面向动态目标搜索与打击的空地协同自主任务分配方法

doi:10.12305/j.issn.1001-506X.2024.07.17

摘要/Abstract

摘要：

面向复杂城市环境中的运动目标搜索与打击任务, 单域无人平台受视野范围和运动能力等限制, 易出现目标遗漏和任务完成率低的问题。针对这些问题, 提出一种面向动态目标搜索与打击的空地协同自主任务分配方法, 通过结合无人机视野范围广以及无人车机动性强的特点, 提升空地无人系统的任务执行效率和区域覆盖率。一方面, 针对未知运动目标, 提出一种基于数字信息素的目标搜索模型, 以平台协同收益和区域覆盖率为优化指标, 保证在尽可能短的时间周期内发现区域中的所有目标。另一方面, 面向动态到达的打击任务, 构建基于可行路径规划的任务分配模型, 以平台能耗和任务完成时间为目标函数, 在保证任务完成率的同时, 提高空地协同系统的资源利用率。与现有方法相比, 所提方法能够在最短的时间内发现所有目标, 区域覆盖率达到55%以上, 且资源利用率为84.4%。实验结果表明, 所提方法具备较好的目标搜索和任务执行能力。

关键词: 空地协同, 数字信息素, 目标搜索, 任务分配, 路径规划

Abstract:

For the task of moving target search and strike in a complicated urban environment, the single-domain unmanned platform is limited by its field of vision and movement capability, which is easy to cause problems of the target omission and the low task completion rate. To solve these problems, an air-ground cooperative autonomous task allocation method for dynamic target search and strike is proposed. It aims to improve the efficiency of task execution and regional coverage of air-ground unmanned systems by combining the characteristics of the wide field of vision of unmanned aerial vehicles and the strong mobility of unmanned ground vehicles. For unknown moving targets, a target search model based on digital pheromones is proposed, which takes platform cooperation income and regional coverage as optimization metrics to ensure that all targets in the region can be found in the shortest possible time period. In addition, for the dynamic arrival strike task, a task allocation model based on feasible path planning is proposed, which takes the platform energy consumption and task completion time as the objective function to ensure the task completion rate and improve the resource utilization of the air-ground cooperative system. Compared with the existing methods, the proposed method can find all targets in the shortest time. The regional coverage rate can reach more than 55%. And the resource utilization rate is 84.4%. Experimental results show that the proposed method has excellent capabilities for target search and task execution.

Key words: air-ground cooperation, digital pheromone, target search, task allocation, path planning

中图分类号:

TP391.9

费博雯, 包卫东, 刘大千, 朱晓敏. 面向动态目标搜索与打击的空地协同自主任务分配方法[J]. 系统工程与电子技术, 2024, 46(7): 2346-2358.

Bowen FEI, Weidong BAO, Daqian LIU, Xiaomin ZHU. Air-ground cooperative autonomous task allocation method for dynamic target search and strike[J]. Systems Engineering and Electronics, 2024, 46(7): 2346-2358.

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无人机编号	网格索引	载弹量	速度区间	任务编号
1	(19, 1)	2	[0, 30]	[1, 2, 3]
2	(82, 1)	2	[0, 30]	[1, 2, 3]
3	(19, 82)	2	[0, 30]	[1, 2, 3]
4	(1, 19)	2	[0, 30]	[1, 2, 3]
5	(40, 1)	2	[0, 30]	[1, 2, 3]
6	(82, 19)	2	[0, 30]	[1, 2, 3]
7	(40, 82)	2	[0, 30]	[1, 2, 3]
8	(1, 40)	2	[0, 30]	[1, 2, 3]

无人车编号	网格索引	载弹量	速度区间	任务编号
1	(61, 1)	2	[0, 40]	[1, 2, 3]
2	(82, 40)	2	[0, 40]	[1, 2, 3]
3	(61, 82)	2	[0, 40]	[1, 2, 3]
4	(1, 61)	2	[0, 40]	[1, 2, 3]
5	(79, 1)	2	[0, 40]	[1, 2, 3]
6	(82, 61)	2	[0, 40]	[1, 2, 3]
7	(79, 82)	2	[0, 40]	[1, 2, 3]
8	(1, 79)	2	[0, 40]	[1, 2, 3]

编号	网格索引	运动类型	需打击量	运动状态
T₁	(61, 1)	1	2	[0, 1]
T₂	(82, 40)	2	3	[0, 1]
T₃	(61, 82)	3	4	[0, 1]
T₄	(1, 61)	1	2	[0, 1]
T₅	(79, 1)	2	3	[0, 1]
T₆	(82, 61)	3	4	[0, 1]
T₇	(79, 82)	1	2	[0, 1]
T₈	(1, 79)	2	3	[0, 1]
T₉	(61, 61)	3	4	[0, 1]

方法	时间周期	目标发现数	目标打击数	区域覆盖率/%	运行时间/s
AGENTS	100	7	6	37.2	77.9
	200	9	9	61.2	149.2
	300	9	9	72.3	191.6
DMPC	100	6	4	31.7	76.4
	200	7	6	52.4	165.1
	300	8	6	70.1	206.6
LPSO	100	4	4	32.6	85.6
	200	7	6	55.4	175.4
	300	9	9	70.9	236.9

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