Reconnaissance Mission Planning Method for Air-ground Heterogeneous Unmanned Systems

doi:10.16182/j.issn1004731x.joss.23-0103

Abstract

Abstract:

Compared with the air-based homogeneous unmanned system, the motion capabilities, resource payloads, and combat scenes in the air-ground heterogeneous unmanned system increase the number of constraint conditions and significantly increase the computational complexity of the solution model. The modeling of collaborative combat missions and the efficient solution of large-scale problems are the key issues. With the time, path cost, and reconnaissance benefit as the objective functions, considering the constraints such as the endurance of unmanned platforms, a multi-objective programming model for the reconnaissance missions of an air-ground heterogeneous unmanned system is constructed. Aiming at the urban combat environments with multiple threat zones, considering the path safety and timeliness of unmanned platform missions, the improved A* algorithm path planning strategies for unmanned aerial vehicles and unmanned ground vehicles are proposed. Aiming at the problem that the optimization effect of snake optimizer (SO) is unstable and easy to fall into local optimal solutions, an improved snake optimizer (IMSO) is proposed by combining the particle swarm algorithm and the genetic algorithm. Simulation verification and comparative analysis with existing algorithms are carried out by using Python language to verify the feasibility of the model and the superiority of the algorithm. Solving 10 tasks independently under three different task loads from small to large, the average objective function values of IMSO are 100.11%, 108.99%, and 110.01% of SO, respectively. It can be seen that IMSO can jump out of local optima multiple times, and the stability and final fitness values of the algorithm are better than SO, and is more superior in solving the larger-scale problems.

Key words: unmanned combat, air-ground heterogeneity, mission planning, snake optimization, A* algorithm

CLC Number:

TP391.9

Zhang Guohui, Zhang Ya'nan, Gao Ang, Xu Aoyu. Reconnaissance Mission Planning Method for Air-ground Heterogeneous Unmanned Systems[J]. Journal of System Simulation, 2024, 36(2): 497-510.

Figures/Tables 20

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Table 1

Table 2

Unmanned platform task sequence

无人平台	任务序列
S₁	$O 2 → O 5$
S₂	$O 4$
S₃	$O 3 → O 1$

Table 2

Fig. 9

Fig. 10

Table 3

Fig. 11

Fig. 12

Fig. 13

Table 4

Unmanned platform allocation information

无人平台	类型	目标序列	航程/m
S₁	UAV	O₇	25 858
S₂	UAV	$O 8 → O 2$	28 085
S₃	UAV	$O 14 → O 13$	36 691
S₄	UAV	$O 3$	38 845
S₅	UGV	$O 6 → O 12$	56 622
S₆	UGV	$O 4 → O 5 → O 9$	63 318
S₇	UGV	$O 1 → O 11 → O 10$	62 493

