Central node selection strategy of spatial robot cluster based on data and compression ratio prediction

doi:10.7523/j.ucas.2023.007

Abstract

Abstract: 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.

Key words: space robots, space robot clusters, cluster communication, center node selection

CLC Number:

TN927+.2

YANG Xuan, CHEN Hongyu. Central node selection strategy of spatial robot cluster based on data and compression ratio prediction[J]. Journal of University of Chinese Academy of Sciences, 2024, 41(5): 695-704.

References

[1] 孟光,韩亮亮,张崇峰.空间机器人研究进展及技术挑战[J].航空学报,2021, 42(1):523963. DOI:10.7527/S1000-6893.2020.23963.
[2] Ding X L, Wang Y C, Wang Y B, et al. A review of structures, verification, and calibration technologies of space robotic systems for on-orbit servicing[J]. Science China Technological Sciences, 2021,64(3):462-480. DOI:10.1007/s11431-020-1737-4.
[3] Oda M, Kibe K, Yamagata F. ETS-VII, space robot in-orbit experiment satellite[C]//Proceedings of IEEE International Conference on Robotics and Automation. April 22-28, 1996, Minneapolis, MN, USA. IEEE, 2002:739-744. DOI:10.1109/ROBOT.1996.503862.
[4] Ogilvie A, Allport J, Hannah M, et al. Autonomous robotic operations for on-orbit satellite servicing[C]//SPIE Defense and Security Symposium. Proc SPIE 6958, Sensors and Systems for Space Applications II, Orlando, Florida, USA. 2008, 6958:50-61. DOI:10.1117/12.784081.
[5] 刘宏,蒋再男,刘业超.空间机械臂技术发展综述[J].载人航天, 2015, 21(5):435-443. DOI:10.16329/j.cnki.zrht.2015.05.002.
[6] Li W J, Cheng D Y, Liu X G, et al. On-orbit service (OOS) of spacecraft:a review of engineering developments[J]. Progress in Aerospace Sciences, 2019, 108:32-120. DOI:10.1016/j.paerosci.2019.01.004.
[7] Song R, Song X M, Ma Y N, et al. Research on orbit design method for on-orbit service of high orbit spacecraft[C]//2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). December 17-19, 2021, Chongqing, China. IEEE, 2022:657-661. DOI:10.1109/ICIBA52610.2021.9688110.
[8] Mark C P, Kamath S. Review of active space debris removal methods[J]. Space Policy, 2019, 47:194-206. DOI:10.1016/j.spacepol.2018.12.005.
[9] Koene J. Applied network analysis:a methodological introduction[J]. European Journal of Operational Research, 1984, 17(3):422-423. DOI:10.1016/0377-2217(84)90146-2.
[10] Chen D B, Lü L Y, Shang M S, et al. Identifying influential nodes in complex networks[J]. Physica A:Statistical Mechanics and Its Applications, 2012, 391(4):1777-1787. DOI:10.1016/j.physa.2011.09.017.
[11] Nie T Y, Guo Z, Zhao K, et al. Using mapping entropy to identify node centrality in complex networks[J]. Physica A:Statistical Mechanics and Its Applications, 2016, 453:290-297. DOI:10.1016/j.physa.2016.02.009.
[12] Kitsak M, Gallos L K, Havlin S, et al. Identification of influential spreaders in complex networks[J]. Nature Physics, 2010, 6(11):888-893. DOI:10.1038/nphys1746.
[13] 周丽娜,李发旭,巩云超,等.基于K-shell的超网络关键节点识别方法[J].复杂系统与复杂性科学, 2021, 18(3):15-22. DOI:10.13306/j.1672-3813.2021.03.003.
[14] 李钢,王聿达,崔蓉. KiC:一种结合"结构洞"约束值与K壳分解的社交网络关键节点识别算法[J].现代情报, 2020, 40(12):27-35. DOI:10.3969/j.issn.1008-0821.2020.12.003.
[15] Nardelli E, Proietti G, Widmayer P. Finding the most vital node of a shortest path[M]//Lecture Notes in Computer Science. Berlin, Heidelberg:Springer Berlin Heidelberg, 2001:278-287. DOI:10.1007/3-540-44679-6_31.
[16] 赵星.信息网络关键节点对之删除判定[J].中国图书馆学报, 2018, 44(5):47-58. DOI:10.13530/j.cnki.jlis.180014.
[17] Yang Y Z, Yu L, Wang X, et al. A novel method to identify influential nodes in complex networks[J]. International Journal of Modern Physics C, 2020, 31(2):2050022. DOI:10.1142/s0129183120500229.