面向城市环境的多无人机协同打击任务分配与路径规划算法

胡晟荣; 王强; 钱玥; 杜雄梓; 郑文天

doi:10.12382/bgxb.2025.0551

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面向城市环境的多无人机协同打击任务分配与路径规划算法

更新时间：2026-04-24

- 面向城市环境的多无人机协同打击任务分配与路径规划算法
- Multi-UAV Cooperative Strike Task Assignment and Path Planning Algorithm for Urban Environments
- 兵工学报 2026年47卷第1期页码：250551
- 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：
  
  *通信作者邮箱：wang_qiang@bit.edu.cn
- 基金信息：
- DOI：10.12382/bgxb.2025.0551
  中图分类号： V249.12;V279+.2
- 收稿：2025-06-25，
  
  网络首发：2025-12-25，
  
  纸质出版：2026-01-31
- 稿件说明：
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胡晟荣, 王强, 钱玥, 等. 面向城市环境的多无人机协同打击任务分配与路径规划算法[J]. 兵工学报, 2026,47(1):250551.

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.
胡晟荣, 王强, 钱玥, 等. 面向城市环境的多无人机协同打击任务分配与路径规划算法[J]. 兵工学报, 2026,47(1):250551. DOI： 10.12382/bgxb.2025.0551.

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

FEI C, LU Z, JIANG W W. Heuristic optimization-based trajectory planning for UAV swarms in urban target strike operations [J]. Drones,2024,8（12）:777.

XUN Y L, MA Y K, ZHAO M Y, et al. Research on multi-UAv coordinated strike task allocation based on particle swarm optimization [C]∥Proceedings of International Conference on Guidance, Navigation and Control. Singapore: Springer, 2024:305-316.

ZHANG J, CUI Y N, REN J. Dynamic mission planning algorithm for UAV formation in battlefield environment [J]. IEEE Transactions on Aerospace and Electronic Systems,2022,59（4）:3750-3765.

刘兴宇，郭荣化，任成才，等.基于身份匈牙利算法的无人机蜂群分布式目标分配方法[J].兵工学报，2023,44（9）:2824-2835.

LIU X Y, GUO R H, REN C C, et al.Distributed target assignment method for UAV swarms using identity hungarian algorithm[J].Acta Armamentarii,2023,44（9）:2824-2835.（in Chinese）

MA T X, LU P, DENG F W, et al. Air-ground collaborative multi-target detection task assignment and path planning optimization [J]. Drones,2024,8（3）:110.

XIONG J, LI J, LI J, et al. Probability-tuned market-based allocations for UAV swarms under unreliable observations [J]. IEEE Transactions on Cybernetics,2023,53（11）:6803-6814.

KIM K S, KIM H Y, CHOI H L. A bid-based grouping method for communication-efficient decentralized multi-UAV task allocation [J]. International Journal of Aeronautical and Space Sciences, 2020,21:290-302.

ZHEN Z Y, XING D J, GAO C. Cooperative search-attack mission planning for multi-UAV based on intelligent self-organized algorithm [J]. Aerospace Science and Technology, 2018, 76:402-411.

王磊，徐超，李淼，等.多飞行器协同任务分配的改进粒子群优化算法[J].兵工学报，2023,44（8）:2224-2232.

WANG L, XU C, LI M, et al.Improved particle swarm optimization algorithm for cooperative task assignment of multiple vehicles[J].Acta Armamentarii,2023,44（8）:2224-2232.（in Chinese）

宫远强，张业鹏，马万鹏，等.无人机蜂群中的群体智能涌现机理[J].兵工学报，2023,44（9）:2661-2671.

GONG Y Q, ZHANG Y P, MA W P, et al.Mechanisms of group intelligence emergence in UAV swarms[J].Acta Armamentarii, 2023,44（9）:2661-2671.

ZHANG Z, JIANG J, ZHANG W A. Distributed dynamic task allocation for unmanned aerial vehicle swarm systems: a networked evolutionary game-theoretic approach [J]. Chinese Journal of Aeronautics,2024,37（6）:182-204.

CHEN J X, WU Q H, XU Y H, et al. Joint task assignment and spectrum allocation in heterogeneous UAV communication networks:a coalition formation game-theoretic approach[J]. IEEE Transactions on Wireless Communications, 2021, 20（1）: 440-452.

MARTIN J G, MUROS F J, MAESTRE J M, et al. Multi-robot task allocation clustering based on game theory [J]. Robotics and Autonomous Systems,2023,161:104314.

ZHOU L, CHEN Y, LI K X, et al. Stackelberg game-based computation offloading in urban iot systems with UAV-assisted multi-access edge computing [J]. IEEE Internet of Things Journal,2024,12（7）:8178-8191.

HE W J, YAO H P, MAI T L, et al. Three-stage Stackelberg game enabled clustered federated learning in heterogeneous UAV swarms[J]. IEEE Transactions on Vehicular Technology,2023, 72（7）:9366-9380.

