考虑安全性评估的无人机集群鸽群优化避碰决策

彭雅兰; 段海滨; 魏晨

doi:10.13700/j.bh.1001-5965.2025.0444

考虑安全性评估的无人机集群鸽群优化避碰决策

doi: 10.13700/j.bh.1001-5965.2025.0444

北京航空航天大学自动化科学与电气工程学院飞行器一体化控制全国重点实验室，北京 100191

基金项目:

国家自然科学基金(U24B20156,62350048,T2121003)

详细信息

通讯作者:
E-mail：hbduan@buaa.edu.cn

中图分类号: V221⁺.3；TB553
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出版历程
- 收稿日期: 2025-07-04
- 录用日期: 2025-09-05
- 网络出版日期: 2025-09-28
- 整期出版日期: 2026-10-31

A safety-evaluation-driven UAV swarm collision avoidance decision-making based on pigeon-inspired optimization

National Key Laboratory of Aircraft Integrated Flight Control，School of Automation Science and Electrical Engineering，Beihang University，Beijing 100191，China

Funds:

National Natural Science Foundation of China (U24B20156, 62350048,T2121003)

More Information

Corresponding author: E-mail：hbduan@buaa.edu.cn

摘要

摘要:
为提高无人机(UAV)集群在复杂环境中的避碰能力与协同效率，提出一种考虑安全性评估的无人机集群鸽群优化(PIO)避碰决策机制。通过分析无人机集群局部冲突风险与整体网络协调程度，构建多维安全性评估指标体系；利用主成分分析法对集群状态进行综合安全评估，并将避碰决策分为安全、警戒与高危3个阶段。基于综合安全性评估得分动态调节的人工势场函数，实现个体避碰控制参数的自适应调整。为进一步提升高风险状态下集群全局避碰协同效率，引入PIO算法对无人机运动方向与避碰参数进行协同优化。仿真测试结果表明：所提方法在保持集群稳定性的同时有效降低了局部冲突概率，验证了该方法的有效性与实用性。
- 无人机 /
- 鸽群优化 /
- 避碰决策 /
- 安全性评估 /
- 主成分分析法 /
- 人工势场法
Abstract:
This research proposes an adaptive avoidance decision-making mechanism that combines safety assessment with pigeon-inspired optimization (PIO) to enhance the collision avoidance capabilities and coordination efficiency of unmanned aerial vehicle (UAV) swarms operating in complicated environments. A multidimensional safety evaluation index system is constructed by analyzing both local conflict risks and the overall coordination level of the swarm. Principal component analysis is employed to derive a comprehensive safety score, based on which a three-stage avoidance control strategy is formulated. An artificial potential field function with adaptively tuned parameters is designed according to the safety score, enabling responsive and autonomous individual-level control. Furthermore, to improve global cooperative avoidance under high-risk scenarios, the improved PIO algorithm is embedded to jointly optimize the velocity direction and avoidance parameters of swarm members. The effectiveness and resilience of the suggested strategy in dynamic contexts are validated by simulation findings, which show that it dramatically lowers the likelihood of local conflicts while preserving swarm cohesion.
- unmanned aerial vehicle /
- pigeon-inspired optimization /
- collision avoidance decision /
- safety evaluation /
- principal component analysis /
- artificial potential field

HTML全文

图 1 双无人机冲突

Figure 1. Two-UAV conflict scenario

下载: 全尺寸图片幻灯片

图 2 安全等级驱动避碰决策框架

Figure 2. Framework of security-level-driven collision avoidance decision

下载: 全尺寸图片幻灯片

图 3 无人机集群三维飞行轨迹

Figure 3. 3D flight path of UAV swarm

下载: 全尺寸图片幻灯片

图 4 无人机集群飞行轨迹俯视图

Figure 4. Top view of flight path of UAV swarm

下载: 全尺寸图片幻灯片

表 1 无人机集群安全等级划分

Table 1. UAV swarm security classification

等级	综合安全性评估得分	模式类型	控制策略
安全区	$ {S}_{{\mathrm{FSA}}i}\leqslant {\theta }_{1} $	协同前进	吸引力占主导，维持队形或目标引导
警戒区	$ {\theta }_{1}< {S}_{{\mathrm{FSA}}i}< {\theta }_{2} $	正常避碰	平衡吸引力与排斥力，进行协调避碰
高危区	$ {\theta }_{2}\leqslant {S}_{{\mathrm{FSA}}i} $	紧急避碰	排斥权重最大化，抑制吸引力，优化避碰
注：$ {\theta }_{1} $、$ {\theta }_{2} $为策略阈值，可根据任务需求设定。

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表 2 算法参数

Table 2. Parameters of algorithms

算法	参数	数值
APF	吸引力系数k_a	5
	排斥力系数k_r	15
	排斥力作用阈值d₀/m	200
	权重调节灵敏度系数α	0.75
	权重调节灵敏度系数β	0.75
PIO	鸽子种群数量N_p	20
	地图和指南针算子迭代次数N_c1	100
	地标算子迭代次数N_c2	80
	地图和指南针因子r	0.02

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表 3 安全性评估指标数据

Table 3. Data on security assessment indicators

指标	A	E	F^max	W	S	C	$ {F}_{i} $	冲突次数
APF	1.42	117.80	19.30	5.10	0.49	0.53	0.75	5.10
PSO	1.17	96.50	14.70	4.60	0.42	0.45	0.67	2.40
PIO	1.12	91.10	13.50	4.40	0.38	0.41	0.62	1.20
FSA-PIO（本文）	0.95	78.90	10.20	3.80	0.32	0.35	0.51	0.20

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