AGV Positioning Based on Multi-sensor Data Fusion

Wengliang Zhu; Yunpeng Zhou; Junjie Huang; Shukai Guo

doi:doi:10.11648/j.mcs.20220706.13

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AGV Positioning Based on Multi-sensor Data Fusion

Wengliang Zhu, Yunpeng Zhou, Junjie Huang, Shukai Guo

Published in Mathematics and Computer Science (Volume 7, Issue 6)

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

[1]	Chen Yuanyuan, Chen Jing, Zhang Shouxing. Discussion on the Research Status of AGV Navigation Technology [J]. Mechanical Management Development, 2020, 35 (05): 249-250.
[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.
[13]	Yongqi Chen, ShengXin Lin, Wenxiang Lin et al, Indoor Mobile Robot Study based on ROS [J] Journal of Dongguan Institute of Technology, 2021, 28 (3): p 33-39.
[14]	Wen-liang Zhu, Fu-sheng Ni, Cao-gen Hong. “An Algorithm for the Welding Torch Weaving Control of Arc Welding Robot”, Elektronika ir elektrotechnika, issn 1392-1215, 2015, 21 (2): 3-9.
[15]	Dongjie Wang, Yeyuguang Peng, Design ofTrackless AGV Navigation Software design for Android Terminal [J], Automation and Instrumentation, 2021, (6): p 111-115.
[16]	Ma Yuewu, Wang Minjie, Jia Dong, Luo Yangjing, Wang Haitao. Design of Qt based heterogeneous sensor information fusion display [J]. Foreign Electronic Measurement Technology, 2022, 41 (09): 176-182.

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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

Wengliang Zhu

School of Mechanical Engineering, Jiangsu Ocean University, Lianyungang, China
Yunpeng Zhou

School of Mechanical Engineering, Jiangsu Ocean University, Lianyungang, China
Junjie Huang

School of Mechanical Engineering, Jiangsu Ocean University, Lianyungang, China
Shukai Guo

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

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Plain Text BibTeX RIS

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download