A Cooperative Task Allocation Method for Multi-UAV in Large-Scale Scenarios

WANG Junjie; YAN Dongyu; HAO Yongping; XU Jiulong

doi:10.12382/bgxb.2025.0530

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A Cooperative Task Allocation Method for Multi-UAV in Large-Scale Scenarios

更新时间：2026-04-24

- A Cooperative Task Allocation Method for Multi-UAV in Large-Scale Scenarios
- Acta Armamentarii Vol. 47, Issue 1, Pages: 250530(2026)
- 作者机构：
  
  沈阳理工大学理学院，辽宁沈阳 110159
  沈阳理工大学装备工程学院，辽宁沈阳 110159
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0530
  CLC： V279
- Received：23 June 2025，
  
  Online First：11 February 2026，
  
  Published：31 January 2026
- 稿件说明：
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WANG Junjie, YAN Dongyu, HAO Yongping, et al. A Cooperative Task Allocation Method for Multi-UAV in Large-Scale Scenarios[J]. Acta Armamentarii, 2026, 47(1): 250530.
DOI：

WANG Junjie, YAN Dongyu, HAO Yongping, et al. A Cooperative Task Allocation Method for Multi-UAV in Large-Scale Scenarios[J]. Acta Armamentarii, 2026, 47(1): 250530. DOI： 10.12382/bgxb.2025.0530.

摘要

目前，多无人机（Unmanned Aerial Vehicle

UAV）在大规模任务场景下的任务分配问题仍是一个挑战性问题。传统启发式算法可在较低计算复杂度下得到满意的解，但收敛速度慢且难以收敛到全局最优解。为此提出一种基于UAV链、任务链和双阶段修复策略的遗传算法（Genetic Algorithm Based on UAV-chain

Task-chain

and Two-Stage Repair strategy

UTTSRGA）。在编码结构中设计UAV链和任务链来量化任务执行代价，增强了编码中的信息承载能力并显著提升搜索效率。针对交叉操作后出现任务缺失与任务重复问题，设计双阶段修复策略。第一阶段设计随机填充机制，增强对解空间的全局搜索能力；第二阶段设计邻接映射表修复机制，根据任务间的邻接关系提供进化方向，有效引导种群向当前最优解快速收敛。提出动态复合变异策略，融合自适应变异率与基于任务链值的变异点选择，并设计4种功能互补的变异算子，多维度协同优化解的质量。针对大规模场景下的路径交叉问题，引入路径优化策略，从实践角度进一步优化任务分配方案。实验结果表明，UTTSRGA在不同任务规模下，尤其是大规模复杂任务场景中，在解的质量、收敛速度和鲁棒性3个方面均表现出显著优势。

Abstract

Currently

the task allocation problem for multiple Unmanned Aerial Vehicles(UAV) in large-scale scenarios remains a challenge. While traditional heuristic algorithms can achieve satisfactory solutions with lower computational complexity

they are often characterized by slow convergence rates and difficulty in reaching the global optimal solution. Therefore

this paper proposes an improved genetic algorithm based on UAV-chain

task-chain

and a two-stage repair strategy (UTTSRGA) . UAV chains and task chains are designed within the encoding structure to quantify task execution costs

which enhances the information carrying capacity in coding and significantly improves the search efficiency. To address the issues of task omission and duplication arising after crossover operations

a two-stage repair strategy is designed. In the first stage

a random filling mechanism is designed to enhance the global search capability in the solution space; In the second stage

an adjacency mapping table repair mechanism is designed to provide evolutionary direction based on the adjacency relationships between tasks

effectively guiding the population to rapidly converge towards the current optimal solution. A dynamic composite mutation strategy is proposed

which integrates an adaptive mutation rate with mutation point selection based on task chain values. Four complementary mutation operators are designed to collaboratively optimize solution quality from multiple dimensions. To address the path crossing problem in large-scale scenarios

a path optimization strategy is introduced to further optimize the task allocation scheme from an application perspective. Experimental results demonstrate that UTTSRGA exhibits significant advantages in solution quality

convergence speed

and robustness across various task scales

particularly in large-scale and complex task scenarios.

关键词

Keywords

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