无人机集群任务分配技术研究综述

doi:10.12305/j.issn.1001-506X.2024.03.18

摘要/Abstract

摘要：

任务分配是无人机集群实现高效遂行作战任务的关键技术。随着无人机集群技术的发展和作战样式的转变, 无人机集群的作战任务领域不断拓展, 任务分配所涵盖的范围不断扩大, 任务分配问题的规模和复杂性不断增加, 这都对无人机集群任务分配技术提出了新的挑战。本文对无人机集群作战理论、任务分配建模、任务预\重分配算法、异构无人系统联合应用下任务分配的研究现状进行了全面的总结, 凝练了目前无人机集群任务分配技术面临的通用化建模、面向多任务的任务预分配算法最优解求解、有限时间下面向突发事件的任务重分配算法寻优、路径规划紧耦合下面向大规模异构无人系统的协同任务分配等问题, 并针对性地论述了未来无人机集群任务分配技术的若干发展方向, 为提升无人机集群任务分配的求解质量和求解速度提供新的研究思路和解决途径, 对于全面了解无人机集群任务分配技术具有重要参考意义。

关键词: 无人机集群, 任务预分配, 任务重分配, 通用化建模, 突发事件

Abstract:

Task allocation is one of the most important technologies for the unmanned aerial vehicle (UAV) swarm to effectively fulfill their combat mission. With the rapid development of UAV swarm technology and the change of combat style, the field of combat missions of UAV swarm continues to expand, the scope of task allocation continues to extend, and the scale and complexity of task allocation problems continue to increase, which have posed new challenges for task allocation technology of the UAV swarm. This paper conducted a throughout review of the current studies on the UAV swarm operation theories, task allocation modeling, task pre-allocation and task reallocation algorithms, task allocation under joint application of heterogeneous unmanned system. Several key questions of the UAV swarm task allocation problem, including generalized modeling of the UAV swarm tasks, optimization of task pre-allocation algorithm for multi-task, optimization of task reallocation algorithm for emergencies in limited time, and cooperative task allocation to large-scale heterogeneous unmanned system under tight coupling of path planning are identified. On the basis of that, the future development directions of the UAV swarm task allocation technology are discussed, which could provide new research ideas and solutions for improving the solution quality and solution speed of UAV swarm task allocation. This review has important reference significance for the comprehensive understanding of task allocation technology of UAV swarm.

Key words: unmanned aerial vehicle (UAV) swarm, task pre-allocation, task reallocation, generalized modeling, emergencies

中图分类号:

V249

毕文豪, 张梦琦, 高飞, 杨咪, 张安. 无人机集群任务分配技术研究综述[J]. 系统工程与电子技术, 2024, 46(3): 922-934.

Wenhao BI, Mengqi ZHANG, Fei GAO, Mi YANG, An ZHANG. Review on UAV swarm task allocation technology[J]. Systems Engineering and Electronics, 2024, 46(3): 922-934.

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算法分类		算法简要介绍	算法特点	代表性方法
集中式算法	最优化方法	构建任务分配问题对应的最优数学模型, 并进行求解	复杂度大, 控制中心负担较重; 对复杂问题的描述能力有限; 难以表达环境随机性和动态性	穷举法图论方法动态规划分支定界
集中式算法	启发式方法	通过运算时间和求解质量的协调, 对解进行启发式优化	易于实现; 计算复杂度低, 性能优越; 但规划结果具有随机性, 依赖于固定角色控制中心	遗传算法粒子群优化算法
分布式算法	自顶向下分布式算法	基于分层递阶求解的思路, 将复杂任务协同分配问题逐层分解为若干个更简单的子任务分配问题, 各无人机通过协商与合作实现问题求解	便于理解; 可扩展性高; 但对通信环境要求较高	基于市场机制算法分布式马尔可夫决策方法动态分布式约束优化方法
分布式算法	自下而上分布式算法	通过研究无人机个体的局部感知和动态反应, 设计基于反应和行为的协同优化策略, 实现多无人机整体自组织任务分配	计算简单; 鲁棒性好; 对规模变化不敏感	基于群智能的自组织算法阈值响应法蚁群优化算法

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