基于T/S-SAS的多无人机四维协同攻击航线生成

doi:10.12382/bgxb.2022.0211

摘要/Abstract

摘要：

针对多无人机攻击目标时间协同以及飞行航线空间协同问题,提出基于时间与空间双协同稀疏A^*搜索(T/S-SAS)算法的多无人机四维协同攻击航线生成算法。改进飞行扩展节点模型,设计基于并发扩展的算法结构,建立时间协同代价计算模型与多机防碰撞约束模型,并开展仿真研究。研究结果表明:所提算法能够增强无人机攻击航线的规划效率、减少不同无人机抵达目标的协同攻击时间极差、解决不同无人机之间的空间防碰撞问题;该算法使多无人机协同攻击航线满足时间/空间约束,提升了多无人机协同时间打击性能及飞行路线空间协同性能,提高无人机协同作战效率与作战能力。

关键词: 多无人机, 双约束, 四维协同, 攻击航线生成

Abstract:

To address the problem of target attack time coordination and route space coordination of multiple UAVs, a multi-UAV four-dimensional cooperative attack route generation algorithm based on the T/S-SAS (Time/Space Dual Cooperative Sparse A^* Search) algorithm is proposed. The flight expansion node model is improved, an algorithm structure based on concurrent expansion is designed, and a cost calculation model for time coordination as well as a multi-UAV anti-collision constraint model are established. Simulations are performed. The results show that the proposed algorithm can enhance the planning efficiency of UAV attack route, shorten the range in coordination time for different drones to reach the target, and solve the anti-collision problem for different UAVs. The multi-UAV cooperative attack route can meet the time/space constraints, allowing the multi-UAV strike performance at cooperative time and the flight route space coordination performance to be improved, and the operational efficiency and capability of multi-UAV cooperative combat to be increased.

Key words: unmanned aerial vehicles, dual constraints, four-dimensional coordination, attack route generation

张堃, 刘泽坤, 华帅, 张振冲, 李珂, 于竞婷. 基于T/S-SAS的多无人机四维协同攻击航线生成[J]. 兵工学报, 2023, 44(6): 1576-1587.

ZHANG Kun, LIU Zekun, HUA Shuai, ZHANG Zhenchong, LI Ke, YU Jingting. Generation of Multi-UAV Four-dimensional Cooperative Attack Route Based on T/S-SAS[J]. Acta Armamentarii, 2023, 44(6): 1576-1587.

图/表 22

图1 不利气候威胁区

Fig.1 Adverse climate threat zone

图2 雷达威胁区

Fig.2 Radar threat zone

图3 防空导弹威胁区

Fig.3 Air defense missile threat zone

图4 SAS算法水平面内节点扩展方式

Fig.4 Horizontal plane node expansion mode of SAS algorithm

图5 SAS算法竖直面内节点扩展方式

Fig.5 Vertical plane node expansion mode of SAS algorithm

图6 飞行扩展节点改进模型

Fig.6 Improved model of flight node expansion

图7 并发扩展算法规划过程

Fig.7 Planning process of concurrent expansion algorithm

图8 防碰撞约束实现

Fig.8 Realization of anti-collision constraints

图9 基于T/S-SAS算法的多无人机四维协同攻击航线生成算法流程

Fig.9 Algorithm flow of the generation of multi-UAV four-dimensional cooperative attack route based on T/S-SAS algorithm

表1 环境威胁参数

Table 1 Environment threat parameters

编号	威胁种类	威胁二维中心坐标/km	最大威胁高度/km	最大威胁半径/km
1	山峰威胁	(45.0, 72.0)	3.0	15.0
2	山峰威胁	(67.0, 40.0)	1.5	10.0
3	山峰威胁	(31.0, 28.0)	2.0	10.0
4	山峰威胁	(88.0, 26.0)	2.5	12.0
5	雷达威胁	(60.0, 52.0)	12.0	12.0
6	雷达威胁	(81.0, 55.0)	8.0	8.0
7	雷达威胁	(57.0, 86.0)	10.0	10.0
8	雷达威胁	(23.0, 68.0)	9.6	9.6
9	雷达威胁	(20.0, 38.0)	10.0	10.0
10	雷达威胁	(44.0, 19.0)	8.0	8.0
11	雷达威胁	(69.0, 24.0)	8.0	8.0
12	防空导弹威胁	(94.0, 27.0)	7.0	10.0
13	防空导弹威胁	(92.0, 46.0)	7.0	10.0
14	防空导弹威胁	(78.0, 75.0)	7.0	8.0
15	防空导弹威胁	(36.0, 80.0)	7.0	9.0
16	防空导弹威胁	(25.0, 16.0)	7.0	9.0
17	防空导弹威胁	(92.0, 85.0)	7.0	7.0
18	气候威胁	(38.0, 38.0)	30.0	6.0
19	气候威胁	(71.0, 38.0)	30.0	8.0
20	气候威胁	(58.0, 69.0)	30.0	6.0
21	气候威胁	(20.0, 85.0)	30.0	6.0
22	气候威胁	(40.0, 66.0)	30.0	6.0
23	气候威胁	(90.0, 68.0)	30.0	6.0

