Acta Aeronautica et Astronautica Sinica ›› 2024, Vol. 45 ›› Issue (15): 29653-029653.doi: 10.7527/S1000-6893.2023.29653
• Reviews • Previous Articles
Honghong ZHANG1,2(), Wenhua LI1, Jiayi ZHENG1, Hongbin LIU1, Peng ZHANG1, Peng GAO1, Xusheng GAN2,3
Received:
2023-09-27
Revised:
2023-11-03
Accepted:
2023-12-04
Online:
2023-12-08
Published:
2023-12-07
Contact:
Honghong ZHANG
E-mail:anhuifuyangzhh@sina.com
Supported by:
CLC Number:
Honghong ZHANG, Wenhua LI, Jiayi ZHENG, Hongbin LIU, Peng ZHANG, Peng GAO, Xusheng GAN. Manned/unmanned aerial vehicle cooperative combat system: Concepts, technologies, and challenges[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(15): 29653-029653.
Table 1
Coordination level of MAV/UAV combat
等级 | 物理域 | 信息域 | 认知域 | 社会域 |
---|---|---|---|---|
第1级(Ⅰ) | 协同系统具有模块独立特征,协同毁伤效果、任务效益能够适应单一环境,无人平台为完全机构化的控制方案和策略 | 有人平台与无人平台通过地面站发射和接收数据信息 | 协同系统态势质量、作战意图能够适应单一环境 | 协同系统信息交互、认知共享、协同决策能够适应单一环境 |
第2级(Ⅱ) | 协同系统具有项目体系特征,协同毁伤效果、任务效益能够适应局部任务环境,无人平台能够适应对象和环境的不确定性,具有故障实时诊断、隔离、和根据故障情况进行系统重构能力 | 有人平台直接接收无人平台数据信息 | 协同系统态势质量、作战意图能够适应局部任务环境 | 协同系统信息交互、认知共享、协同决策能够适应局部任务环境 |
第3级(Ⅲ) | 协同系统具有工程体系特征,协同毁伤效果、任务效益能够适应少领域任务环境,无人平台能够根据变化的任务和态势决策和任务重规划的能力 | 有人平台控制无人平台任务载荷 | 协同系统态势质量、作战意图能够适应少领域任务环境 | 协同系统信息交互、认知共享、协同决策能够适应少领域任务环境 |
第4级(Ⅳ) | 协同系统具有联合体系特征,协同毁伤效果、任务效益能够适应多领域任务环境,无人平台具有与其它单体或系统进行交互、协同的能力 | 有人平台控制无人平台任务飞行 | 协同系统态势质量、作战意图能够适应多领域任务环境 | 协同系统信息交互、认知共享、协同决策能够适应多领域任务环境 |
第5级(Ⅴ) | 协同系统具有融合体系特征,协同毁伤效果、任务效益能够适应任何任务环境,无人平台能够自学习,具有集群自组织协调的能力 | 有人平台控制无人平台发射与回收 | 协同系统态势质量、作战意图能够适应任何任务环境 | 协同系统信息交互、认知共享、协同决策能够适应任何任务环境 |
Table 2
Practical information of the MAV/UAV cooperative combat test evaluation project[21]
国家 | 起始年份 | 项目名称 | 验证平台 | 主要研究内容梳理 |
---|---|---|---|---|
美国 | 1993 | Bird Dog | 开发有人/无人协同作战概念 | |
美国 | 1996 | 机载有人/ 无人系统技术(AMUST) | AH-64D/“猎人”战术无人机 | AMUST:基于仿真环境对有人/无人系统进行功能需求定义、关键技术分析和试验验证 AMUST-D:在编组基础上增加宽带视频和数据传输功能,降低有人/无人编组风险 AMUST-D 6.3:重点验证指挥控制飞机、直升机与无人机之间的互连互通,并开发和综合各种直接视频/数据接收、直接载荷控制以及直接飞行控制等相关技术 |
美国 | 1997 | 战术控制系统(TCS) | P-3C、F/A-18、 AV-8B/RQ-8A、MQ-4C、 X-47B、UTAP-22 | 重点在海军协同作战项目的试验验证,实现对无人机的5级控制 |
美国 | 2003 | Scathe Falcon | C-130/捕食者 | 实现空基指挥控制功能仿真与飞行实验 |
美国 | 2003 | SEC | F-15E/T-33 | 通过语音控制执行协同目标信息收集 |
美国 | 2004 | 联合无人空战系统(JUCAS) | F-15E、X-45A /T-33 | 验证协同通信与飞行能力 |
美国 | 2004 | MCAP | 阿帕奇/影子-200 | 设计与验证先进航电系统 |
美国 | 2006 | 猎人远距杀手小组(HSKT) | AH-64D /RQ-5B | 验证有人武装直升机/无人侦察机/战斗机编队作战能力 |
美国 | 2008 | 无人机视频智能共享系统(VUIT-2) | 阿帕奇Ⅱ | 验证无人机视频数据传输功能 |
美国 | 2011 | MUSIC | 阿帕奇、基奥瓦/灰鹰、影子 | 验证多机协同控制能力 |
英国 | 2012 | 未来空军进攻性系统 | 改装的“狂风”战斗轰炸机/BAC-111 | 验证有人机、无人机及空射巡航导弹组成的混合编队体系作战能力 |