Table 4

Fig. 14

Table 5

Fig. 15

References 15

1	谭威, 胡永江, 李文广, 等. 多无人机协同任务规划研究综述[J]. 微型电脑应用, 2021, 37(9): 189-192.
	Tan Wei, Hu Yongjiang, Li Wenguang, et al. A Survey of Multi-UAV Cooperative Mission Planning[J]. Microcomputer Applications, 2021, 37(9): 189-192.
2	武文亮, 周兴社, 沈博, 等. 集群机器人系统特性评价研究综述[J]. 自动化学报, 2022, 48(5): 1153-1172.
	Wu Wenliang, Zhou Xingshe, Shen Bo, et al. A Review of Swarm Robotic Systems Property Evaluation Research[J]. Acta Automatica Sinica, 2022, 48(5): 1153-1172.
3	马悦, 吴琳, 郭圣明. 作战任务分配建模及求解方法研究[J]. 系统仿真学报, 2023, 35(4): 887-898.
	Ma Yue, Wu Lin, Guo Shengming. Research on Modeling and Solution Method of Operational Tasks Assignment[J]. Journal of System Simulation, 2023, 35(4): 887-898.
4	赵越. 多无人机协同任务分配和航路规划技术研究[D]. 成都: 电子科技大学, 2022.
	Zhao Yue. Research on Multi-UAV Cooperative Task Allocation and Route Planning Technology[D]. Chengdu: University of Electronic Science and Technology of China, 2022.
5	Deng Qibo, Yu Jianqiao, Wang Ningfei. Cooperative Task Assignment of Multiple Heterogeneous Unmanned Aerial Vehicles Using a Modified Genetic Algorithm with Multi-type Genes[J]. Chinese Journal of Aeronautics, 2013, 26(5): 1238-1250.
6	马硕, 马亚平. 异构无人系统群协同作战任务规划方法[J]. 指挥控制与仿真, 2019, 41(2): 24-30.
	Ma Shuo, Ma Yaping. Heterogeneous Cooperative Unmanned System Mission Planning Method[J]. Command Control & Simulation, 2019, 41(2): 24-30.
7	Ye Fang, Chen Jie, Tian Yuan, et al. Cooperative Multiple Task Assignment of Heterogeneous UAVs Using a Modified Genetic Algorithm with Multi-type-gene Chromosome Encoding Strategy[J]. Journal of Intelligent & Robotic Systems, 2020, 100(2): 615-627.
8	范博洋, 赵高鹏, 薄煜明, 等. 多目标空地异构无人系统协同任务分配方法[J]. 兵工学报, 2023, 44(6): 1564-1575.
	Fan Boyang, Zhao Gaopeng, Bo Yuming, et al. Collaborative Task Allocation Method for Multi-target Air-ground Heterogeneous Unmanned System[J]. Acta Armamentarii, 2023, 44(6): 1564-1575.
9	Wang Jianfeng, Jia Gaowei, Lin Juncan, et al. Cooperative Task Allocation for Heterogeneous multi-UAV Using Multi-objective Optimization Algorithm[J]. Journal of Central South University, 2020, 27(2): 432-448.
10	Tan Guoge, Zhuang Jiayuan, Zou Jin, et al. Multi-type Task Allocation for Multiple Heterogeneous Unmanned Surface Vehicles (USVs) Based on the Self-organizing Map[J]. Applied Ocean Research, 2022, 126: 103262.
11	Chen Lizhi, Liu Weili, Zhong Jinghui. An Efficient Multi-objective Ant Colony Optimization for Task Allocation of Heterogeneous Unmanned Aerial Vehicles[J]. Journal of Computational Science, 2022, 58: 101545.
12	Kool Wouter, van Hoof Herke, Welling Max. Attention, Learn to Solve Routing Problems![EB/OL]. (2019-02-07) [2022-04-04]. .
13	周灵叶. 基于任务的无人机协同路径规划方法设计与实现[D]. 北京: 北京邮电大学, 2021.
	Zhou Lingye. Sesign and Implementation of Task-based UAV Cooperative Path Planning Method[D]. Beijing: Beijing University of Posts and Telecommunications, 2021.
14	朱云虹, 袁一. 基于改进A*算法的最优路径搜索[J]. 计算机技术与发展, 2018, 28(4): 55-59.
	Zhu Yunhong, Yuan Yi. Optimal Path Search Based on Improved A* Algorithm[J]. Computer Technology and Development, 2018, 28(4): 55-59.
15	Hashim Fatma A, Hussien Abdelazim G. Snake Optimizer: A Novel Meta-heuristic Optimization Algorithm[J]. Knowledge-based Systems, 2022, 242: 108320.

