Autonomous Multi-UAV Target Search Algorithm for Unknown Environments

WANG Zhifang; ZHU Shaohui; LIU Xinyang; LIN Min; XU Gang; LIU Yong

doi:10.12382/bgxb.2025.0521

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Autonomous Multi-UAV Target Search Algorithm for Unknown Environments

更新时间：2026-04-24

- Autonomous Multi-UAV Target Search Algorithm for Unknown Environments
- Acta Armamentarii Vol. 47, Issue 1, Pages: 250521(2026)
- 作者机构：
  
  浙江大学工程师学院，浙江杭州 310015
  天津航海仪器研究所，天津 300130
  浙江大学控制科学与工程学院，浙江杭州 310027
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0521
  CLC： V279
- Received：19 June 2025，
  
  Online First：11 February 2026，
  
  Published：31 January 2026
- 稿件说明：
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WANG Zhifang, ZHU Shaohui, LIU Xinyang, et al. Autonomous Multi-UAV Target Search Algorithm for Unknown Environments[J]. Acta Armamentarii, 2026, 47(1): 250521.
DOI：

WANG Zhifang, ZHU Shaohui, LIU Xinyang, et al. Autonomous Multi-UAV Target Search Algorithm for Unknown Environments[J]. Acta Armamentarii, 2026, 47(1): 250521. DOI： 10.12382/bgxb.2025.0521.

摘要

在执行搜索和救援等关键任务时，旋翼无人机因其高速覆盖能力而日益受到重视。基于这一背景，提出了一种多无人机快速目标搜索算法，旨在实现无人机在目标搜索中的高效协同。该算法的核心在于通过快速探索实现对搜索区域的高效覆盖，进而提升目标搜索效率。具体而言，采用基于网格划分的探索策略和分布式的成对交互机制，使各无人机能够均匀覆盖未知区域，为后续目标搜索奠定基础。在此基础上，提出了一种双模式目标搜索策略，通过动态调整快速搜索与谨慎搜索的优先级，在区域覆盖和目标定位之间实现最优平衡。为提升目标识别能力，传感器融合算法整合了激光雷达与深度相机的感知信息，构建了可集成的目标搜索模块。仿真实验结果表明，在多无人机协同场景下，该算法能够实现对复杂未知环境的自主搜索，不仅保持100%的目标搜索成功率，相比现有算法搜索时间缩短25. 4%以上，同时成功应用于300 m×300 m的大范围场景，平均搜索时间为836. 0 s，并通过消融实验验证了双模式搜索策略的有效性。

Abstract

In critical missions such as search and rescue

the rotary-wing unmanned aerial vehicle is gaining increasing attention due to their high-speed coverage capabilities. A multi-UAV rapid target search algorithm is proposed to achieve the efficient collaboration among UAVs during target search. The proposed algorithm is mainly used to achieve the efficient coverage of the search area through rapid exploration

thereby improving the efficiency of target search. Specifically

a grid-based exploration strategy and a distributed pairwise interaction mechanism are adopted to enable UAVs to evenly cover the unknown area

thus laying the foundation for subsequent target search. On this basis

a dual-mode target search strategy is proposed

which dynamically adjusts the priority between rapid search and cautious search to achieve the optimal balance between area coverage and target localization. The effectiveness of the dual-mode target search strategy is verified through the ablation experiment. To enhance the ability of target recognition

the sensor fusion algorithm integrates the perceptional information from LiDAR and depth cameras to build an integrable target search module. Simulation experiments show that

in a multi-UAV collaborative scenario

the proposed algorithm can achieve autonomous search in complex unknown environments. It not only maintains a 100% target search success rate but also reduces search time by more than 25. 4% compared with existing algorithms. Moreover

it has been successfully applied to a large-scale scenario of 300 m×300 m with an average search time of 836. 0 s.

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references

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Related Institution

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Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan

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