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由于科技的迅速发展和现代生活的需要，研究异构多无人机多任务分配技术，发挥多无人机协同操作优势具有重要的理论价值与现实意义。集中式控制结构下，中心控制单元掌握所有无人机的信息，分析无人机与任务要求的匹配情况，完成多无人机多任务分配的核心在于构建任务分配模型并求解。设计基于任务完成质量、无人机总飞行航程的综合考量，充分考虑无人机种类、数量、性能与任务类型、数量等因素间的约束关系，建立多无人机多任务分配模型，采用离散粒子群算法求解，仿真结果验证了所提方法的有效性。
Abstract：With the rapid development of science and technology and the demands of modern life, studying heterogeneous multi-UAV multi-task assignment technology and leveraging the advantages of multi-UAV collaborative operation have important theoretical value and practical significance. Under the centralized control structure, the central control unit can handle the information from all UAVs and analyzes the matching situation between UAVs and task requirements. The core of completing the multi-task assignment of multiple UAVs lies in constructing a task assignment model and solving it. Based on the comprehensive consideration of task completion quality and total flight range of UAVs, the design fully considers the constraint relationship between the type, quantity, performance of UAVs and the type and quantity of tasks, and establishes a multi-task assignment model for multiple UAVs. The discrete particle swarm optimization algorithm is used to solve the problem, and the simulation results verify the effectiveness of the proposed method.
[1] 叶文，朱爱红，潘长鹏，等.多UCAV协同目标分配算法研究[J].系统工程与电子技术，2010,32(1):104-108.
[2] GAO C,ZHEN Z Y,GONG H J.A Self-organized Search and Attack Algorithm for Multiple Unmanned Aerial Vehicles[J].Aerospace Science and Technology,2016,54:229-240.
[3] DANOY G,BRUST M R,BOUVRY P.Connectivity Stability in Autonomous Multi-level UAV Swarms for Wide Area Monitoring[C]//Proceedings of the Fifth ACM International Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications.Cancun:ACM,2015:1-8.
[4] 李峥，蔡译锋，吴军.面向作战效能优化的反辐射无人机目标分配[J].无线电工程，2019,49(10):849-854.
[5] 刘树光，刘荣华，王欢，等.国外无人机集群协同控制技术新进展[J].飞航导弹，2021(8):24-31.
[6] 陈宗基，魏金钟，王英勋，等.无人机自主控制等级及其系统结构研究[J].航空学报，2011,32(6):1075-1083.
[7] BETHKE B,VALENTI M,HOW J P.UAV Task Assignment[J].IEEE Robotics and Automation Magazine,2008,15(1):39-44.
[8] SECREAT B R.Traveling Salesman Problem for Surveillance Mission Using Particle Swarm Optimization[M].[S.l.]:Biblioscholar,2012.
[9] O'ROURKE K P,CARLTON W B,BAILEY T G,et al.Dynamic Routing of Unmanned Aerial Vehicles Using Reactive Tabu Search[J].Military Operations Research,2001,6(1):5-30.
[10] ALIGHANBARI M.Task Assignment Algorithms for Teams of UAVs in Dynamic Environments[D].Cambridge:Massachusetts Institute of Technology,2004.
[11] SMITH R G.The Contract Net Protocol:High-level Communication and Control in a Distributed Problem Solver[J].IEEE Transactions on Computers,1980,C-29(12):1104-1113.
[12] NII H P.Blackboard Systems:The BB Model of Problem Solving and the Evolution of Blackboard Architecture[J].AI Magazine,1986,7(2):38-53.
[13] 胡嘉薇，贾泽群，孙延涛，等.多约束条件下多无人机协同任务规划问题分析及求解方法综述[J].计算机科学，2023,50(7):176-193.
[14] MA G J,DUAN H B,LIU S Q.Improved Ant Colony Algorithm for Global Optimal Trajectory Planning of UAV Under Complex Environment[J].International Journal of Computer Science and Applications,2007,4(3):57-68.
[15] 陈侠，乔艳芝.无人机任务分配综述[J].沈阳航空航天大学学报，2016,33(6):1-7.
[16] 陈明强，李奇峰，冯树娟，等.基于改进粒子群算法的无人机三维航迹规划[J].无线电工程，2023,53(2):394-400.
[17] 樊洁茹，李东光.有人机/无人机协同作战研究现状及关键技术浅析[J].无人系统技术，2019,2(1):39-47.
[18] KENNEDY J,EBERHART R.Particle Swarm Optimization[C]//Proceedings of ICNN'95-International Conference on Neural Networks.Perth:IEEE,1995:1942-1948.
基本信息:
DOI：
中图分类号:V355;E91
引用信息:
[1]马培博,刘新,耿川,等.基于粒子群算法的集中式多无人机静态任务分配[J].无线电工程,2025,55(01):204-210.
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