摘要
近年来,空间机器人集群方式的在轨服务技术受到各航天大国的重视。空间机器人集群对目标航天器进行在轨服务时,要将收集的目标信息传输至中枢卫星,如何均衡集群中每个节点的通信功耗是一个重要的研究问题。针对空间机器人集群与数据中枢卫星之间的通信功率最优问题,提出一种基于数据及其压缩率预测的空间机器人集群中心节点选择算法(DCP)。由于集群通信中的通信功耗主要与通信距离和通信时长(数据量)有关,可以基于集群的运动轨迹预测未来时刻的数据和压缩率,从而选择最优的集群中心节点,构建通信链路。在实验仿真中,与固定中心算法、度中心性算法、介数中心性算法以及接近中心性算法相比,DCP算法可以有效降低集群通信功耗,并且与实际的最优结果相比误差小于3%。
In recent years,the in-orbit service technology of space robot clusters has attracted the attention of various space powers.When the space robot cluster serves the target spacecraft in orbit,the collected target information needs to be transmitted to the central satellite.How to balance the communication power consumption of each node in the cluster is an important research problem.Aiming at the problem of optimal communication power between the space robot cluster and the data hub satellite,a central node selection algorithm(data and data compression ratio prediction,DCP)based on data and compression ratio prediction was proposed in this paper.Since the communication power consumption in the cluster communication is mainly related to the communication distance and communication duration(data volume),the data and compression rate at future times can be predicted based on the movement trajectory of the cluster,thus selecting the optimal central node of the cluster and constructing the communication link.In the experimental simulation,compared to a fixed point,degree centrality,betweenness centrality,and closeness centrality,DCP algorithm can effectively reduce the power consumption of cluster communication,and the error is less than 3%compared with the actual optimal result.
作者
杨晅
陈宏宇
YANG Xuan;CHEN Hongyu(Innovation Academy for Microsatellites,Chinese Academy of Sciences,Shanghai 201203,China;ShanghaiTech University,Shanghai 201210,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《中国科学院大学学报（中英文）》
CAS
CSCD
北大核心
2024年第5期695-704,共10页
Journal of University of Chinese Academy of Sciences
