ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Synchronized self⁃localization and relative⁃localization of unmanned swarms based on graph model
Received date: 2023-10-12
Revised date: 2023-11-14
Accepted date: 2023-12-01
Online published: 2023-12-07
Supported by
National Natural Science Foundation of China(62203228);Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(NY221137);Aeronautical Science Foundation of China(ASFC-2022Z0220X9001);Open Research Fund Program of LIESMARS, Wuhan University （22P01）;China Postdoctoral Science Foundation(2023M742216)
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.
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 AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(S2) : 729708 -729708 . DOI: 10.7527/S1000-6893.2023.29708
| 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. |
/
| 〈 |
|
〉 |
CJA