CAO Yuan, ZHANG Shuangshuang, YANG Guizhi, et al., “Research on High Precision Tracking Method of Guided Transport Vehicle Based on Autonomous Combination Positioning,” Chinese Journal of Electronics, vol. 29, no. 4, pp. 779-785, 2020, doi: 10.1049/cje.2020.06.006
Citation: CAO Yuan, ZHANG Shuangshuang, YANG Guizhi, et al., “Research on High Precision Tracking Method of Guided Transport Vehicle Based on Autonomous Combination Positioning,” Chinese Journal of Electronics, vol. 29, no. 4, pp. 779-785, 2020, doi: 10.1049/cje.2020.06.006

Research on High Precision Tracking Method of Guided Transport Vehicle Based on Autonomous Combination Positioning

doi: 10.1049/cje.2020.06.006
Funds:  This work is supported by the National Key R&D Plan (No.2018YFB1201601).
  • Received Date: 2020-01-02
  • Rev Recd Date: 2020-03-08
  • Publish Date: 2020-07-10
  • Guided transport vehicle (GTV) uses satellite positioning technology, lidar, and highperformance video sensors to achieve real-time positioning and tracing, these devices cost a lot. In addition, GTV shifts left and right during operation due to there is no fixed orbits constraint. It is very important to detect the lateral offset in real time to ensure the accurate tracing of the vehicle along the dotted line. Aiming at the above issues, this paper proposes a low-cost high precision tracing method for GTV based on integrated positioning, high resolution estimation of the vehicles positioning information is implemented by Cubature Kalman filters (CKF) with a loosely coupled mode based on GPS/SINS in this method; Considering that GTV has a fixed driving route, a two-stage map matching algorithm based on Hidden Markov model (HMM) is proposed, which further improves the accuracy; The lateral offset distance is detected based on the positioning information, which provides theoretical support for the subsequent control of the precise operation of the vehicle. The algorithm's feasibility has been verified by real vehicle experiments, the results show that the proposed algorithm achieves a centimeter-level positioning accuracy, and when the lateral offset distance is not less than 65cm, the detection accuracy of the lateral offset distance is more than 92%.
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  • Y. Cao, L. Ma and S. Xiao, et al., “Standard analysis for transfer delay in CTCS-3”, Chinese Journal of Electronics, Vol.26, No.5, pp.1057-1063, 2017.
    R. Jung and R. Kelber, “A lane departure warning system using lateral offset with uncalibrated camera”, Proc. 8th Int. IEEE Conf. Intell. Transp. Syst., No.17, pp.348-353, 2005.
    B. Murphy and L. Morgan, “A satellite-based position location system for global data collection and messaging”, Proceedings of MILCOM'93-IEEE Military Communications Conference, DOI:10.1109/MILCOM.1993.408642, 1993.
    K. Nam, W. Lee, M.Hyeok. Lee, et al., “Application of fuzzy predictive control technology in automatic train operation”, IEEE Transactions on Industrial Electronics, Vol.65, No.7, pp.5673-5681, 2018.
    Joshi and R. James, “Generation of accurate lane-level maps from coarse prior maps and lidar”, IEEE Intell. Transp. Syst. Mag., Vol.7, No.1, pp.19-29, 2015.
    M. Rafael, D. Betaille and F. Peyret, “Lane-level integrity provision for navigation and map matching with GNSS, dead reckoning, and enhanced maps”, IEEE Transactions on Intelligent Transportation Systems, Vol.11, No.1, pp.100-112, 2010.
    M. Rohani, D. Gingras and D. Gruyer, “A novel approach for improved vehicular positioning using cooperative map matching and dynamic base station DGPS concept”, IEEE Transac-tions on Intelligent Transportation Systems, Vol.17, No.1, pp.230-239, 2016.
    S. Choy, J. Kuckartz, Andrew G. Dempster,et al., “GNSS satellite-based augmentation systems for Australia”, GPS Solutions, Vol.21, No.3, pp.835-848, 2017.
    K. M. Ng, J. Johari, S. A. C. Abdullah, et al., “Performance evaluation of the RTK-GNSS navigating under different landscape”, 18th In-ternational Conference on Control, Automation and Systems, 2018.
    Y. Cao, H. Lu and T. Wen, “A safety computer system based on multi-sensor data processing”, Sensors, Vol.19, No.4, 2019.
    Y. Z. Zhang, Y. Cao, Y. H. Wen, et al.,“Optimization of information interaction protocols in cooperative vehicleinfrastructure systems”, Chinese Journal of Electronics, Vol.27, No.2, pp.439-444, 2018.
    X. Zhang, F. Ding, X. Wang, et al.,“Recursive parameter identification of the dynamical models for bilinear state space systems”, Non-linear Dynamics, Vol.89, No.4, pp.2415-2429, 2017.
    X. Zhang, L. Xu, F. Ding, et al.,“Combined state and parameter estimation for a bilinear state space system with moving average noise”, Journal of the Franklin Institute, Vol.355, No.6, pp.3079-3103, 2018.
    L. Li, D. Liang, Y. Wang, et al., “Application of particle filter combined with RBF NN in indoor positioning”, Computer Engineering and Design, Vol.38, No.9, pp.2509-2514, 2017.
