The Localization Method of ALV Based on Lateral Dynamics

Xin  Chen; Min Tao; Xing Lian Yue; Jing Bin Song

doi:10.4028/www.scientific.net/AMM.599-601.1272

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 599-601The Localization Method of ALV Based on Lateral...

The Localization Method of ALV Based on Lateral Dynamics

Abstract:

Localization is a criticalissue for autonomous vehicle navigation. A localization model based on thestable response in lateral dynamics of the vehicle is hard for practicalapplication because of its nonlinearity, multidimensionality and multivariable.A dynamic self - adaptive network, which is able to adjust the scale of theparameters dynamically and has a good regression performance and self learningability, is used as a approximator to modeling the localization of the vehicle.In order to get a higher accuracy in modeling, a Kalman filer is designed forthe input signal such as steering angle and velocity. From the practicaltesting results on our vehicle, it’s indicated that the localization error is less than 1meter. So, the proposed method in this paper is a practicable and efficient onefor application.

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Periodical:

Applied Mechanics and Materials (Volumes 599-601)

Pages:

1272-1275

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.599-601.1272

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Online since:

August 2014

Authors:

Xin Chen*, Min Tao, Xing Lian Yue, Jing Bin Song

Keywords:

Kalman Filtering, Lateral Dynamics of Thevehicle, Localization of ALV, Radius Basic Function Neural Network (RBFNN)

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References

[1] Leonard,J. andDurrant-whyte,H.F., 1991, Mobile robot Localization by Tracking Geometric Beacons., [J] IEEE Transaction on Robotics and Automation, Vol. 7, No. 3, pp.376-382.

DOI: 10.1109/70.88147

Google Scholar

[2] XU Xiao-han, ANG Qing-yin. Emergence of Uncertainty information and its classification[M]. Proceeding of SCT94, Wuhan: Huazhong University Press，1994. 679-682.

Google Scholar

[3] Puneet Goel, Stergios I. Roumeliotis and Gaurav S. Sukhatme. Robust Localization Using Re lative and Absolute Position Estimates[J]. Department of Computer Science Institute for Robotics and Intelligent Systems University of Southern California Los Angeles, CA 90089-0781.

DOI: 10.20965/jrm.1996.p0286

Google Scholar

[4] J. Borenstein and L. Feng. Gyrodommetry: A new method for combining data from gyros and odometry in mobile robots[J]. In Proceedings of the 1996 IEEE International Conference on Robotics and Automation, page 423-428, (1995).

DOI: 10.1109/robot.1996.503813

Google Scholar

[5] Kwang Bo Cho. Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction [J]. Fuzzy Sets and Systems, 1996, 83: 325-339.

DOI: 10.1016/0165-0114(95)00322-3

Google Scholar