| Peer-Reviewed

Mobile Robot Positioning System of Adaptive Unscented Kalman Filter with Forgetting Factor

Received: 6 November 2022     Accepted: 21 November 2022     Published: 30 November 2022
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Abstract

For the problem of inaccurate positioning of mobile robots in complex industrial environments, a multi-sensor combination localization method for omnidirectional mobile robots is proposed that incorporates the unscented Kalman filter (UKF), Real-Time Kinematic (RTK), and Inertial Measurement Unit (IMU). Firstly, the position information of the mobile robot is obtained by Real-Time Kinematic (RTK) and Wheel Odometry respectively. Secondly, the inertial measurement unit (IMU) determines the cart yaw angle while dual RTK is proposed to solve the yaw angle in real-time. Finally, the position and yaw angle data are input to the unscented Kalman filter in real time. This paper proposes the F-AUKF algorithm, which optimizes the traditional unscented Kalman filter algorithm by introducing a forgetting factor in order to improve the robustness of mobile robot localization for continuous operation in complex industrial building environments. The experimental results show that the F-AUKF algorithm eventually achieves a positioning accuracy about 10 times higher than that of a single odometer, about 6 times higher than that of a single RTK and about 3 times higher than that of the traditional UKF algorithm, effectively improving the problem of dispersion of the filtering effect after a long period of operation and providing better stability.

Published in Mathematics and Computer Science (Volume 7, Issue 6)
DOI 10.11648/j.mcs.20220706.11
Page(s) 106-112
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

Outdoor Mobile Robots, Multi-sensor Fusion Positioning, F-AUKF, Forgetting Factor

References
[1] Jun, F. U., X. U. Da, and F. U. Yang. "Application of an adaptive UKF in UWB indoor positioning." Bulletin of Surveying and Mapping (2019). doi: 10.1109/CAC48633.2019.8996692.
[2] J. Sun, L. Tao, Z. Niu and B. Zhu, "An Improved Adaptive Unscented Kalman Filter With Application in the Deeply Integrated BDS/INS Navigation System," in IEEE Access, vol. 8, pp. 95321-95332, 2020. doi: 10.1109/ ACCESS.2020.299574.
[3] Yuan, J., Luo, H., Yu, L., Luo, M., & Shi, J. J.. (2020). Performance assessment of single frequency gnss rtk/mems-imu combined positioning. Journal of Physics Conference Series, 1544, 012166. doi: 10.1088/1742-6596/1544/1/012166.
[4] Yuan, D., Zhang, J., Wang, J., Cui, X., Liu, F., & Zhang, Y.. (2021). Robustly adaptive ekf pdr/uwb integrated navigation based on additional heading constraint. Sensors (Basel, Switzerland), 21 (13). doi: 10.3390/s21134390.
[5] Zhan, M., & Xi, Z. H.. (2020). Indoor location method of wifi / pdr fusion based on extended kalman filter fusion. Journal of Physics Conference Series, 1601, 042004. doi: 10.1088/1742-6596/1601/4/042004.
[6] Hu, F., & Wu, G.. (2020). Distributed error correction of ekf algorithm in multi-sensor fusion localization model. IEEE Access, PP (99), 1-1. doi: 10.1109/ACCESS.2020.2995170.
[7] El Din, M. S., Hussein, A. A., & Abdel-Hafez, M. F.. (2018). Improved battery soc estimation accuracy using a modified ukf with an adaptive cell model under real ev operating conditions. IEEE Transactions on Transportation Electrification, 1-1. doi: 10.1109/TTE.2018.2802043.
[8] J. Feng, F. Cai, J. Yang, S. Wang and K. Huang, "An Adaptive State of Charge Estimation Method of Lithium-ion Battery Based on Residual Constraint Fading Factor Unscented Kalman Filter," in IEEE Access, vol. 10, pp. 44549-44563, 2022. doi: 10.1109/ACCESS.2022.3170093.
[9] Zhuang, J. H. (2015). Research on GPS precise positioning system based on real-time dynamic differencing of carrier phase. Unpublished master’s thesis, Harbin University of Science and Technology, Harbin.
[10] Moore, T.. (2019). Understanding gps/gnss: principles and applications, third edition. The Aeronautical journal (123-Aug. TN. 1266).
[11] Yang Cheng, Shi Wenzhong, Chen Wu. (2016). Comparison of Unscented and Extended Kalman Filters with Application in Vehicle Navigation. Journal of Navigation, 70 (2): 411-431. doi: 10.1017/S0373463316000655.
[12] Madhukar, P. S., & Prasad, L. B.. (2020). State Estimation using Extended Kalman Filter and Unscented Kalman Filter. 2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3). doi: 10.1109/ICONC345789.2020.9117536.
[13] Kolakowski, M.. (2020). Comparison of Extended and Unscented Kalman Filters Performance in a Hybrid BLE-UWB Localization System. 2020 23rd International Microwave and Radar Conference (MIKON). doi: 10.23919/MIKON48703.2020.9253854.
[14] Sever, M., & Hajiyev, C.. (2021). Gnss signal processing with ekf and ukf for stationary user position estimation. WSEAS Transactions on Signal Processing (17-), 17. doi: 10.37394/232014.2021.17.10.
[15] Allotta, B., Chisci, L., Costanzi, R., Fanelli, F., Fantacci, C., & Meli, E., et al. (2015). A comparison between EKF-based and UKF-based navigation algorithms for AUVs localization. Oceans (pp. 1-5). IEEE. doi: 10.1109/OCEANS-Genova.2015.7271681.
Cite This Article
  • APA Style

