Generation of Multi-UAV Four-dimensional Cooperative Attack Route Based on T/S-SAS

doi:10.12382/bgxb.2022.0211

Abstract

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

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.

Figures/Tables 22

References 24

[1]	尤嵩菀, 王文龙. 美俄军事智能化发展及启示[J]. 海军工程大学学报(综合版), 2020, 17(2): 64-69.
	YOU S Y, WANG W L. Research on military intellectualization of United States and Russia and its enlightenment[J]. Journal of Naval University of Engineering(Comprehensive Edition), 2020, 17(2): 64-69. (in Chinese)
[2]	XIE R L, MENG Z J, ZHOU Y M, et al. Heuristic Q-learning based on experience replay for three-dimensional path planning of the unmanned aerial vehicle[J]. Science Progress, 2020, 103(1):1-18.
[3]	孙健, 井立, 刘朝君. 突发威胁下的无人机航迹规划算法[J]. 飞行力学, 2018, 36(3): 52-55.
	SUN J, JING L, LIU Z J. Route planning algorithm of UAV with unexpected threat[J]. Flight Dynamics, 2018, 36(3): 52-55. (in Chinese)
[4]	ALBERT A, LEIRA F S, IMSLAND L. UAV path planning using MILP with experiments[J]. Modeling, Identification and Control, 2017, 38(1): 21-32. doi: 10.4173/mic.2017.1.3 URL
[5]	代进进, 李相民, 薄宁, 等. 基于模型预测控制的无人机避障路径规划方法[J]. 火力与指挥控制, 2020, 45(1): 114-119.
	DAI J J, LI X M, BO N, et al. Study on UAV obstacle avoidance path planning based on model predictive control[J]. Fire Control & Command Control, 2020, 45(1): 114-119. (in Chinese)
[6]	王文彬, 秦小林, 张力戈, 等. 基于滚动时域的无人机动态航迹规划[J]. 智能系统学报, 2018, 13(4): 524-533.
	WANG W B, QIN X L, ZHANG L G, et al. Dynamic UAV trajectory planning based on receding horizon[J]. CAAI Transactions on Intelligent Systems, 2018, 13(4): 524-533. (in Chinese)
[7]	LIU Q, SHI L, SUN L L, et al. Path planning for UAV-mounted mobile edge computing with deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2020, 69(5): 5723-5728. doi: 10.1109/TVT.25 URL
[8]	BLASI L, D AMATO E, MATTEI M, et al. Path planning and real-time collision avoidance based on the essential visibility graph[J]. Applied Sciences, 2020, 10(16): 5613. doi: 10.3390/app10165613 URL
[9]	朱杰, 鲁艺, 张辉明. 突发威胁情况下的无人机航迹重规划[J]. 计算机工程与应用, 2018, 54(8): 255-259. doi: 10.3778/j.issn.1002-8331.1709-0345
	ZHU J, LU Y, ZHANG H M. Path replanning for UAV in emergent threats[J]. Computer Engineering and Application, 2018, 54(8): 255-259. (in Chinese)
[10]	吴云华, 牛康, 李磊, 等. 基于3D-APF和约束动力学的无人机编队飞行控制[J]. 系统工程与电子技术, 2018, 40(5): 1104-1108.
	WU Y H, NIU K, LI L, et al. Formation flight control of UAV based on 3D-APF and constraint dynamics[J]. Systems Engineering & Electronics, 2018, 40(5):1104-1108. (in Chinese)
[11]	陈天德, 黄炎焱, 沈炜. 基于虚拟障碍物法的无震荡航路规划[J]. 兵工学报, 2019, 40(3): 651-658. doi: 10.3969/j.issn.1000-1093.2019.03.025
	CHEN T D, HUANG Y Y, SHEN W. Non-oscillation path planning based on virtual obstacle method[J]. Acta Armamentarii, 2019, 40(3): 651-658. (in Chinese) doi: 10.3969/j.issn.1000-1093.2019.03.025
[12]	潘无为, 姜大鹏, 庞永杰, 等. 人工势场和虚拟结构相结合的多水下机器人编队控制[J]. 兵工学报, 2017, 38(2): 326-334. doi: 10.3969/j.issn.1000-1093.2017.02.017
	PAN W W, JIANG D P, PANG Y J, et al. A multi-AUV formation algorithm combining artificial potential field and virtual structure[J]. Acta Armamentarii, 2017, 38(2): 326-334. (in Chinese)
[13]	梁宵, 王宏伦, 李大伟, 等. 基于流水避石原理的无人机三维航路规划方法[J]. 航空学报, 2013, 34(7): 1670-1681.
