基于类脑决策模型的多无人机任务分配  

Multi Drone Task Assignment based on Brain-like Decision Model

作　　者：杨明 刘蓉[1,2] 王佑 闫俊辰 YANG Ming;LIU Rong;WANG You;YAN Junchen(College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;Research Institute of Pilotless Aircraft,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)

机构地区：[1]南京航空航天大学自动化学院,南京210016 [2]南京航空航天大学无人机研究院,南京210016

出　　处：《计算机测量与控制》2025年第3期275-286,322,共13页Computer Measurement &Control

基　　金：JWK创新项目(19-163-15-ZT-008-002-06);江苏省重点研发计划(BE2022068-4);LZ十四五预研项目(LZY202303052)。

摘　　要：针对多无人机同时执行多个任务的执行时序不同和无人机任务完成效果以及无人机能耗效率低等问题,建立经验判断-任务预分配-任务重分配-在线决策的类脑决策模型,提出一种基于类脑决策模型的多无人机任务分配算法;融合调度改进组合聚类算法和混合强化学习等策略,将任务分配分为预分配和重分配两个阶段,生成决策网络和评价体系,并引入孤立点离散化策略进行在线决策,旨在解决孤立点的影响,减少状态空间和动作空间的复杂性,提高多无人机系统的分配效率;最后对算法进行了仿真验证。Aiming at the problems of different execution timings for multiple drones simultaneously executing multiple tasks,as well as the low efficiency of drone task completion and energy consumption,a brain-like decision model of experience judgment-task pre-assignment-task reassignment-online decision is established,and a task assignment algorithm based on brain-like decision model is proposed.Fusion scheduling improves strategies such as combinational clustering algorithm and hybrid reinforcement learning,divides task assignment into two stages:pre-assignment and reassignment,generates decision network and evaluation system,and introduces isolated point discretization strategy for online decision-making,aiming to solve the influence of isolated points,reduce the complexity of state space and action space,and improve the allocation efficiency of multi-unmanned aircraft systems.Finally,the algorithm is verified by simulation.

关 键 词：任务分配 类脑决策模型 聚类算法 强化学习 离散化策略 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]