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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Autores principales: Xing Zhang, Shihua Yuan, Xufeng Yin, Xueyuan Li, Xinyi Qu, Qi Liu
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Lenguaje:EN
Publicado: MDPI AG 2021
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Acceso en línea:https://doaj.org/article/411b31388a154b849d03ea964beeaf16
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spelling 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)
institution DOAJ
collection DOAJ
language EN
topic 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
spellingShingle 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 AT xingzhang estimationofskidsteeredwheeledvehiclestatesusingstukfwithadaptivenoiseadjustment
AT shihuayuan estimationofskidsteeredwheeledvehiclestatesusingstukfwithadaptivenoiseadjustment
AT xufengyin estimationofskidsteeredwheeledvehiclestatesusingstukfwithadaptivenoiseadjustment
AT xueyuanli estimationofskidsteeredwheeledvehiclestatesusingstukfwithadaptivenoiseadjustment
AT xinyiqu estimationofskidsteeredwheeledvehiclestatesusingstukfwithadaptivenoiseadjustment
AT qiliu estimationofskidsteeredwheeledvehiclestatesusingstukfwithadaptivenoiseadjustment
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