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

Wenliang Zhu; Junjie Huang; Yunpeng Zhou; Chengxiao Zhu

doi:doi:10.11648/j.mcs.20220706.11

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

Wenliang Zhu, Junjie Huang, Yunpeng Zhou, Chengxiao Zhu

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

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

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[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.
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[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.
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[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.
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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

Wenliang Zhu

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

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

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

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

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Plain Text BibTeX RIS

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

Copy | Download

@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}
}

Copy | Download

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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