未知环境下多无人机分布式协同搜索方法

江昊贤; 周锐

doi:10.12382/bgxb.2025.0249

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未知环境下多无人机分布式协同搜索方法

更新时间：2026-04-24

- 未知环境下多无人机分布式协同搜索方法
- Distributed Cooperative Search Method for Multi-UAV in Unknown Environment
- 兵工学报 2026年47卷第3期页码：250249
- 作者机构：
  
  北京航空航天大学自动化科学与电气工程学院，北京 100191
- 作者简介：
  
  通信作者邮箱：zhr@buaa.edu.cn
- 基金信息：
  
  科技创新2030-重大项目(2022ZD0208804);国家自然科学基金项目(62473017)
- DOI：10.12382/bgxb.2025.0249
  中图分类号： TP273
- 收稿：2025-04-03，
  
  网络首发：2026-02-05，
  
  纸质出版：2026-03
- 稿件说明：
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江昊贤, 周锐. 未知环境下多无人机分布式协同搜索方法[J]. 兵工学报, 2026,47(3):250249.

JIANG Haoxian, ZHOU Rui. Distributed Cooperative Search Method for Multi-UAV in Unknown Environment[J]. Acta Armamentarii, 2026, 47(3): 250249.
江昊贤, 周锐. 未知环境下多无人机分布式协同搜索方法[J]. 兵工学报, 2026,47(3):250249. DOI： 10.12382/bgxb.2025.0249.

JIANG Haoxian, ZHOU Rui. Distributed Cooperative Search Method for Multi-UAV in Unknown Environment[J]. Acta Armamentarii, 2026, 47(3): 250249. DOI： 10.12382/bgxb.2025.0249.

摘要

为快速降低环境不确定性并减少搜索重叠，从而提高协同搜索效率，提出一种适用于未知环境且通信受限条件下的多无人机分布式协同搜索方法。针对协同区域搜索问题，构建一种基于迭代加权Voronoi图划分的分布式区域划分算法，实现任务区域的动态均衡划分。设计一种基于动态目标选择和参考路径生成的策略，以引导无人机优化搜索过程。采用基于粒子群优化的分布式模型预测控制算法，构建多目标代价函数以优化控制指令，从而避免陷入局部最优并保障无人机的安全性。通过仿真实验，将所提算法与分布式反聚集算法进行对比。研究结果表明：所提算法能够更快实现区域覆盖，并通过增大无人机之间的平均间距有效降低搜索重叠，从而提升系统整体搜索效率与飞行安全性；在障碍环境、不同参数设置及通信受限条件下的测试结果进一步表明，该算法在满足无人机安全约束的前提下能够保持稳定的搜索性能，搜索效率随参数变化的波动较小，表现出良好的实时性与鲁棒性。

Abstract

In order to quickly reduce environmental uncertainty and search overlap

thereby improving the efficiency of collaborative search

a distributed collaborative search method for multiple unmanned aerial vehicles（UAVs）is proposed

which is suitable for unknown environments under communication-constrained conditions. For the collaborative area search problem

a distributed region partitioning algorithm based on iterative weighted Voronoi diagram partitioning is proposed to achieve the dynamic and balanced partitioning of task area. A strategy based on dynamic target selection and reference path generation method is designed to guide UAVs to optimize the search process. Then

a distributed model predictive control algorithm based on particle swarm optimization is employed to construct a multi-objective cost function for optimizing control commands

thereby avoiding being trapped in local optima and ensuring the safety of UAV. The proposed algorithm is compared with the distributed anti-aggregation algorithm through simulation experiments. The results show that the proposed algorithm can achieve faster area coverage and effectively reduce search overlap by increasing the average inter-UAV distance

thus improving overall search efficiency and flight safety. Further tests under obstacle environments

different parameter settings and limited communication conditions demonstrate that the proposed algorithm maintains stable search performance while satisfying the safety constraints of UAVs

with small fluctuation of search efficiency being with the change in parameter

exhibiting good real-time capability and robustness.

关键词

Keywords

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