| Peer-Reviewed

AGV Positioning Based on Multi-sensor Data Fusion

Received: 21 November 2022     Accepted: 5 December 2022     Published: 15 December 2022
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Abstract

In recent years, with the rapid development of robot technology and explosive growth of robot demand, AGV robot has gradually infiltrated into many aspects of human production and life, and has become a global hot research direction. However, due to the changeable and compact working environment, AGV robot still has many technical problems to be solved. The localization of AGV robot is the premise and key for AGV robot to move freely. To address the problem of accumulated error in wheel Odometry positioning and data drift in ultra-wideband (UWB) positioning when positioning AGV robots in an unknown environment, this paper establishes the coordinate system of AGV robots based on an independently built AGV robot motion control system, and combines the advantages and disadvantages of wheel Odometry and UWB positioning sensors, and uses the TEKF algorithm to fuse the positioning data of the two sensors The TEKF algorithm is used to fuse the positioning data of the two sensors in order to improve the positioning accuracy of the AGV robot. The experimental results show that the integrated positioning system of wheel Odometry and UWB can effectively restrain the cumulative error and data drift, and the positioning accuracy of multi-sensor fusion positioning is greatly improved compared with that of a single sensor, providing accurate and reliable positioning data for the motion control of AGV robot.

Published in Mathematics and Computer Science (Volume 7, Issue 6)
DOI 10.11648/j.mcs.20220706.13
Page(s) 118-123
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

AGV, Odometry, UWB, Fusion, TEKF

References
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[2] Liu Mengyun, Chen Ruizhi, Li Deren, al. Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach [J]. MDPI AG, (12): 2847.
[3] J. Chen, S. Song, H. Yu. An indoor multi-source fusion positioning approach based on PDR/MM/WiFi [J]. Aeu - International Journal of Electronics and Communications, 2021, 135: 153733.
[4] Wenbo Pan, Yuanzhengyu Li, Teng Long et al. Study of Multi-sensor Fusion on Intelligent Railway Electrical Vehicles [J], 2022, (2): p 76-83.
[5] Xiaoxu, Min Liu, Ju Zhang et al. Positioning in Indoor Space based a Lidar [J], Electrical Technology in Intelligent Construction, 2022, 16 (1): p 59-62.
[6] Feng Daquan, Wang Chunqi, He Chunlong, et al. Kalman-Filter-Based Integration of IMU and UWB for High-Accuracy Indoor Positioning and Navigation [J]. Institute of Electrical and Electronics Engineers (IEEE), (4): 3133-3146.
[7] Xiaoqin Wei SLAM, Positioning and Navigation Study of the AGV Robot with Lidar [D]. South China University of Technology, 2019.
[8] Chen Zhenjiang, Feng Xiaoyang. Research on the design of omnidirectional mobile autonomous robot based on McNamm wheel [J]. Science and Technology Innovation, 2022, (22): 150-152.
[9] Yan Chenkai, Du Yu, Wu Zhihong. Conceptual design of omnidirectional mobile fire detection robot based on McNamm wheel [J]. Industrial Design, 2022 (06): 158-160.
[10] Zhixim Jin, Hongyuan Wang, Youxu Gou et al, Design and Experiments of Object Tracking Mobile Robots based on ROS [J], Computer Knowledge and Technology, 2021, 17 (2), p 1-3.
[11] Du Xin, Zhu Wenliang, Wen Xiqin, Zhu Jiahao. Research on multisensor fusion positioning method based on ultra wideband communication technology [J]. Science and Technology Innovation, 2022 (07): 5-8.
[12] Ding Yanan, Zhang Xu, Xu Lu. Overview of indoor positioning technology based on UWB [J]. Intelligent Computer and Application, 2019, 9 (05): 91-94.
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Cite This Article
  • APA Style

    Wengliang Zhu, Yunpeng Zhou, Junjie Huang, Shukai Guo. (2022). AGV Positioning Based on Multi-sensor Data Fusion. Mathematics and Computer Science, 7(6), 118-123. https://doi.org/10.11648/j.mcs.20220706.13

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    ACS Style

    Wengliang Zhu; Yunpeng Zhou; Junjie Huang; Shukai Guo. AGV Positioning Based on Multi-sensor Data Fusion. Math. Comput. Sci. 2022, 7(6), 118-123. doi: 10.11648/j.mcs.20220706.13

