智能无人机集群可靠性技术内涵、发展及挑战

doi:10.12382/bgxb.2024.0322

摘要/Abstract

摘要：

随着无人机相关技术的快速发展,智能无人机集群得到越来越多的研究。智能无人机集群应用自然界集群组织算法模型,并结合其他智能技术,形成高级群体智能行为,具有传统单人工智能体无法比拟的优势,但是可靠性问题也是制约其面向大规模应用的一个重要因素。系统分析智能无人机集群的概念、特征及技术发展路径,基于智能无人机集群的应用场景、应用模式及行为过程,探讨智能无人机集群的可靠性内涵,构建无人机集群可靠性技术框架,并结合智能无人机集群的技术特征和应用需求,给出智能无人机集群可靠性技术发展的技术途径与建议。最终在“1个核心,2种能力,N个基础”的可靠性内涵下,提出由可靠性指标、可靠性研制验证以及可靠性维持保障技术组成的智能UAVS可靠性技术框架。

关键词: 无人机集群, 人工智能, 集群智能, 可靠性

Abstract:

With the rapid development of unmanned aerial vehicle (UAV)-related technologies,the intelligent UAV swarms have attracted more and more research attention.The organizational algorithm models of natural swarms are used for the intelligent UAV swarms,and combined with other intelligent technologies to form advanced group intelligence behaviors,which have the advantages that traditional single artificial intelligent agents cannot match.However,the issue of reliability is also an important factor that restricts its large-scale application.The concept,characteristics,and technological development path of intelligent UAV swarms are systematically analyzed.Based on the application scenarios,application modes,and behavioral processes of intelligent UAV swarms,the connotation of UAV swarm reliability is discussed.A technological framework for the reliability of UAV swarms is constructed,and the technical approaches and suggestions for the development of UAV swarm reliability technology are given based on the technological characteristics and application requirements of intelligent UAV swarms.Under the reliability connotation of “one core, two capabilities, and N foundations,” a reliability technology framework for intelligent UAVs has been proposed,which consists of reliability metrics,reliability development and verification,and reliability maintenance and support technologies.

Key words: unmanned aerial vehicle swarm, artificial intelligence, swarm intelligence, reliability

中图分类号:

V279

曾照洋, 彭文胜, 李云凯, 徐明. 智能无人机集群可靠性技术内涵、发展及挑战[J]. 兵工学报, 2025, 46(3): 240322-.

ZENG Zhaoyang, PENG Wensheng, LI Yunkai, XU Ming. Connotation,Development and Challenges of Reliability Technology of Intelligent UAV Swarm[J]. Acta Armamentarii, 2025, 46(3): 240322-.

图/表 13

图1 UAVS

Fig.1 UAV swarm

图2 UAVS应用于军事

Fig.2 Military UAV swarm

图3 集中式控制模式

Fig.3 Centralized control

图4 分散式控制模式

Fig.4 Decentralized control

表1 智能UAVS关键技术面临的可靠性问题

Table 1 Reliability issues faced by key technologies of intelligent UAV swarms

智能UAVS关键技术	可靠性问题
自主控制算法	算法复杂度、易受攻击、资源受限等
异构无人机协同	协同效率、可解释性、软硬件兼容问题
网络通信技术	不稳定的信号、易受攻击
分布式数据融合算法	数据丢失、算法扩展性
大数据技术	数据丢失、软硬件兼容问题等
机器学习技术	泛化能力、可解释性问题

图5 智能UAVS系统组成

Fig.5 Intelligent UAV swarm system composition

图6 智能UAVS典型任务剖面

Fig.6 Typical mission profile of intelligent UAV swarm

图7 智能UAVS可靠性的影响要素

Fig.7 Factors influencing the reliability of intelligent UAV swarm

图8 智能UAVS可靠性内涵

Fig.8 The connotation of reliability of intelligent UAV swarm

图9 智能UAVS可靠性技术框架

Fig.9 Technical framework for the reliability of intelligent UAV swarm

表2 可靠性指标体系

Table 2 Reliability index system

体系层次	可靠性指标需求
任务完成	任务成功率
功能实现	协同可靠性、自组织适应性、智能决策准确性、自主恢复效率
结构组成	硬件可靠性、软件可靠性、算法鲁棒性、数据质量和安全性、通信可靠性等