    Y. Cao, Y. Zhang, T. Wen,et al.,“Research on dynamic nonlinear input prediction of fault diagnosis based on fractional differential operator equation in high-speed train control system”, Chaos, https://doi.org/10.1063/1.5085397, 2019.
    S. Baek, C. Liu, P. Watta, et al.,“Accurate vehicle position estimation using a Kalman filter and neural networkbasedapproach”, 2017 IEEE Symposium Series on Computational Intelligence (SSCI), DOI: 10.1109/SSCI.2017.8285360, 2017.
    Y. Cao, L. Ma and Y. Zhang, “Application of fuzzy predictive control technology in automatic train operation”, Cluster Computing, https://doi.org/10.1007/s10586-018-2258-0, 2018.
    H. Khazraj, F. Silva and L. Claus, “A performance comparison between extended Kalman filter and unscented Kalman filter in power system dynamic state estimation”, 201651st Inter-national Universities Power Engineering Conference (UPEC), DOI: 10.1109/UPEC.2016.8114125, 2016.
    J. Ivan, Z. Zarko and B. Krstajic, “State-of-charge estimation of lithium-ion batteries using extended Kalman filter andunscented Kalman filter”, 2018 23rd International ScientificProfessional Conference on Information Technolohy (IT), DOI:10.1109/SPIT.2018.8350462, 2018.
    M. Rampelli and D. Jena, “Advantage of unscented Kalman filter over extended Kalman filter in dynamic state estimation of power system network”, Michael Faraday IET International Summit 2015. (1), DOI: 10.1049/cp.2015.1644, 2015.
    I. Arasaratnam and S. Haykin, “Cubature Kalman filters”, IEEE Transactions on Auto-matic Control, Vol.54, No.6, pp.1254-1269, 2009.
    Y. Cao, Y. Wen, Y. Zhang, et al., “Performance evaluation with improved receiver design for asynchronous coordinated multi-point transmissions”, Chinese Journal of Electronics, Vol.25, No.2, pp.372-378, 2016.
    H. Zhang, “Research on weight-based long-term interval map matching algorithm”, MA.Eng., Jilin University, China, 2018.
    J. Yuan, Y. Zheng, C. Y. Zhang, et al., “An interactivevoting based map matching algorithm”, Proceedings of the 2010 Eleventh International Conference on Mobile Data Management, DOI:10.1109/MDM.2010.14, 2010.
    R. Raymond, T. Morimura, T. Osogami, et al., “Map matching with hidden Markov model on sampled road network”, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp.2242-2245, 2012.
    L. Huang, J. Song, Y. Feng, et al., “Analysis of methylation and -141C Ins/Del polymorphisms of the dopamine receptor D2 gene in patients with schizophrenia”, Optik-International Journal for Light and Optics, No.172, pp.484-493, 2018.
    K.W. Chiang, G. J. Tsai, H.W. Chang, et al., “Seamless navigation and mapping using an INS/GNSS/grid-based SLAM semi-tightly coupled integration scheme”, Information Fusion, No.50, pp.181-196, 2019.
    R.V. Garcia, P. C. Pardal, P. C. P. M, et al., “Nonlinear filtering for sequential spacecraft attitude estimation with real data: Cubature Kalman filter, unscented Kalman filter and extended Kalman filter”, Advances in Space Research, Vol.63, No.2, pp.1038-1050, 2019.
    J. Sasiadek, Q. Wang and M. Zaremba,“INS/GPS navigation data fusion using fuzzy adaptive Kalman filtering”, IFAC Proceedings Volumes, Vol.33, No.27, pp.423-428, 2017.
    H. Benzerrouk, A. Nebylov and H. Salhi, “Quadrotor UAV state estimation based on high-degree cubature Kalman filter”, IFAC-Papers OnLine, DOI: 10.1109/MITS.2019.2907681, 2016.
    S. Wang, J. Feng and T. Chik, “Novel cubature Kalman filtering for systems involving nonlinear states and linear measurements”, AEU-International Journal of Electronics and Communications, Vol.69, No.1, pp.314-320, 2015.
    Y. Cao, Y. Sun, G. Xie, et al., “Fault diagnosis of train plug door based on a hybrid criterion for IMFs selection and fractional wavelet package energy entropy”, IEEE Transactions on Vehicular Technology(2019), DOI:10.1109/TVT.2019.2925903, 2019.
    Shuai Su, Tao Tang, Jing Xun, et al., “Design of running grades for energy-efficient train regulation: A case study for Beijing Yizhuang Line”, IEEE Intelligent Transportation Systems Magazine, DOI: 10.1109/MITS.2019.2907681, 2016.
    Y. Cao, Z. Wang and F. Liu, “Bio-inspired speed curve optimization and sliding mode tracking control for subway trains”, IEEE Transactions on Vehicular Technology, DOI:10.1109/TVT.2019.2914936, 2019.
    Y. Chen, M. S. Illindala, A. S. Khalsa, et al., “Modified Viterbi Algorithm Based Distribution System Restoration Strategy for Grid Resiliency”, IEEE Transactions on Power Delivery, Vol.32, No.1, pp.310-319, 2016.
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