Slid parameter estimates for tracked vehicles with trajectory prediction compensation

LI Rui; LI Chunming; SU Jie; CHEN Liang; QIN Zhaobo; BIAN Yougang

doi:10.16511/j.cnki.qhdxxb.2021.22.020

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Journal of Tsinghua University(Science and Technology) ›› 2022, Vol. 62 ›› Issue (1) : 133-140. DOI: 10.16511/j.cnki.qhdxxb.2021.22.020

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Slid parameter estimates for tracked vehicles with trajectory prediction compensation

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Abstract

Accurate motion models for tracked vehicles are difficult to build due to the complex interactions between the tracks and the terrain. A dual-layer unscented Kalman filter (DUKF) is developed to estimate the slip parameters in real time for tracked vehicles. An upper-layer unscented Kalman filter (UKF) is used first to estimate the slip parameters based on the historical trajectory information, which are then imported into the vehicle model based on the instantaneous centers of rotation (ICRs) to predict the forward trajectory. A lower-layer UKF is then used to correct the preliminarily estimated slip parameters based on the residual position of the trajectory prediction. The effectiveness of the DUKF is verified by simulations on RecurDyn and MATLAB/Simulink. The simulations show that the DUKF improves the accuracy of the slip parameter estimation and reduces the trajectory prediction errors with curvatures compared with predictions using the UKF and the extended Kalman filter (EKF).

Key words

tracked vehicle / slip parameter / unscented Kalman filter (UKF) / trajectory prediction / instantaneous center of rotation (ICR)

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LI Rui, LI Chunming, SU Jie, CHEN Liang, QIN Zhaobo, BIAN Yougang. Slid parameter estimates for tracked vehicles with trajectory prediction compensation[J]. Journal of Tsinghua University(Science and Technology). 2022, 62(1): 133-140 https://doi.org/10.16511/j.cnki.qhdxxb.2021.22.020

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