Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment
Skid-steered wheeled vehicles are commonly adopted in outdoor environments with the benefits of mobility and flexible structure. However, different from Ackerman turning vehicles, skid-steered vehicles do not possess geometric constraint but only dynamic constraint when steered, which leads to motio...
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oai:doaj.org-article:411b31388a154b849d03ea964beeaf162021-11-11T15:23:57ZEstimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment10.3390/app1121103912076-3417https://doaj.org/article/411b31388a154b849d03ea964beeaf162021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10391https://doaj.org/toc/2076-3417Skid-steered wheeled vehicles are commonly adopted in outdoor environments with the benefits of mobility and flexible structure. However, different from Ackerman turning vehicles, skid-steered vehicles do not possess geometric constraint but only dynamic constraint when steered, which leads to motion control and state estimation problems for skid-steered vehicles. The controlling accuracy of a skid-steered vehicle depends largely on feedback state information from sensors and an observer. In this study, a 3-DOF dynamic model using a Brush nonlinear tire model is built, first, to model a 6 × 6 skid-steered wheeled vehicle in flat ground driving conditions. Then, an observer using the unscented Kalman filter with a strong tracking algorithm and adaptive noise matrix adjustment (AN-STUKF) is established to estimate vehicle motion states based on the 3-DOF dynamic model. Finally, the experiment is carried out in three different driving conditions to verify the accuracy and stability of the proposed method. The results show that the AN-STUKF method possesses better accuracy and tracking rate than the traditional UKF, and the phenomenon of ICRs shifting forward of the skid-steered wheeled vehicle is also verified.Xing ZhangShihua YuanXufeng YinXueyuan LiXinyi QuQi LiuMDPI AGarticleskid-steered wheeled vehicleUKFadaptive noise matrixstrong trackingICRsTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10391, p 10391 (2021) |
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skid-steered wheeled vehicle UKF adaptive noise matrix strong tracking ICRs Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
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skid-steered wheeled vehicle UKF adaptive noise matrix strong tracking ICRs Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Xing Zhang Shihua Yuan Xufeng Yin Xueyuan Li Xinyi Qu Qi Liu Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment |
description |
Skid-steered wheeled vehicles are commonly adopted in outdoor environments with the benefits of mobility and flexible structure. However, different from Ackerman turning vehicles, skid-steered vehicles do not possess geometric constraint but only dynamic constraint when steered, which leads to motion control and state estimation problems for skid-steered vehicles. The controlling accuracy of a skid-steered vehicle depends largely on feedback state information from sensors and an observer. In this study, a 3-DOF dynamic model using a Brush nonlinear tire model is built, first, to model a 6 × 6 skid-steered wheeled vehicle in flat ground driving conditions. Then, an observer using the unscented Kalman filter with a strong tracking algorithm and adaptive noise matrix adjustment (AN-STUKF) is established to estimate vehicle motion states based on the 3-DOF dynamic model. Finally, the experiment is carried out in three different driving conditions to verify the accuracy and stability of the proposed method. The results show that the AN-STUKF method possesses better accuracy and tracking rate than the traditional UKF, and the phenomenon of ICRs shifting forward of the skid-steered wheeled vehicle is also verified. |
format |
article |
author |
Xing Zhang Shihua Yuan Xufeng Yin Xueyuan Li Xinyi Qu Qi Liu |
author_facet |
Xing Zhang Shihua Yuan Xufeng Yin Xueyuan Li Xinyi Qu Qi Liu |
author_sort |
Xing Zhang |
title |
Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment |
title_short |
Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment |
title_full |
Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment |
title_fullStr |
Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment |
title_full_unstemmed |
Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment |
title_sort |
estimation of skid-steered wheeled vehicle states using stukf with adaptive noise adjustment |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doaj.org/article/411b31388a154b849d03ea964beeaf16 |
work_keys_str_mv |
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