表3 可靠性参数模型与功能

Table 3 Reliability parameter model and function

模型类别	可靠性参数模型	模型功能	相关研究
集群行为模型类	协同度模型	用于评估通信效率、任务完成率等指标	文献[86]提出的UAVS的协同度模型,其中包括协同时间基本模型、协同信息量模型,以及基于多Agent的协同方法协同性能模型。该文提出的协同时间T为 T=T₁+T₂+T₃+T₄+T₅ 式中:T₁,…,T₅为一次协同过程的感知、决策与执行相关时间。
	自组织能力评估模型	用于考察集群对环境变化的响应速度	文献[87]提出的UAVS重新集结的指标体系。其中提出了UAVS集结并协调一致时间的计算指标T_c。第i个无人机的预计到达时间为 $T E T A i$ ,将整个编队的集结时间T_e与 $T E T A i$ 的差记为协调一致时间误差: E_i(t)=T_e- $T E T A i$ (t) 当n个无人机的协调一致时间误差的平方和等于δ时,t=T_c。
	智能决策质量模型	用于衡量指挥主体根据战场态势完成目标分配、战略决策和战术决策的能力	文献[88]提出的模型,其指挥决策系统能力E_DEC可以用态势感知能力、战术决策能力、指挥控制能力和系统生存能力表示: E_DEC=λ_gz·C_gz+λ₄·P_t+λ_s·C_s+λ_jc·C_jc 式中:C_gz表示态势感知能力;P_t为指挥控制时效性;C_s表示系统生存能力;C_jc为战术决策能力:λ_gz、λ_t、λ_s、λ_gc为权重系数。
集群恢复模型类	自主恢复效率模型	用于分析集群在故障时的重新配置速度	文献[87]提出的整个无人机编队的可靠度,以体现其自主恢复效率: $P = ∑ i = 1 n P i / n$ 式中:Pⁱ为第i个无人机的可靠度,需要通过第i个无人机飞行时间和平均故障间隔时间来计算;整个无人机编队共有n个无人机。

表3 可靠性参数模型与功能

Table 3 Reliability parameter model and function

模型类别	可靠性参数模型	模型功能	相关研究
集群行为模型类	协同度模型	用于评估通信效率、任务完成率等指标	文献[86]提出的UAVS的协同度模型,其中包括协同时间基本模型、协同信息量模型,以及基于多Agent的协同方法协同性能模型。该文提出的协同时间T为 T=T₁+T₂+T₃+T₄+T₅ 式中:T₁,…,T₅为一次协同过程的感知、决策与执行相关时间。
	自组织能力评估模型	用于考察集群对环境变化的响应速度	文献[87]提出的UAVS重新集结的指标体系。其中提出了UAVS集结并协调一致时间的计算指标T_c。第i个无人机的预计到达时间为 $T E T A i$ ,将整个编队的集结时间T_e与 $T E T A i$ 的差记为协调一致时间误差: E_i(t)=T_e- $T E T A i$ (t) 当n个无人机的协调一致时间误差的平方和等于δ时,t=T_c。
	智能决策质量模型	用于衡量指挥主体根据战场态势完成目标分配、战略决策和战术决策的能力	文献[88]提出的模型,其指挥决策系统能力E_DEC可以用态势感知能力、战术决策能力、指挥控制能力和系统生存能力表示: E_DEC=λ_gz·C_gz+λ₄·P_t+λ_s·C_s+λ_jc·C_jc 式中:C_gz表示态势感知能力;P_t为指挥控制时效性;C_s表示系统生存能力;C_jc为战术决策能力:λ_gz、λ_t、λ_s、λ_gc为权重系数。
集群恢复模型类	自主恢复效率模型	用于分析集群在故障时的重新配置速度	文献[87]提出的整个无人机编队的可靠度,以体现其自主恢复效率: $P = ∑ i = 1 n P i / n$ 式中:Pⁱ为第i个无人机的可靠度,需要通过第i个无人机飞行时间和平均故障间隔时间来计算;整个无人机编队共有n个无人机。

表4 可靠性维持保障技术分类

Table 4 Reliability maintenance support technology and classification

技术分类	可靠性维持保障技术
故障自主隔离技术	分布式故障定位
	智能故障检测与诊断
	自适应性故障隔离
自适应自恢复技术	动态任务重新分配
	自组织网络拓扑重构
	预测性维护与自修复
	环境适应性飞行控制
	冗余系统设计与切换

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