Multi-UAV Cooperative Strike Task Assignment and Path Planning Algorithm for Urban Environments

HU Shengrong; WANG Qiang; QIAN Yue; DU Xiongzi; ZHENG Wentian

doi:10.12382/bgxb.2025.0551

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Multi-UAV Cooperative Strike Task Assignment and Path Planning Algorithm for Urban Environments

更新时间：2026-04-24

- Multi-UAV Cooperative Strike Task Assignment and Path Planning Algorithm for Urban Environments
- Acta Armamentarii Vol. 47, Issue 1, Pages: 250551(2026)
- 作者机构：
  
  北京理工大学机电学院，北京 100081
  北方科技信息研究所，北京 100089
  北京机电工程研究所，北京 100074
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0551
  CLC： V249.12;V279+.2
- Received：25 June 2025，
  
  Online First：25 December 2025，
  
  Published：31 January 2026
- 稿件说明：
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HU Shengrong, WANG Qiang, QIAN Yue, et al. Multi-UAV Cooperative Strike Task Assignment and Path Planning Algorithm for Urban Environments[J]. Acta Armamentarii, 2026, 47(1): 250551.
DOI：

HU Shengrong, WANG Qiang, QIAN Yue, et al. Multi-UAV Cooperative Strike Task Assignment and Path Planning Algorithm for Urban Environments[J]. Acta Armamentarii, 2026, 47(1): 250551. DOI： 10.12382/bgxb.2025.0551.

摘要

针对城市复杂环境下多旋翼无人机协同打击任务中目标分配效率与路径规划安全性的挑战，提出融合博弈论与智能优化的两阶段决策框架。任务分配阶段构建Stackelberg-契约模型，设计包含风险收益的打击契约。无人机基于自身状态动态选择契约，通过信誉激励实现效率与公平性平衡。路径规划阶段使用改进型多目标常青藤算法，融合莱维飞行增强全局搜索并建立路径长度-能耗-安全的多目标优化函数。两阶段通过路径成本反馈形成闭环优化。仿真结果表明，该框架相较于其他方法，总任务效能提升4%，生成路径长度减少13. 4%，可为多无人机集群城市作战提供高效鲁棒的协同决策方案。

Abstract

To address the challenges of target allocation efficiency and path planning safety in the collaborative strike tasks of multi-rotor unmanned aerial vehicles (UAVs) in urban environments

this paper proposes a two-phase decision-making framework that integrates game theory and intelligent optimization. A Stackelberg contract model for the task allocation phase is constructed to design a strike contract that incorporates risk-benefit considerations. The UAV dynamically selects the contract based on its state and realizes the balance between efficiency and fairness through reputation incentives. An improved multi-objective Ivy algorithm is used in the path planning phase. The algorithm incorporates Levy flight to enhance the global search and establish a multi-objective optimization function of path length-energy consumption-safety. The two phases form a closed-loop optimization through path cost feedback. Simulated results show that the framework improves the total effectiveness of task assignment by 4% and reduces the length of generated paths by 13. 4% compared to other methods. The framework provides an efficient and robust cooperative scheme for multi-UAV swarms in urban combat.

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references

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