面向未知动目标的多无人机协同动态搜索决策方法

傅晋博; 张栋; 王孟阳; 邓杰

doi:10.12382/bgxb.2025.0057

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面向未知动目标的多无人机协同动态搜索决策方法

更新时间：2026-04-24

- 面向未知动目标的多无人机协同动态搜索决策方法
- Multi-UAV Collaborative Dynamic Search Decision-making Method for Unknown Moving Targets
- 兵工学报 2026年47卷第1期页码：250057
- 作者机构：
  
  西北工业大学航天学院，陕西西安 710072
  四川九洲电器集团有限责任公司，四川绵阳 621000
- 作者简介：
  
  *通信作者邮箱：zhangdong@nwpu.edu.cn
- 基金信息：
  
  国家自然科学基金项目(52472417)
- DOI：10.12382/bgxb.2025.0057
  中图分类号： V249
- 收稿：2025-01-16，
  
  网络首发：2026-02-11，
  
  纸质出版：2026-01-31
- 稿件说明：
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傅晋博, 张栋, 王孟阳, 等. 面向未知动目标的多无人机协同动态搜索决策方法[J]. 兵工学报, 2026,47(1):250057.

FU Jinbo, ZHANG Dong, WANG Mengyang, et al. Multi-UAV Collaborative Dynamic Search Decision-making Method for Unknown Moving Targets[J]. Acta Armamentarii, 2026, 47(1): 250057.
傅晋博, 张栋, 王孟阳, 等. 面向未知动目标的多无人机协同动态搜索决策方法[J]. 兵工学报, 2026,47(1):250057. DOI： 10.12382/bgxb.2025.0057.

FU Jinbo, ZHANG Dong, WANG Mengyang, et al. Multi-UAV Collaborative Dynamic Search Decision-making Method for Unknown Moving Targets[J]. Acta Armamentarii, 2026, 47(1): 250057. DOI： 10.12382/bgxb.2025.0057.

摘要

为高效指引无人机群搜索指定区域内的多个未知动态目标，设计一种基于深度强化学习的预测驱动协同搜索决策方法（Deep Reinforcement Learning-Predictive Collaborative Search Decision Method

DRL-P-CSDM）。基于栅格化方法，综合环境信息和历史搜索信息构建环境信息图与信息确定性图，并通过设计时间衰减因子生成状态量，引导无人机进行区域重访以应对目标的主动规避，提升搜索效率。设计一个功能分区的深度神经网络架构，能够自主对环境进行预测，避免人工设计模型适配性差的问题。基于强化学习方法设计奖励函数，在稠密奖励中引入捕获概率，加速收敛过程，采用分布式架构，以适应任意数量无人机的部署要求，并在通信距离受限和信息更新延迟的情况下仍能完成任务。通过算法对比、鲁棒性分析以及半实物仿真验证新方法的有效性。仿真结果表明：DRL-P-CSDM在目标检获率上较传统深度强化学习方法提高11. 45%，任务完成时间减少48. 02%，无人机生存概率提高10. 31%；DRL-P-CSDM具有较强的综合性、鲁棒性和通用性，能在多尺度复杂环境下稳定运行，不受集群规模限制，在安全监控、战场侦察、林区巡检和灾后救援等领域具有广泛的工程应用价值。

Abstract

In order to efficiently guide an unmanned aerial vehicle (UAV) swarm to search multiple unknown dynamic targets within a designated area

a deep reinforcement learning-based predictive-driven collaborative search decision-making method (DRL-P-CSDM ) is proposed. The proposed method integrates environmental and historical search information to construct the environmental information maps and information determinacy maps based on a grid-based approach. A time-decaying window is designed to generate state variables

guiding UAVs to revisit regions and counteract the active evasion of targets

thereby improving search efficiency. A deep neural network architecture is designed

which incorporates the target location prediction

survival decision-making and action judgment functionalities. This architecture enables autonomous environmental prediction

eliminating the issue of poor adaptability caused by manually designed models. A reward function is formulated based on reinforcement learning

where a capture probability is introduced into a dense reward framework to accelerate the convergence process. A distributed architecture is used to be capable of accommodating arbitrary numbers of UAVs

and still completing the task under the conditions of limited communication distance and delayed information update. The effectiveness of the proposed method is validated through algorithm comparison

robustness analysis

and semi-physical simulation. Simulated results show that the DRL-P-CSDM improves the target detection rate by 13. 72%

reduces the mission completion time by 48. 65%

and increases the UAV survival probability by 14. 86%. This method demonstrates strong comprehensiveness

robustness and versatility. It is capable of stable operation in multi-scale complex environments and is not limited by the size of UAV swarm. It holds broad engineering application potential in fields such as security monitoring

battlefield reconnaissance

forest area inspection

and post-disaster rescue operations.

关键词

Keywords

references

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