航空学报 > 2020, Vol. 41 Issue (S2): 724287-724287   doi: 10.7527/S1000-6893.2020.24287

基于有向图的蜂群无人机故障影响

陈严波, 黄金龙, 汪志军, 姜斌, 程月华, 杨浩   

  1. 南京航空航天大学 自动化学院, 南京 210016
  • 收稿日期:2020-04-29 修回日期:2020-05-28 发布日期:2020-06-18
  • 通讯作者: 程月华 E-mail:chengyuehua@nuaa.edu.cn
  • 基金资助:
    中央高校基本科研业务费专项资金(NC2020002,NP2020103)

Influences of drone swarm failure based on directed graph

CHEN Yanbo, HUANG Jinlong, WANG Zhijun, JIANG Bin, CHENG Yuehua, YANG Hao   

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2020-04-29 Revised:2020-05-28 Published:2020-06-18
  • Supported by:
    The Fundamental Research Funds for the Central Universities(NC2020002,NP2020103)

摘要: 基于蜂群无人机控制模型,针对虚假数据注入的故障情形,为了分析该故障情形下,蜂群无人机中故障的的影响及传播关系,采用故障传播有向图及一致性理论,开展蜂群无人机故障影响机理研究。首先,通过Dijkstra算法计算故障源到各无人机的最短路径,建立故障传播有向图;然后预测各无人机受故障影响的程度,结合蜂群无人机系统,计算一致性行为偏差的指标,并将其作为实际故障影响程度;最后,通过对预测结果与实际故障影响程度进行仿真比较,验证了所建立的故障影响机理的合理性。

关键词: 蜂群无人机, 虚假数据, 有向图, 故障影响, 故障传播

Abstract: Based on the drone swarm control model, the influence mechanism of the drone swarm failure is studied using the failure propagation directed graph and consistency theory, aiming at the fault resulted from false information injection. The shortest path from the fault source to each drone is first obtained by the Dijkstra Algorithm, based on which a directed graph of fault propagation is established. Then, the influence degree on each drone by the fault is predicted, and the index of consistent behavior deviation is calculated as the actual influence degree in combination with the drone swarm system. Finally, the prediction result is compared with the actual fault influence degree through simulation, and the accuracy of the fault influence mechanism is verified.

Key words: drone swarm, false data, directed graph, influence of fault, fault propagation

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