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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Main Authors:	Xing Zhang, Shihua Yuan, Xufeng Yin, Xueyuan Li, Xinyi Qu, Qi Liu
Format:	article
Language:	EN
Published:	MDPI AG 2021
Subjects:	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
Online Access:	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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Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment

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