基于图模型的无人集群同步自定位与相对定位

doi:10.7527/S1000-6893.2023.29708

Abstract

Abstract:

Cooperative localization， including self-localization and relative-localization of unmanned carriers， is a technology that improves the localization accuracy of unmanned swarms in navigation infrastructure-limited environments. Self-localization is used to obtain the absolute pose of oneself， and relative-localization is used to obtain the relative pose between carriers. However， existing cooperative localization methods usually solve one aspect of the problem while ignoring the other， which not only limits the engineering application value of cooperative localization but also ignores the constraint relationship between swarm motion and individual motion； thereby， reducing cooperative estimation performance. To solve this problem， this paper proposes a Simultaneous Self and Relative Localization （SSRL） method that can parallelly perform self-localization and relative-localization of collaborative swarm. A probability graph model is constructed with probability constraint relationships to describe the probability relationship between self-localization states， relative localization states， and swarm measurements. Moreover， Gaussian information form is used to estimate the marginal probability of localization states in the graph model via an efficient approach. The experimental results show that SSRL can not only achieve simultaneous estimation of self-localization and relative-localization， but also has better estimation accuracy than traditional cooperative localization methods. The self-localization error and relative-localization error are reduced by 59.2% and 30.3%， respectively， indicating that SSRL provides a new fusion framework for swarm cooperative localization systems.

Key words: unmanned swarm, cooperative localization, graph model, relative-localization, self-localization

CLC Number:

V249.3

Jun XIONG, Xiangpeng XIE, Zhi XIONG, Yuan ZHUANG, Yu ZHENG. Synchronized self⁃localization and relative⁃localization of unmanned swarms based on graph model[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(S2): 729708-729708.

Figures/Tables 12

Fig.1

Table 1

Symbol definition

符号	定义
$R$	无人载体集合
$L$	地标集合
$C i (t)$	t时刻与载体i有量测关系的其他载体集合
$s i (t)$	t时刻载体i的自定位状态（节点）
$r i j (t)$	t时刻载体i、j的相对定位状态（节点）
$u ˜ i (t)$	t时刻载体i的自身运动量测
$z ˜ i j (t)$	t时刻载体i获得相对于其他载体、地标j的量测
$M i t$	t时刻载体i的运动量测功能节点
$L l j t$	t时刻载体i关于地标j的相对量测功能节点
$F j t$	t时刻由载体j自定位状态构成的量测功能节点
$C i j t$	t时刻载体i、j相对运动约束功能节点
$R i j t$	t时刻载体i、j相对量测功能节点
$η$	信息向量
$Λ$	信息矩阵
$J$	雅各比矩阵
$m s i t → X$	由状态节点 $s i (t)$ 广播至因子 $X$ 的消息
$m X → s i t$	状态节点 $s i (t)$ 接收的来自因子 $X$ 的消息