美国 | 2013 | 无人机系统战术通用数据链组件(UTA) | AH-64 III / MQ-1 | 验证协同侦察功能,利用双向高带宽数据链控制无人机的飞行路线及任务负载,实时接收高清战场图像 |
美国 | 2015 | 忠诚僚机 | F-35/改装F-16 | 验证对作战环境动态自适应的任务计划能力、传感器融合技术,战斗辨别,优先排序及分发功能等、通信及对环境限制的适应性、飞行控制、针对不同作战环境、负载、通信情况的自适应导航界面和负载管理技术、任务结束后的人-机任务报告技术 |
美国/澳大利亚 | 2019 | 空中力量编队系统(ATS) | MQ-28A | 开放式架构根据任务需求快速更换作战载荷,承担探测感知、电子战等多元化任务 |
英国 | 2019 | 蚊子(Mosquito) | F-35、“台风”和“暴风雪”/蜂群无人机 | 验证有人-蜂群无人协同作战能力 |
美国 | 2020 | 天空博格人(Skyborg) | F-22、F-35/UTAP-22、MQ-20、XQ-58A | 验证有人-无人自主协同,对空中或地面的指令进行无缺陷响应 |
美国 | 2021 | 无人综合作战问题-21(UxS IBP21) | E-2C、EA-18G、MH-60/MQ-9 | 重点评估有人/无人系统在情报、监视和侦察、瞄准和导弹打击、空中有人/无人组合3个主要领域的能力 |
美国 | 2022 | 海军陆战队与海军协同训练 | UH-1Y、AH-1Z/MQ-8C | 验证海军有人/无人编队在未来沿海环境中协同作战能力 |
美国 | 2023 | 海军陆战队新一代卫星通信系统 | UH-1Y通用直升机、MQ-25“黄貂鱼”无人空中加油机 | 重点对有人/无人系统新一代卫星通信系统的数据传输测试飞行 |
Table 3
Comparation of MAV/UAV collaborative task assignment techniques
算法分类 | 优点 | 缺点 | 代表方法 | |
---|---|---|---|---|
集中式算法 | 最优化算法 | 全局性好、求解质量高 | 无法准确描述复杂环境下的大规模任务分配问题不能模拟出战场环境的随机性和动态性 | 穷举搜索法、整数规划法、图论法、动态规划、分支定界等 |
智能启发式算法 | 复杂度低、易于实现 | 鲁棒性差,求解结果不稳定,具有随机性 未考虑分配问题中复杂耦合约束可扩展性差 | 狼群算法、蚁群算法、遗传算法等 | |
分布式算法 | 时效性好、灵活性强、鲁棒性好 | 目前无人平台智能化程度不高,计算能力差,分布式方法全局性较差求解质量不高 | 合同网协议、分布式拍卖等 | |
混合式算法 | 兼具集中式与 分布式优势 | 设计与应用难度较大对系统智能化水平与数据链可靠性要求高 | 集成集中式与分布式方法 |
Table 4
Route/trajectory planning techniques for MAV/UAV systems
类型 | 算法 | 优点 | 缺点 | 规划类型 | |
---|---|---|---|---|---|
优化类 | 数学优化法 | 最优控制、伪谱法、凸优化、混合整数线性规划、动态规划等 | 准确度高 | 模型提取困难 | 航线/轨迹 |
智能优化类 | 蚁群算法、遗传算法、粒子群算法等 | 描述简单,易实现 | 计算量大,结果具有随机性,耗时长 | 航线/轨迹 | |
图搜索算法 | Dijkstra 算法、Voronoi 图、RRT、PRM、Dubins 曲线、通视图法等 | 快速规划 | 实用性不强 | 航线 | |
势场与导航函数类 | 人工势场法、速度障碍法 | 计算量小、实时性好,结构简单 | 存在局部最优以及陷入死锁 | 航线 | |
学习类 | 神经网络、深度学习、强化学习、深度强化学习 | 鲁棒性高、实时性强 | 需要大量先验数据 | 航线/轨迹 |
Table 5
Summary of evaluation techniques for MAV/UAV cooperative combat effectiveness
评估类型 | 评估方法 | 数据 | 特点 | 适用场景 |
---|---|---|---|---|
经验驱动方法 | 多属性决策方法 | 定性、定量 | 构建多层次结构模型,定性与定量分析 | 指标权重确定 |
专家经验法 | 定性、定量 | 依靠专业经验,主观性强 | 复杂领域评估 | |
D-S证据理论 | 定性、定量 | 构建联合概率推理过程 | 多类型的不确定性信息 | |
模型驱动方法 | ADC方法 | 定量 | 分析问题全面、思路清晰 | 特定场景下的评估 |
指数法 | 定量 | 考虑性能参数构建函数关系,具有可比性 | 指标可用数值衡量 | |
数据包络分析法 | 定量 | 运用线性规划进行相对有效性评价 | 多输入多输出的评估 | |
灰色理论 | 定性、定量 | 方法简单,准确度较高 | 信息不完备、不全面、不充分的情况 | |
云模型 | 定性 | 描述事物的随机不确定性和认知不确定性 | 评估标准具有认知模糊性 | |
数据驱动方法 | 神经网络 | 定量 | 能够自主学习与调整,构建非线性关系 | 已知大量输入输出数据 |
支持向量机 | 定量 | 具有完备数学理论基础 | 小样本情况下 | |
仿真驱动方法 | 贝叶斯理论 | 定性、定量 | 有强大的动态推理能力 | 处理多源不确定性信息 |
蒙特卡洛 | 定量 | 随机实验来模拟不确定性因素对结果的影响 | 指标不确定性强 | |
系统动力学 | 定量 | 考虑系统内部组成要素互为因果的反馈特点 | 强涌现性复杂场景 |
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