CHEN J X, WU Q H, XU Y H, et al. A multi-leader multi-follower stackelberg game for coalition-based UAV MEC networks [J]. IEEE Wireless Communications Letters, 2021, 10（11）:2350-2354.

XIE R C, WEN W, WANG W Z, et al. Incentive mechanism design for trust-driven resources trading in computing force networks:contract theory approach [J]. IEEE Transactions on Network and Service Management,2025,22（1）:618-634.

宋超，李波，马云红，等.基于优化A * 和MPC融合算法的三维无人机航迹规划[J ] .系统工程与电子技术，2023,45（12）:3995-4004.

SONG C, LI B, MA Y H, et al.3D UAV trajectory planning based on optimized A * and MPC fusion algorithm [J ] .Systems Engineering and Electronics, 2023, 45（12）: 3995-4004.（in Chinese）

WANG J Y, LI Y H, LI R X, et al. Trajectory planning for UAV navigation in dynamic environments with matrix alignment Dijkstra[J].Soft Computing,2022,26（22）:12599-12610.

SALZMAN O, HALPERIN D. Asymptotically near-optimal RRT for fast, high-quality motion planning[J]. IEEE Transactions on Robotics,2016,32（3）:473-483.

张海阔，孟秀云.基于改进RRT * 算法的无人机在线航迹规划[J ] .系统工程与电子技术，2024,46（12）:4157-4164.

ZHANG H K, MENG X Y.UAV online trajectory planning based on improved RRT * algorithm [J ] .Systems Engineering and Electronics,2024,46（12）:4157-4164.（in Chinese）

SHAO S K, PENG Y, HE C L, et al. Efficient path planning for UAV formation via comprehensively improved particle swarm optimization[J]. ISA Transactions,2020,97:415-430.

武晓雯，郭孟营，胡阿建，等.基于改进遗传粒子群算法的无人机路径规划[J].仪器仪表学报，2025,46（4）:315-325.

WU X W, GUO M Y, HU A J, et al.Path planning for UAV based on improved hybrid genetic particle swarm algorithm[J].Chinese Journal of Scientific Instrument, 2025, 46（4）: 315-325.（in Chinese）

ASLAN M F, DURDU A, SABANCI K. Goal distance-based UAV path planning approach, path optimization and learning-based path estimation:GDRRT * , PSO-GDRRT * and BiLSTM-PSO-GDRRT[J ] . Applied Soft Computing,2023,137:110156.

LIU X Y, LI G Q, YANG H Y, et al. Agricultural UAV trajectory planning by incorporating multi-mechanism improved grey wolf optimization algorithm [J]. Expert Systems with Applications, 2023,233:120946.

王康，司鹏，陈莉，等.基于改进沙猫群算法的无人机三维航迹规划[J].兵工学报，2023,44（11）:3382-3393.

WANG K, SI P, CHEN L, et al.3D path planning of unmanned aerial vehicle based on enhanced sand cat swarm optimization algorithm[J].Acta Armamentarii,2023,44（11）:3382-3393.（in Chinese）

CHEN H, LIANG Y H, MENG X. A UAV path planning method for building surface information acquisition utilizing opposition-based learning artificial bee colony algorithm [J]. Remote Sensing,2023,15（17）:4312.

GHASEMI M, ZARE M, TROJOVSKY P, et al. Optimization based on the smart behavior of plants with its engineering applications:Ivy algorithm[J]. Knowledge-Based Systems,2024,295:111850.

于彦鹏，余墨多，汤奇荣，等.面向城市应急物资配送的多无人机协同路径规划算法[J].控制与决策，2025,40（4）:1098-1106.

YU Y P, YU M D, TANG Q R, et al.Multi-UAV cooperative path planning algorithm for urban emergency material distribution [J].Control and Decision, 2025, 40（4）: 1098-1106.（in Chinese）

DUAN H B, ZHAO J X, DENG Y M, et al. Dynamic discrete pigeon-inspired optimization for multi-UAV cooperative search-attack mission planning[J]. IEEE Transactions on Aerospace and Electronic Systems,2021,57（1）:706-720.

XU W L, ZHANG T K, MU X D, et al. Trajectory planning and resource allocation for multi-UAV cooperative computation [J]. IEEE Transactions on Communications, 2024, 72（7）: 4305-4318.

HOUSSEIN E H, SAAD M R, HASHIM F A, et al. Lévy flight distribution:a new metaheuristic algorithm for solving engineering optimization problems[J]. Engineering Applications of Artificial Intelligence,2020,94:103731.

HU S R, WANG Q, LI Y X, et al. A framework for multi-UAV task allocation and path planning in complex urban environments [J]. Journal of Physics: Conference Series, 2024, 2891（11）:112006.

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