cannot use global positioning systems. This paper combines the navigation module and obstacle-avoidance module, and use the adaptive destination-oriented strategy （ADOS） in the process of UAV swarm path planning. Moreover, this paper have constructed an evaluation system to ensure the consensus of swarm velocity in the overall process, and no collision between UAVs. Then, a fitness function is established and a distributed optimization framework is built by using the sparrow search algorithm. Finally, this paper optimizes the whole path planning process through minimizing the optimal parameters of navigation module and obstacle-avoidance module. It is particularly noteworthy that this paper introduces the concept of “UAV line of sight” in obstacle-avoidance module, and based on this, an efficient obstacle-avoidance method is obtained to improve the efficiency of the swarm path planning by using multiple lines of sight as alternative obstacle-avoidance lines. Finally, we conduct obstacle avoidance flight simulation experiments in different test platforms and compared with existing algorithms, which demonstrate the effectiveness of the proposed algorithm.

Key words: unmanned aerial vehicle （UAV）swarm, collision avoidance, sparrow search algorithm, distributed control

中图分类号:

TP 391.9

徐奇琛, 张朝辉, 李靖. 面向未知拒止环境的分布式自适应多无人机协同航迹规划[J]. 系统工程与电子技术, 2026, 48(1): 172-184.

Qichen XU, Zhaohui ZHANG, Jing LI. Distributed adaptive multi-UAV collaborative path planning in unknown denial environments[J]. Systems Engineering and Electronics, 2026, 48(1): 172-184.

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编号	k
编号	1	2	3	4	5	6	7	8	9	10	11	12
F_dire	0.5551	0.6055	0.578	0.4888	0.383	0.2969	0.2438	0.2235	0.2342	0.2769	0.3533	0.4558
F_dist	0.7351	0.6705	0.65	0.7763	0.9963	0.6811	0.6346	0.5996	0.7654	0.9071	0.9533	0.9087
F	0.2999	0.2722	0.2442	0.2945	0.3802	0.1377	0.0982	0.0803	0.1372	0.2278	0.3211	0.3764
单位方向	40.00	6.25	3.89	0.00	−8.11	−34.64	−6.70	−6.57	−9.95	0.00	5.03	34.64
	0.00	3.61	6.74	40.00	14.05	20.00	0.00	−3.79	−17.23	−40.00	−8.71	−20.00
	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

参数	参数范围	最优点
${\phi ^\text{vel}}$	$ [0,{V_{\max }}/{v_0}] $	1
${\phi ^\text{corr}}$	$ {\text{[ − 1,1] }} $	1
${\phi ^\text{u2u}}$	$ {\text{[0,1]}} $	0
${\phi ^\text{u2o}}$	$\{ 0,1, \cdots ,N\} $	0
${\phi ^\text{arrive}}$	$ {\text{[0,1]}} $	1

参数	数值
${p_s}$	7.0789
${p_a}$	1.8849
${p_c}$	0.01
$\lambda $	0.0003
$\mu $	0.9459
${F_{\min }}$	−25798

算法	所提算法	Vásárhelyi所提算法	BICS算法
时间/s	58.05	100+	65

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