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    AMA Style

    Wengliang Zhu, Yunpeng Zhou, Junjie Huang, Shukai Guo. AGV Positioning Based on Multi-sensor Data Fusion. Math Comput Sci. 2022;7(6):118-123. doi: 10.11648/j.mcs.20220706.13

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  • @article{10.11648/j.mcs.20220706.13,
      author = {Wengliang Zhu and Yunpeng Zhou and Junjie Huang and Shukai Guo},
      title = {AGV Positioning Based on Multi-sensor Data Fusion},
      journal = {Mathematics and Computer Science},
      volume = {7},
      number = {6},
      pages = {118-123},
      doi = {10.11648/j.mcs.20220706.13},
      url = {https://doi.org/10.11648/j.mcs.20220706.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20220706.13},
      abstract = {In recent years, with the rapid development of robot technology and explosive growth of robot demand, AGV robot has gradually infiltrated into many aspects of human production and life, and has become a global hot research direction. However, due to the changeable and compact working environment, AGV robot still has many technical problems to be solved. The localization of AGV robot is the premise and key for AGV robot to move freely. To address the problem of accumulated error in wheel Odometry positioning and data drift in ultra-wideband (UWB) positioning when positioning AGV robots in an unknown environment, this paper establishes the coordinate system of AGV robots based on an independently built AGV robot motion control system, and combines the advantages and disadvantages of wheel Odometry and UWB positioning sensors, and uses the TEKF algorithm to fuse the positioning data of the two sensors The TEKF algorithm is used to fuse the positioning data of the two sensors in order to improve the positioning accuracy of the AGV robot. The experimental results show that the integrated positioning system of wheel Odometry and UWB can effectively restrain the cumulative error and data drift, and the positioning accuracy of multi-sensor fusion positioning is greatly improved compared with that of a single sensor, providing accurate and reliable positioning data for the motion control of AGV robot.},
     year = {2022}
    }
    

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  • TY  - JOUR
    T1  - AGV Positioning Based on Multi-sensor Data Fusion
    AU  - Wengliang Zhu
    AU  - Yunpeng Zhou
    AU  - Junjie Huang
    AU  - Shukai Guo
    Y1  - 2022/12/15
    PY  - 2022
    N1  - https://doi.org/10.11648/j.mcs.20220706.13
    DO  - 10.11648/j.mcs.20220706.13
    T2  - Mathematics and Computer Science
    JF  - Mathematics and Computer Science
    JO  - Mathematics and Computer Science
    SP  - 118
    EP  - 123
    PB  - Science Publishing Group
    SN  - 2575-6028
    UR  - https://doi.org/10.11648/j.mcs.20220706.13
    AB  - In recent years, with the rapid development of robot technology and explosive growth of robot demand, AGV robot has gradually infiltrated into many aspects of human production and life, and has become a global hot research direction. However, due to the changeable and compact working environment, AGV robot still has many technical problems to be solved. The localization of AGV robot is the premise and key for AGV robot to move freely. To address the problem of accumulated error in wheel Odometry positioning and data drift in ultra-wideband (UWB) positioning when positioning AGV robots in an unknown environment, this paper establishes the coordinate system of AGV robots based on an independently built AGV robot motion control system, and combines the advantages and disadvantages of wheel Odometry and UWB positioning sensors, and uses the TEKF algorithm to fuse the positioning data of the two sensors The TEKF algorithm is used to fuse the positioning data of the two sensors in order to improve the positioning accuracy of the AGV robot. The experimental results show that the integrated positioning system of wheel Odometry and UWB can effectively restrain the cumulative error and data drift, and the positioning accuracy of multi-sensor fusion positioning is greatly improved compared with that of a single sensor, providing accurate and reliable positioning data for the motion control of AGV robot.
    VL  - 7
    IS  - 6
    ER  - 

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Author Information
  • School of Mechanical Engineering, Jiangsu Ocean University, Lianyungang, China

  • School of Mechanical Engineering, Jiangsu Ocean University, Lianyungang, China

  • School of Mechanical Engineering, Jiangsu Ocean University, Lianyungang, China

  • School of Mechanical Engineering, Jiangsu Ocean University, Lianyungang, China

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