对抗条件下的无人集群目标重分配方法研究

doi:10.16182/j.issn1004731x.joss.24-1005

摘要/Abstract

摘要：

异构无人集群在未来战争中具有重要的应用潜力，然而，在高强度的对抗中，集群个体遭受打击后，如何高效快速地将受损智能体携带的任务进行重分配进而保证集群任务顺利完成，是无人集群作战运用中必须面对的难题。提出了一种基于改进合同网-匈牙利算法的目标重分配方法。该方法通过指派机制和招标机制，相比对比算法实现了较低通信代价、较快速度的受损智能体目标重分配。仿真实验中，在单个智能体受损情况下，提出的方法将目标重分配完成度平均值由87%提升到100%，在多个智能体受损时仍能稳定完成目标重分配，且该方法资源调度率和快速性均优于对比算法。

关键词: 对抗条件, 个体受损, 无人集群, 目标分配, 重分配

Abstract:

Heterogeneous unmanned swarms have important potential applications in future wars. However, during the high-intensity confrontation in the battlefield, how to efficiently and quickly redistribute the tasks carried by the damaged agents so that the swarms could successfully complete the mission is a difficult problem that must be addressed in the combat application of unmanned swarms. This paper proposes a task reallocation method named improved CNP-HA (contract net protocol-Hungarian algorithm). Through the allocation mechanism and the bidding mechanism, the method realizes the task reallocation of damaged agents with lower communication cost and faster speed comparing with baseline methods. In the simulation experiment, when a single agent is damaged, the average completion rate of target reallocation is increased from 87% to 100% by the proposed method, and the target reallocation can still be completed stably when multiple agents are damaged, and the resource scheduling rate and rapidity of the proposed method are better than the comparison algorithms.

Key words: adversarial conditions, agent damage, unmanned swarm, task allocation, reallocation

中图分类号:

TP391.9

张伦,杨妹,赵拓等 . 对抗条件下的无人集群目标重分配方法研究[J]. 系统仿真学报, 2025, 37(1): 1-12.

Zhang Lun,Yang Mei,Zhao Tuo,et al . Task Reallocation Method for Unmanned Swarm Under Adversarial Conditions[J]. Journal of System Simulation, 2025, 37(1): 1-12.

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类别	速度/(m/s)	位置	数量	最大任务数
侦察智能体	[1,30]	随机	10	6
打击智能体	[1,30]	随机	10	6

关键节点	组内成员
U₀	U₉、U₁₀、U₈、U₁₅、U₁₄、U₁₁、U₁₇、U₁₉
U₃	U₁、U₂、U₄、U₅、U₆、U₇、U₁₂、U₁₈

智能体编号	任务类型	目标分配情况
U₀	侦察	T₁₆→T₂₄→T₁₈
U₁	侦察	T₂₁→T₂₀→T₂₇→T₂₃
U₂	侦察	T₂₂→T₂₆→T₈→T₁
U₃	侦察	T₁₂
U₄	侦察	0
U₅	侦察	T₃
U₆	侦察	T₁₁→T₁₃→T₁₇→T₂
U₇	侦察	T₆
U₈	侦察	T₁₄→T₉→T₇ →T₅→T₂₈→T₂₉
U₉	侦察	T₁₉→T₂₅→T₄→T₀→T₁₅→T₁₀
U₁₀	打击	T₁₉→T₃
U₁₁	打击	T₁₄→T₉→T₁₆→T₂₄→T₁₈→T₂₈
U₁₂	打击	T₇→T₁₂
U₁₃	打击	T₂₆→T₁
U₁₄	打击	T₂₁→T₂₀→T₅→T₂₇→T₂₃→T₂₉
U₁₅	打击	T₆→T₂
U₁₆	打击	T₂₂→T₂₅→T₄→T₀→T₁₅→T₈
U₁₇	打击	0
U₁₈	打击	T₁₁→T₁₃→T₁₇
U₁₉	打击	T₁₀

智能体编号	任务类型	携带目标数量	CNP算法完成度/%	CNP-HA算法完成度/%
U₀	侦察	3	100	100
U₁	侦察	4	75	100
U₂	侦察	4	50	100
U₃	侦察	1	100	100
U₄	侦察	0
U₅	侦察	1	100	100
U₆	侦察	4	75	100
U₇	侦察	1	100	100
U₈	侦察	6	66	100
U₉	侦察	6	66	100
U₁₀	打击	2	100	100
U₁₁	打击	6	66	100
U₁₂	打击	2	100	100
U₁₃	打击	2	100	100
U₁₄	打击	6	83	100
U₁₅	打击	2	100	100
U₁₆	打击	6	83	100
U₁₇	打击	0
U₁₈	打击	3	100	100
U₁₉	打击	1	100	100

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