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Terrain machine learning: A predictive method for estimating terrain model parameters using simulated sensors, vehicle and terrain
Umeå University, Faculty of Science and Technology, Department of Physics.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Predicting terrain trafficability of deformable terrain is a difficult task with applications in e.g, forestry, agriculture, exploratory missions. The currently used techniques are neither practical, efficient, nor sufficiently accurate and inadequate for certain soil types. An online method which predicts terrain trafficability is of interest for any vehicle with purpose to reduce ground damage, improve steering and increase mobility. This thesis presents a novel approach for predicting the model parameters used in modelling a virtual terrain. The model parameters include particle stiffness, tangential friction, rolling resistance and two parameters related to particle plasticity and adhesion. Using multi-body dynamics, both vehicle and terrain can be simulated, which allows for an efficient exploration of a great variety of terrains. A vehicle with access to certain sensors can frequently gather sensor data providing information regarding vehicle-terrain interaction. The proposed method develops a statistical model which uses the sensor data in predicting the terrain model parameters. However, these parameters are specified at model particle level and do not directly explain bulk properties measurable on a real terrain. Simulations were carried out of a single tracked bogie constrained to move in one direction when traversing flat, homogeneous terrains. The statistical model with best prediction accuracy was ridge regression using polynomial features and interaction terms of second degree. The model proved capable of predicting particle stiffness, tangential friction and particle plasticity, with moderate accuracy. However, it was deduced that the current predictors and training scenarios were insufficient in estimating particle adhesion and rolling resistance. Nevertheless, this thesis indicates that it should be possible to develop a method which successfully predicts terrain model properties.

Place, publisher, year, edition, pages
2018.
Keywords [en]
machine learning, terrain-vehicle interaction, discrete element method, multivariate statistics, terrain trafficability
National Category
Other Physics Topics Probability Theory and Statistics Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:umu:diva-149815OAI: oai:DiVA.org:umu-149815DiVA, id: diva2:1227973
External cooperation
UMIT Research Lab
Subject / course
Examensarbete i teknisk fysik
Educational program
Master of Science Programme in Engineering Physics
Presentation
2018-06-11, NA332, Naturvetarhuset, Umeå, 11:00 (English)
Supervisors
Examiners
Available from: 2018-08-13 Created: 2018-06-27 Last updated: 2018-08-13Bibliographically approved

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1863e08d1863cae37e62df4ab6f82eefb101b51cd0951de071d4b05d135346a1239e021565f616225d5b9476a3762438cd7d0e098f2043b7581b2f68089e9715
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Department of Physics
Other Physics TopicsProbability Theory and StatisticsComputer Vision and Robotics (Autonomous Systems)

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CiteExportLink to record
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