面向网络化无人机集群的计算换通信分布式任务调度方法  

Distributed task scheduling method for networked UAV swarm based on computation-for-communication

作　　者：李杰[1] 陈润丰 彭婷 LI Jie;CHEN Runfeng;PENG Ting(College of Intelligence Science and Technology,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]国防科技大学智能科学学院,湖南长沙410073

出　　处：《国防科技大学学报》2023年第4期45-54,共10页Journal of National University of Defense Technology

基　　金：科技创新2030“新一代人工智能”重大资助项目(2020AAA0108200)。

摘　　要：针对网络化无人机集群任务自主协同问题以及市场竞拍法的优缺点,提出“计算换通信”思想及其相应的分布式任务调度方法。通过对显式和隐式冲突任务的分析,建立任务相关智能体集合。提出基于任务抑制的局部优化方法,用于提前消解部分任务冲突,以减少算法迭代次数。设计基于历史竞标信息的智能体位置推断法,为局部优化提供必要的信息输入。基于组网仿真平台与集群救援场景开展蒙特卡罗仿真实验,结果表明,相比于市场竞拍法中具有代表性的基于共识的捆绑算法和性能影响算法,所提方法能够获得更少的迭代次数、更短的收敛时间以及更优的调度性能。Aiming at the problem of autonomous coordination of networked UAV swarm and the advantages and disadvantages of market auction method,the idea of“computation-for-communication”and its corresponding distributed task scheduling method were proposed.By analyzing explicit and implicit conflicting tasks,a set of task-related agents was established.A local optimization method based on task suppression was proposed to resolve some task conflicts in advance,so as to reduce the number of algorithm iterations.An agent position inference method based on historical bidding information was designed to provide necessary information input for local optimization.Monte Carlo simulation experiments were carried out based on the networking simulation platform and the swarm rescue scenario.The results show that compared with the representative consensus-based bundle algorithm and performance impact algorithm in the market auction method,the proposed method can obtain fewer iterations,shorter convergence time and better scheduling performance.

关 键 词：分布式任务调度 计算换通信框架 市场竞拍法 无人机集群 

分 类 号：V19[航空宇航科学与技术—人机与环境工程]