    Wenliang Zhu, Junjie Huang, Yunpeng Zhou, Chengxiao Zhu. (2022). Mobile Robot Positioning System of Adaptive Unscented Kalman Filter with Forgetting Factor. Mathematics and Computer Science, 7(6), 106-112. https://doi.org/10.11648/j.mcs.20220706.11

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    ACS Style

    Wenliang Zhu; Junjie Huang; Yunpeng Zhou; Chengxiao Zhu. Mobile Robot Positioning System of Adaptive Unscented Kalman Filter with Forgetting Factor. Math. Comput. Sci. 2022, 7(6), 106-112. doi: 10.11648/j.mcs.20220706.11

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    AMA Style

    Wenliang Zhu, Junjie Huang, Yunpeng Zhou, Chengxiao Zhu. Mobile Robot Positioning System of Adaptive Unscented Kalman Filter with Forgetting Factor. Math Comput Sci. 2022;7(6):106-112. doi: 10.11648/j.mcs.20220706.11

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  • @article{10.11648/j.mcs.20220706.11,
      author = {Wenliang Zhu and Junjie Huang and Yunpeng Zhou and Chengxiao Zhu},
      title = {Mobile Robot Positioning System of Adaptive Unscented Kalman Filter with Forgetting Factor},
      journal = {Mathematics and Computer Science},
      volume = {7},
      number = {6},
      pages = {106-112},
      doi = {10.11648/j.mcs.20220706.11},
      url = {https://doi.org/10.11648/j.mcs.20220706.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20220706.11},
      abstract = {For the problem of inaccurate positioning of mobile robots in complex industrial environments, a multi-sensor combination localization method for omnidirectional mobile robots is proposed that incorporates the unscented Kalman filter (UKF), Real-Time Kinematic (RTK), and Inertial Measurement Unit (IMU). Firstly, the position information of the mobile robot is obtained by Real-Time Kinematic (RTK) and Wheel Odometry respectively. Secondly, the inertial measurement unit (IMU) determines the cart yaw angle while dual RTK is proposed to solve the yaw angle in real-time. Finally, the position and yaw angle data are input to the unscented Kalman filter in real time. This paper proposes the F-AUKF algorithm, which optimizes the traditional unscented Kalman filter algorithm by introducing a forgetting factor in order to improve the robustness of mobile robot localization for continuous operation in complex industrial building environments. The experimental results show that the F-AUKF algorithm eventually achieves a positioning accuracy about 10 times higher than that of a single odometer, about 6 times higher than that of a single RTK and about 3 times higher than that of the traditional UKF algorithm, effectively improving the problem of dispersion of the filtering effect after a long period of operation and providing better stability.},
     year = {2022}
    }
    

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  • TY  - JOUR
    T1  - Mobile Robot Positioning System of Adaptive Unscented Kalman Filter with Forgetting Factor
    AU  - Wenliang Zhu
    AU  - Junjie Huang
    AU  - Yunpeng Zhou
    AU  - Chengxiao Zhu
    Y1  - 2022/11/30
    PY  - 2022
    N1  - https://doi.org/10.11648/j.mcs.20220706.11
    DO  - 10.11648/j.mcs.20220706.11
    T2  - Mathematics and Computer Science
    JF  - Mathematics and Computer Science
    JO  - Mathematics and Computer Science
    SP  - 106
    EP  - 112
    PB  - Science Publishing Group
    SN  - 2575-6028
    UR  - https://doi.org/10.11648/j.mcs.20220706.11
    AB  - For the problem of inaccurate positioning of mobile robots in complex industrial environments, a multi-sensor combination localization method for omnidirectional mobile robots is proposed that incorporates the unscented Kalman filter (UKF), Real-Time Kinematic (RTK), and Inertial Measurement Unit (IMU). Firstly, the position information of the mobile robot is obtained by Real-Time Kinematic (RTK) and Wheel Odometry respectively. Secondly, the inertial measurement unit (IMU) determines the cart yaw angle while dual RTK is proposed to solve the yaw angle in real-time. Finally, the position and yaw angle data are input to the unscented Kalman filter in real time. This paper proposes the F-AUKF algorithm, which optimizes the traditional unscented Kalman filter algorithm by introducing a forgetting factor in order to improve the robustness of mobile robot localization for continuous operation in complex industrial building environments. The experimental results show that the F-AUKF algorithm eventually achieves a positioning accuracy about 10 times higher than that of a single odometer, about 6 times higher than that of a single RTK and about 3 times higher than that of the traditional UKF algorithm, effectively improving the problem of dispersion of the filtering effect after a long period of operation and providing better stability.
    VL  - 7
    IS  - 6
    ER  - 

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

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

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

  • School of Advanced Technology, Xi'an Jiaotong-liverpool University, Suzhou, China

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