Table 1

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References 21

1	OUYANG X F， ZENG F L， LV D Q， et al. Cooperative navigation of UAVs in GNSS-denied area with colored RSSI measurements［J］. IEEE Sensors Journal， 2021， 21（2）： 2194-2210.
2	NOH Y， YAMAGUCHI H， LEE U. Infrastructure-free collaborative indoor positioning scheme for time-critical team operations［J］. IEEE Transactions on Systems， Man， and Cybernetics： Systems， 2018， 48（3）： 418-432.
3	徐博，王权达，李盛新，等. 基于马氏距离结合自适应滤波的协同定位方法［J］. 中国惯性技术学报， 2021， 29（5）： 617-624.
	XU B， WANG Q D， LI S X， et al. Cooperative location method based on Mahalanobis distance and adaptive filter［J］. Journal of Chinese Inertial Technology， 2021， 29（5）： 617-624 （in Chinese）.
4	HUANG Y L， ZHANG Y G， XU B， et al. A new adaptive extended Kalman filter for cooperative localization［J］. IEEE Transactions on Aerospace and Electronic Systems， 2018， 54（1）： 353-368.
5	陈明星，熊智，刘建业，等. 基于因子图的无人机集群分布式协同导航方法［J］. 中国惯性技术学报， 2020， 28（4）： 456-461.
	CHEN M X， XIONG Z， LIU J Y， et al. Distributed cooperative navigation method of UAV swarm based on factor graph［J］. Journal of Chinese Inertial Technology， 2020， 28（4）： 456-461 （in Chinese）.
6	WU X W， XIAO B， WU C H， et al. Factor graph based navigation and positioning for control system design： A review［J］. Chinese Journal of Aeronautics， 2022， 35（5）： 25-39.
7	XU H Y， WANG C C， BO Y M， et al. An aerial and ground multi-agent cooperative location framework in GNSS-challenged environments［J］. Remote Sensing， 2022， 14（19）： 5055.
8	WYMEERSCH H， LIEN J， WIN M Z. Cooperative localization in wireless networks［J］. Proceedings of the IEEE， 2009， 97（2）： 427-450.
9	XIONG J， XIONG Z， DING Y M， et al. Multihypothesis Gaussian belief propagation for radio ranging-based localization and mapping［J］. IEEE Transactions on Instrumentation and Measurement， 2022， 71： 1-13.
10	RANGAN S， SCHNITER P， FLETCHER A K. Vector approximate message passing［C］∥ 2017 IEEE International Symposium on Information Theory （ISIT）. Piscataway： IEEE Press， 2017： 1588-1592.
11	王小刚. 非线性滤波方法在无人机相对导航上的应用研究［D］. 哈尔滨：哈尔滨工业大学， 2010.
	WANG X G. Research on application of nonlinear filtering method in relative navigation of UAV［D］.Harbin： Harbin Institute of Technology， 2010 （in Chinese）.
12	CHAKRABORTY A， SHARMA R， BRINK K. Cooperative localization for multi-rotor UAVs［C］∥ Proceedings of the AIAA Scitech 2019 Forum. Reston： AIAA，2019.
13	熊骏，熊智，于永军，等. 超宽带测距辅助的无人机近距离相对导航方法［J］. 中国惯性技术学报， 2018， 26（3）： 346-351.
	XIONG J， XIONG Z， YU Y J， et al. Close relative navigation algorithm for unmanned aerial vehicle aided by UWB relative measurement［J］. Journal of Chinese Inertial Technology， 2018， 26（3）： 346-351 （in Chinese）.
14	DE PONTE MÜLLER F. Survey on ranging sensors and cooperative techniques for relative positioning of vehicles［J］. Sensors， 2017， 17（2）： 271.
15	WU H， WU P F， SHI Z S， et al. An aerial ammunition ad hoc network collaborative localization algorithm based on relative ranging and velocity measurement in a highly-dynamic topographic structure［J］. Defence Technology， 2023， 25： 231-248.
16	XU H， WANG L Q， ZHANG Y C， et al. Decentralized visual-inertial-UWB fusion for relative state estimation of aerial swarm［C］∥ 2020 IEEE International Conference on Robotics and Automation （ICRA）. Piscataway： IEEE Press， 2020： 8776-8782.
17	SUDDERTH E B， IHLER A T， ISARD M， et al. Nonparametric belief propagation［J］. Communications of the ACM， 2010， 53（10）： 95-103.
18	ORTIZ J， EVANS T， DAVISON A J. A visual introduction to gaussian belief propagation ［DB/OL］. arXiv preprint： 2107.02308， 2021.
19	DAVISON A J， ORTIZ J. Future mapping 2： Gaussian belief propagation for spatial AI ［DB/OL］. arXiv preprint：1910.14139， 2019.
20	LEUNG K Y， HALPERN Y， BARFOOT T D， et al. The UTIAS multi-robot cooperative localization and mapping dataset［J］. The International Journal of Robotics Research， 2011， 30（8）： 969-974.
21	ALAM N， DEMPSTER A G. Cooperative positioning for vehicular networks： Facts and future［J］. IEEE Transactions on Intelligent Transportation Systems， 2013， 14（4）： 1708-1717.

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[2]	Ding WANG, Jiexin YIN, Xinguang ZHANG, Na’e ZHENG. A TDOA/FDOA cooperative localization method for multiple disjoint sources based on weighted multidimensional scaling analysis [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(7): 327105-327105.
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[6]	LU Hu, JIANG Xiaoqiang, MIN Huan. Distributed SOR multi-agent trajectory estimation method with communication constraints [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(10): 323056-323056.
[7]	Chen Hao;Li Jun;Jing Ning;Liu Xianghui;Tang Yu. Scheduling Model and Algorithms for AutonomousElectromagnetic Detection Satellites [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2010, 31(5): 1045-1053.
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Synchronized self⁃localization and relative⁃localization of unmanned swarms based on graph model

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