表2 相关算法名称及算法改进描述

Table 2 Related algorithm name and algorithm improvement description

序号	算法	改进
1	传统SAS算法
2	I-SAS算法	添加“飞行扩展节点改进模型”
3	IT-SAS算法	添加“飞行扩展节点改进模型”+“并发扩展结构”+“时间协同代价计算模型”
4	IS-SAS算法	添加“飞行扩展节点改进模型”+“并发扩展结构”+“多机防碰撞约束模型”
5	T/S-SAS算法	添加“飞行扩展节点改进模型”+“并发扩展结构”+“时间协同代价计算模型”+“多机防碰撞约束模型”

表3 算法规划效率测试的起点、目标点参数

Table 3 Starting point and target point parameters of algorithm planning efficiency test

无人机编号	起始点坐标/km	设定目标点坐标/km
U₁	(20.0, 0, 1.0)	(20.0, 100.0, 1.0)
U₂	(40.0, 0, 1.0)	(40.0, 100.0, 1.0)
U₃	(60.0, 0, 1.0)	(60.0, 100.0, 1.0)
U₄	(80.0, 0, 1.0)	(80.0, 100.0, 1.0)

图10 传统SAS算法与I-SAS算法规划结果对比

Fig.10 Planning result comparison of traditional SAS algorithm and I-SAS algorithm

图11 不同扩展节点数量下传统SAS算法与I-SAS算法规划结果数据对比

Fig.11 Data comparison between traditional SAS algorithm and I-SAS algorithm under different number of expanded nodes

表4 时间协同测试的起点、目标点参数

Table 4 Starting point and target point parameters of time coordination test

无人机编号	起始点坐标/km	目标编号	设定目标点坐标/km
U₁	(20.0, 0, 1.0)	T₁	(20.0, 100.0, 1.0)
U₂	(40.0, 0, 1.0)	T₂	(40.0, 100.0, 1.0)
U₃	(60.0, 0, 1.0)	T₃	(60.0, 100.0, 1.0)
U₄	(80.0, 0, 1.0)	T₄	(80.0, 100.0, 1.0)

图12 I-SAS算法与IT-SAS算法规划结果对比

Fig.12 Planning result comparison of I-SAS algorithm and IT-SAS algorithm

图13 不同目标位置设定下I-SAS算法与IT-SAS规划结果数据对比

Fig.13 Data comparison between I-SAS algorithm and IT-SAS algorithm under different target location settings

表5 空间协同测试的起点、目标点参数

Table 5 Starting point and target point parameters of space coordination test

无人机编号	起始点坐标/km	设定目标点坐标/km
U₁	(0.5, 0, 1.0)	(100.0, 100.0, 1.0)
U₂	(1.0, 0, 1.0)	(100.0, 100.0, 1.0)
U₃	(0, 0.5, 1.0)	(100.0, 100.0, 1.0)
U₄	(0, 1.0, 1.0)	(100.0, 100.0, 1.0)

图14 I-SAS算法与IS-SAS算法规划结果对比

Fig.14 Planning result comparison of I-SAS algorithm and IS-SAS algorithm

图15 不同安全距离下I-SAS算法与IS-SAS算法规划结果数据对比

Fig.15 Data comparison between I-SAS algorithm and IS-SAS algorithm under different safety distances

表6 时间/空间协同测试的起点、目标点参数

Table 6 Starting point and target point parameters of time/space coordination test

无人机编号	起始点坐标/km	设定目标点坐标/km
U₁	(1.0, 0, 1.0)	(100.0, 100.0, 1.0)
U₂	(2.0, 0, 1.0)	(100.0, 100.0, 1.0)
U₃	(0, 1.0, 1.0)	(100.0, 100.0, 1.0)
U₄	(0, 2.0, 1.0)	(100.0, 100.0, 1.0)

图16 不同算法规划结果数据对比

Fig.16 Planning result comparison of different algorithms

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