	LIANG X, WANG H L, LI D W, et al. Three dimensional path planning for unmanned aerial vehicles based on principles of stream avoiding obstacles[J]. Acta Aeronautica et Astronautica Sinica, 2013, 34(7): 1670-1681. (in Chinese)
[14]	王宏伦, 吴健发, 姚鹏. 基于扰动流体动态系统的无人机三维航路规划:方法与应用[J]. 无人系统技术, 2018, 1(1): 72-82.
	WANG H L, WU J F, YAO P. UAV three-dimensional path planning based on interfered fluid dynamical system: methodology and application[J]. Unmanned Systems Technology, 2018, 1(1): 72-82. (in Chinese)
[15]	FAROOQ M U, ZHEN Z, EJAZ M, et al. Quadrotor UAVs flying formation reconfiguration with collision avoidance using probabilistic roadmap algorithm[C]//Proceedings of International Conference on Computer Systems, Electronics and Control. Dalian,Liaoning,China:ICCSEC,2017, 2017:866-870.
[16]	高升, 艾剑良, 王之豪. 混合种群RRT无人机航迹规划方法[J]. 系统工程与电子技术, 2020, 42(1): 101-107. doi: 10.3969/j.issn.1001-506X.2020.01.14
	GAO S, AI J L, WANG Z H. Mixed population RRT algorithm for UAV path planning[J]. Systems Engineering & Electronics, 2020, 42(1): 101-107. (in Chinese)
[17]	王生印, 龙腾, 王祝, 等. 基于即时修复式稀疏A^*算法的动态航迹规划[J]. 系统工程与电子技术, 2018, 40(12): 2714-2721.
	WANG S Y, LONG T, WANG Z, et al. Dynamic path planning using anytime repairing sparse A^* algorithm[J]. Systems Engineering & Electronics, 2018, 40(12): 2714-2721. (in Chinese)
[18]	张韬, 项祺, 郑婉文, 等. 基于改进A^*算法的路径规划在海战兵棋推演中的应用[J]. 兵工学报, 2022, 43(4): 960-968. doi: 10.12382/bgxb.2021.0209
	ZHANG T, XIANG Q, ZHENG W W, et al. Application of path planning based on improved A^* algorithm in war gaming of naval warfare[J]. Acta Armamentarii, 2022, 43(4): 960-968. (in Chinese)
[19]	HUANG Y, CHEN J G, WANG H L, et al. A method of 3D path planning for solar-powered UAV with fixed target and solar tracking[J]. Aerospace Science and Technology, 2019, 92(9): 831-838. doi: 10.1016/j.ast.2019.06.027 URL
[20]	李文博, 秦小林, 罗刚. 基于无障碍凸区域的无人机在线航迹规划[J]. 系统科学与数学, 2021, 41(6): 1493-1506.
	LI W B, QIN X L, LUO G. Online trajectory planning of UAV based on convex obstacle-free area[J]. Journal of Systems Science and Mathematical Sciences, 2021, 41(6): 1493-1506. (in Chinese) doi: 10.12341/jssms20355
[21]	WU Y, WU S, HU X. Multi-constrained cooperative path planning of multiple drones for persistent surveillance in urban environments[J]. Complex & Intelligent Systems, 2021, 7(3): 1633-1647.
[22]	ZHANG Z, WU J, DAI J Y, et al. Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2022, 236(1): 72-81. doi: 10.1177/09544100211007381 URL
[23]	ZHANG Z, WU J, DAI J Y, et al. A novel real-time penetration path planning algorithm for stealth UAV in 3D complex dynamic environment[J]. IEEE Access, 2020, 8: 122757-122771. doi: 10.1109/Access.6287639 URL
[24]	GAO F X, DING J F, LIU Z C, et al. An improved A^* algorithm for UAV path planning[C]//Proceedings of 2021 IEEE International Conference on Electrical Engineering and Mechatronics Technology.Qingdao,Shandong, China:ICEEMT, 2021: 772-776.

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

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

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”

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”

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)