Hostname: page-component-848d4c4894-wg55d Total loading time: 0 Render date: 2024-04-30T10:37:19.111Z Has data issue: false hasContentIssue false
  • English
  • Français

Process design and network shape evaluation of multi-target collaborative navigation

Published online by Cambridge University Press:  05 May 2021

Rui Liu ,
Klaus Greve ,
Nan Jiang  and
Pengyu Cui
Rui Liu*
Affiliation:
Department of Geography, University of Bonn, Bonn, Germany
Klaus Greve
Affiliation:
Department of Geography, University of Bonn, Bonn, Germany
Nan Jiang
Affiliation:
Department of Geography, University of Bonn, Bonn, Germany
Pengyu Cui
Affiliation:
Department of Geography, University of Bonn, Bonn, Germany
*
*Corresponding author. E-mail: giserlr@outlook.com
Get access Rights & Permissions [Opens in a new window]

Abstract

The spatial distribution of collaborative targets and the information collaboration process are two important factors affecting the efficiency of real-time collaborative navigation. Addressing these factors, this paper presents the following work. First, the collaborative communication process between navigation targets is designed and illustrated with an application example. Second, the feature and error condition of the spatial distribution of collaborative targets is analysed. Then, a method based on CGDOP (collaborative geometric dilution of precision) value is proposed for the evaluation of the actual spatial distribution conditions of collaborative targets. Finally, a simulated experiment is conducted to evaluate the collaborative navigation process and the collaboration effect of the collaborative navigation network in different spatial shapes. Overall, the results of this study optimised the observation and application efficiency of navigation data, and improved the stability and reliability of real-time navigation service through multi-target collaborative navigation.

Keywords

communicationsintegrated navigation system (INS)multi-sensor navigation
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 5 , September 2021 , pp. 968 - 984
Copyright
Copyright © The Royal Institute of Navigation 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Cui, J., Wang, Z., Zhang, C. and Zhang, Y. (2017). Localization algorithm based on factor graph and hybrid message passing for wireless networks. Journal of Computer Applications, 37(5), 1306–1310.Google Scholar
DARPA (2016). DARPA's Collaborative Operations in Denied Environment (CODE) Phase 2 Concept Video. Unmanned Systems Technology. Available at: https://www.unmannedsystemstechnology.com/video/darpas-collaborative-operations-in-denied-environment-code-phase-2-concept-video/Google Scholar
Fankhauser, P., Bloesch, M., Krüsi, P., Diethelm, R., Wermelinger, M., Schneider, T., Dymczyk, M., Hutter, M. and Siegwart, R. (2016). Collaborative Navigation for Flying and Walking Robots. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea.CrossRefGoogle Scholar
Flemisch, F., Canpolat, Y. and Altendorf, E. (2017). Shared and Cooperative Control of Ground and Air Vehicles: Introduction and General Overview. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, Canada.CrossRefGoogle Scholar
Frost, A. (2019). Mitsubishi and Here develop lane-level V2V hazard warning system. Traffic Technology Today. Available at: https://www.traffictechnologytoday.com/news/connected-vehicles-infrastructure/mitsubishi-and-here-develop-lane-level-v2v-hazard-warning-system.htmlGoogle Scholar
Gao, C., Zhao, G., and Fourati, H. (2020). Cooperative Localization and Navigation: Theory, Research, and Practice. Boca Raton, FL: CRC Press, Taylor & Francis Group.Google Scholar
González-García, J., Gómez-Espinosa, A., Cuan-Urquizo, E., Govinda García-Valdovinos, L., Salgado-Jiménez, T. and Arturo Escobedo Cabello, J. (2020). Autonomous underwater vehicles: Localization, navigation, and communication for collaborative missions. Applied Sciences, 10(4), 1256.CrossRefGoogle Scholar
Kassas, Z. M. (2014). Analysis and Synthesis of Collaborative Opportunistic Navigation Systems. Austin, TX: The University of Texas at Austin.Google Scholar
Kealy, A., Alam, N., Toth, C., Moore, T., Gikas, V., Danezis, C., Roberts, G.W., Retscher, G., Hasnur-Rabiain, A., Grejner-Brzezinska, D.A., Hill, C., Hide, C. and Bonenberg, L. (2012). Collaborative Navigation with Ground Vehicles and Personal Navigators. 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sydney, NSW, Australia.CrossRefGoogle Scholar
Lassoued, K., Fantoni, I. and Bonnifait, P. (2015). Mutual Localization and Positioning of Vehicles Sharing GNSS Pseudoranges: Sequential Bayesian Approach and Experiments. 18th IEEE International Conference on Intelligent Transportation Systems (ITSC 2015), Las Palmas, Spain.CrossRefGoogle Scholar
Liu, R., Greve, K., Jiang, N. and Xu, M. (2018). Task-Oriented Path Planning Algorithm Considering POIs and Dynamic Collaborative Targets Distribution. SDF(Sensor Data Fusion: Trends, Solutions, Applications).Google Scholar
Sivaneri, V. O. (2018). UGV-to-UAV Cooperative Ranging for Robust Navigation in GNSS-Challenged Environments. Morgantown, WV: West Virginia University.CrossRefGoogle Scholar
Wierzbanowski, S. (2016). Collaborative Operations in Denied Environment (CODE). U.S. Defense Advanced Research Projects Agency. Available at: https://www.darpa.mil/program/collaborative-operations-in-denied-environmentGoogle Scholar
Xu, B., Bai, J. and Hao, Y. (2015). The research status and progress of cooperative navigation for multiple AUVs. Acta Automatica Sinica, 41(3), 445–461.Google Scholar
Zhong, R. and Chen, Q. (2019). Cooperative positioning method using distance measurement within a cluster and direction finding of a target. Acta Aeronautica et Astronautica Sinica, 41(S1), 723768.Google Scholar