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Discrete element modelling of large soil deformations under heavy vehicles
Umeå University, Faculty of Science and Technology, Department of Physics. (Digital Physics)
Umeå University, Faculty of Science and Technology, Department of Physics. (Digital Physics)ORCID iD: 0000-0002-0787-4988
Swedish University of Agricultural Sciences, Umeå, Sweden.
2021 (English)In: Journal of terramechanics, ISSN 0022-4898, E-ISSN 1879-1204, Vol. 93, p. 11-21Article in journal (Refereed) Published
Abstract [en]

This paper addresses the challenges of creating realistic models of soil for simulations of heavy vehicles on weak terrain. We modelled dense soils using the discrete element method with variable parameters for surface friction, normal cohesion, and rolling resistance. To find out what type of soils can be represented, we measured the internal friction and bulk cohesion of over 100 different virtual samples. To test the model, we simulated rut formation from a heavy vehicle with different loads and soil strengths. We conclude that the relevant space of dense frictional and frictional-cohesive soils can be represented and that the model is applicable for simulation of large deformations induced by heavy vehicles on weak terrain.

Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 93, p. 11-21
Keywords [en]
DEM, Multibody Dynamics, Weak Soil, Rut Formation, Multipass
National Category
Other Physics Topics Other Earth and Related Environmental Sciences Applied Mechanics
Research subject
Physics
Identifiers
URN: urn:nbn:se:umu:diva-176349DOI: 10.1016/j.jterra.2020.10.002ISI: 000596712200002Scopus ID: 2-s2.0-85094326100OAI: oai:DiVA.org:umu-176349DiVA, id: diva2:1485085
Funder
Mistra - The Swedish Foundation for Strategic Environmental Research, DIA 2017/14 #6eSSENCE - An eScience CollaborationSwedish National Infrastructure for Computing (SNIC), SNIC dnr 2019/3-168Available from: 2020-11-01 Created: 2020-11-01 Last updated: 2023-05-08Bibliographically approved
In thesis
1. Terrain machine learning
Open this publication in new window or tab >>Terrain machine learning
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Maskininlärning i terräng
Abstract [en]

The use of heavy vehicles in rough terrain is vital in the industry but has negative implications for the climate and ecosystem. In addition, the demand for improved efficiency underscores the need to enhance these vehicles' navigation capabilities. Navigating rough terrain presents distinct challenges, including deformable soil, surface roughness, and spatial and temporal terrain variability. Focusing on forestry, this thesis aims to improve navigation using machine learning and physics simulations. Without considering the vehicle-terrain dynamics, methods for navigation can result in unsafe or unnecessarily challenging situations. Specifically, we address route planning, control for autonomous vehicles, and soilde formations. We simulate soil using the discrete element method and vehicles using multibody dynamics.

To enhance route planning, we train a predictor model that uses a height map of the terrain to predict measures of traversability. The model has a directional dependency, couples geometric terrain features with vehicle design and dynamics, and allows for swift evaluations over large areas. The proposed method facilitates detailed route planning, using multiple objectives to yield efficient solutions.

We address autonomy in rough terrain navigation by training a controller through deep reinforcement learning. The control policy uses a local height map for perception to plan and control a forwarder with actively articulated suspensions. The controller adapts to overcome various obstacles and demonstrates skilled driving in rough terrain.

Extending beyond simulation, we address the simulation-to-reality gap of vehicles with complex hydraulic drivelines through system identification and domain randomization. The results show that having an accurate model of the actuators, modelling system delays, and preventing bang-bang control yields successful transfer. Controllers that train in simulation and transfer to reality are a step toward autonomous vehicles.

While the previously mentioned studies assume rigid terrain, we also answer if the discrete element method can capture large soil deformations due to heavy traffic. The results show that the discrete element method can represent a wide variety of natural soil and that the resulting rut depths agree well with empirical models and experimental data, including multipass scenarios.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2023. p. 38
Keywords
multibody dynamics simulation, rough terrain vehicle, autonomous vehicles, robotics control, discrete element method, sim-to-real, reinforcement learning
National Category
Robotics Other Physics Topics Forest Science Computer Vision and Robotics (Autonomous Systems)
Research subject
Physics
Identifiers
urn:nbn:se:umu:diva-207982 (URN)978-91-8070-060-3 (ISBN)978-91-8070-059-7 (ISBN)
Public defence
2023-06-01, NAT.D.410, Umeå, 09:00 (English)
Opponent
Supervisors
Funder
Mistra - The Swedish Foundation for Strategic Environmental Research, DIA 2017/14 #6
Available from: 2023-05-11 Created: 2023-05-05 Last updated: 2023-05-08Bibliographically approved

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Wiberg, ViktorServin, Martin

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Citation style
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