Filters navigation and positioning based on mining vehicle motion model

Chen, Yuheng; Wu, Hongyun; Liu, Yongfeng; Li, Jingwei; Yin, Bolin

doi:10.1109/cacre50138.2020.9230004

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…The unscented partial filter (UPF) establishes particle swarm using unscented transformation similar to the UKF to avoid particle degradation [40]. Chu [41] and Chen [42] estimated electric vehicle state using the UPF. Zhu [43] built a modified PF which performed better than the UPF in the sensor fault mode.…”

Section: Vehicle Motion States Estimationmentioning

confidence: 99%

Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment

et al. 2021

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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.

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Section: Vehicle Motion States Estimationmentioning

confidence: 99%