多约束条件下战斗机三维路径规划问题

doi:10.12305/j.issn.1001-506X.2024.12.30

摘要/Abstract

摘要：

如何在现代战场环境中，为战斗机进行高效且安全的路径规划，同时考虑载机威胁、油耗、任务时限、转弯半径等多重限制因素，以提高作战效能。针对这些多方面需求，提出一种多约束条件下的战斗机路径规划方法，即将三维地形、威胁源、能耗、任务用时等限制条件综合考虑，构建改进粒子群优化算法中的聚合适应度函数，并使用极限曲率矩阵评估路径可飞性。此外，可以根据任务需求确定各约束条件对结果的权重。实验证明，该方法能够快速有效地生成符合要求的最优路径，并具备实际应用价值。

关键词: 路径规划, 多约束条件, 粒子群优化算法, 聚合适应度函数, 威胁源, 极限曲率矩阵

Abstract:

In order to carry out efficient and safe path planning for fighter aircraft, multiple constraints such as carrier threat, fuel consumption, mission time frame, and turning radius, are considered simultaneously to improve combat effectiveness. In order to meet these requirements, a mutiple constraints path planning method is proposed for fighter aircraft, i.e., considering the constraints of three-dimensional terrain, threat sources, energy consumption, mission time, etc., constructing the aggregated fitness function in the improved particle swarm optimization algorithm, and evaluating the fly ability of the paths by using the limit curvature matrix. In addition, the weight of each constraint on the result can be determined according to the task requirements. Experiments prove that the method can quickly and effectively generate the optimal path that meets the requirements and has practical application value.

Key words: path planning, multiple constraint, particle swarm optimization (PSO) algorithm, aggregated fitness function, threat source, limit curvature matrice

中图分类号:

杨平, 肖兵, 陈新, 唐璐琪. 多约束条件下战斗机三维路径规划问题[J]. 系统工程与电子技术, 2024, 46(12): 4213-4221.

Ping YANG, Bing XIAO, Xin CHEN, Luqi TANG. 3D path planning problem for fighter aircraft with multiple constraints[J]. Systems Engineering and Electronics, 2024, 46(12): 4213-4221.

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仿真参数	初值
起点坐标	(0, 0, 5)
终点坐标	(100, 100, 7)
惯性权重ω	1.2
加速因子c₁, c₂	2.5, 0.5
地图范围/km	100×100×30
山峰数量/座	10
防空导弹/部	3
防御型雷达数量/部	3~5
雷达探测半径/km	10
防空导弹射程/km	10
飞机燃油量/kg	3 160
种群数量/个	500
迭代次数/次	100
实验次数/次	100

序号	最优适应度均值${\bar G}$_Best	约束条件权重	迭代收敛次数均值	任务时间均值/min	最优任务速度/Ma	任务油耗均值/kg	曲率约束
1	148.05	ω₁=0.8, ω₂=0.1, ω₃=0.1	25	11.3	0.6	325	满足
2	147.33	ω₁=0.1, ω₂=0.8, ω₃=0.1	30	5.8	1.3	650	不满足
3	148.60	ω₁=0.1, ω₂=0.8, ω₃=0.1	30	6.0	1.2	635	满足
4	149.23	ω₁=0.1, ω₂=0.1, ω₃=0.8	52	10.1	0.75	420	满足
5	145.81	ω₁=0.4, ω₂=0.2, ω₃=0.4	32	9.93	0.8	425	满足
6	143.12	ω₁=0.2, ω₂=0.4, ω₃=0.4	38	7.86	1.0	540	满足
7	145.05	ω₁=0.4, ω₂=0.4, ω₃=0.2	30	8.38	0.9	405	满足
8	142.13	ω₁=0.2, ω₂=0.3, ω₃=0.5	45	9.4	0.78	392	满足
9	142.66	ω₁=0.3, ω₂=0.3, ω₃=0.4	40	9.6	0.85	398	满足
10	142.31	ω₁=0.3, ω₂=0.2, ω₃=0.5	42	9.8	0.78	398	满足

山顶雷达数量	迭代收敛次数	适应度值	运行时间/s
3	42	143.15	32.6
	45	144.10	34.5
	38	147.41	31.5
4	47	142.86	34.2
	51	143.67	35.8
	44	146.30	32.6
5	55	142.67	34.5
	58	143.22	36.7
	56	145.19	31.8

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