Vehicle modeling and state estimation for autonomous driving in terrain
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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2024-11
Major/Subject
Mcode
Degree programme
Language
en
Pages
16
Series
Control Engineering Practice, Volume 152
Abstract
The automobile industry usually ignores the height of the path and uses planar vehicle models to implement automatic vehicle control. In addition, existing literature mostly concerns level terrain or homogeneous road surfaces for estimating vehicle dynamics. However, ground vehicles utilized in forestry, such as forwarders, operate on uneven terrain. The vehicle models built on level terrain assumptions are inadequate to capture the rolling or pitching dynamics of such machines as rollover of such vehicles is a potential risk. Therefore, knowledge about the height profile of the path is crucial for automating such off-road operations and avoiding rollover. We propose the use of a six-degrees-of-freedom (6-DOF) dynamic vehicle model to solve the autonomous forwarder problem. An adaptive linear tire model is used in the 6-DOF model assuming the vehicle operates in a primary handling regime. The force models are modified to include the three-dimensional (3D) map information. The calibration procedures, identifying actuator dynamics, and quantifying sensor delays are also represented. The proposed vehicle modeling contributed to realizing the continuous-discrete extended Kalman filter (CDEKF), which takes into account the 3D path during filtering and fixed-lag smoothing. Polaris (an all-terrain electric car) is used as a case study to experimentally validate the vehicle modeling and performance of the state estimator. Three types of grounds are selected — an asphalt track, a concrete track with a high elevation gradient, and a gravel track inside a forest. Stable state estimates are obtained using CDEKF and sparse 3D maps of terrains despite discontinuities in satellite navigation data inside the forest. The height estimation results are obtained with sufficient accuracy when compared to ground truth obtained by aerial 3D mapping. Finally, the proposed model's applicability for predictive control is demonstrated by utilizing the state estimates to predict future states considering (3D) terrain.Description
Publisher Copyright: © 2024 The Author(s)
Keywords
3D elevation models, Autonomous ground vehicles, Forest machines, Modeling, Observer design and state estimation, Simulation and experimental model validation
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Citation
Badar, T, Backman, J & Visala, A 2024, ' Vehicle modeling and state estimation for autonomous driving in terrain ', Control Engineering Practice, vol. 152, 106046 . https://doi.org/10.1016/j.conengprac.2024.106046