目标	X	[X]	{X}	平台	收益
O₁	3.7	3	0.7	S₃	40
O₂	1.2	1	0.2	S₁	70
O₃	3.5	3	0.5	S₃	30
O₄	2.3	2	0.3	S₂	60
O₅	1.5	1	0.5	S₁	10

目标	经度	纬度	对空威胁系数	对空威胁距离/m	对地威胁系数	对地威胁距离/m	价值
O₁	114.331 3	30.533 1	0.8	2 000	0.2	1 000	100
O₂	114.243 4	30.551 1	0.2	1 500	0.3	500	60
O₃	114.214 6	30.609 0	0.1	1 500	0.4	1 000	40
O₄	114.307 3	30.654 5	0.5	3 000	0.2	800	80
O₅	114.360 8	30.590 1	0.3	3 000	0.1	600	30
O₆	114.414 4	30.543 4	0.9	2 000	0.2	400	60
O₇	114.237 0	30.521 9	0.3	2 700	0.7	500	70
O₈	114.290 8	30.502 0	0.1	1 000	0.8	400	40
O₉	114.281 2	30.606 7	0.8	1 200	0.2	900	50
O₁₀	114.191 7	30.578 4	0.4	2 700	0.1	500	30
O₁₁	114.166 5	30.620 8	0.6	1 800	0.4	1 400	40
O₁₂	114.393 4	30.643 2	0.8	2 700	0.6	600	30
O₁₃	114.218 2	30.549 4	0.2	1 200	0.7	600	50
O₁₄	114.256 4	30.585 4	0.2	2 000	0.7	1 000	100

参数	N_U=3, N_G=3, N_O=9			N_U=4, N_G=3, N_O=14			N_U=5, N_G=4, N_O=14
参数	IMSO	SO	PSO	IMSO	SO	PSO	IMSO	SO	PSO
最优值	314.13	314.13	314.13	429.22	411.87	393.51	440.99	422.95	412.74
平均值	314.04	313.68	304.17	416.15	381.81	354.07	430.94	391.70	368.67
最差值	313.71	311.90	279.37	400.22	352.83	312.74	418.89	349.29	286.13
标准差	0.140 6	0.661 2	14.655 1	9.906 3	19.826 4	28.352 8	5.929 6	21.478 2	38.464 7

[1]	Li Wenjing, Luo Yanlin, Wang Yuhui, Zhu Li. Virtual Navigation Path Planning Based on Octree Potential Field for Endonasal Endoscope [J]. Journal of System Simulation, 2023, 35(9): 2054-2063.
[2]	Teng Li, Peipei Ding, Jinfang Liu. Multi-Stage Multi-AGV Path Planning with Walk under Shelves for Robotic Mobile Fulfillment Systems [J]. Journal of System Simulation, 2022, 34(7): 1512-1523.
[3]	Dun Meng, Zhuo Hu, Huajun Zhang. Simulation of Multi-layer Ship Evacuation System Based on Improved A* Algorithm [J]. Journal of System Simulation, 2022, 34(6): 1375-1382.
[4]	Guochen Wang. Simulation and Effectiveness Evaluation System for Joint Delivery Mission Planning of Airlift Fleets [J]. Journal of System Simulation, 2022, 34(11): 2497-2506.
[5]	Li Qi, Wang Wei. Path Designing of Multi-omnidirectional Wheel Collaborative Sorting Platform [J]. Journal of System Simulation, 2021, 33(3): 698-709.
[6]	Shi Min, Wei Yukun, Jin Xiangchen, Wang Suqin, Mao Tianlu. Virtual Scene Roaming Method Supporting Multi-layer Path Planning [J]. Journal of System Simulation, 2019, 31(7): 1358-1366.
[7]	Zhao Ping, Lei Xinyu, Chen Bozhi, Yang Jiaoyun. TI-A* Based Multi-robot Dynamic Planning and Coordination Method [J]. Journal of System Simulation, 2019, 31(5): 925-935.
[8]	Wu Huaxing, Huang Wei, Kang Fengju, Mao Hongbao. Research on Autonomous Intercept Method for UCAV Based on Lead Attack Aiming [J]. Journal of System Simulation, 2016, 28(9): 2246-2253.