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Learning by Digging: A Differentiable Prediction Model for an Autonomous Wheel Loader
Umeå University, Faculty of Science and Technology, Department of Physics.
2022 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Wheel loaders are heavy duty machines that are ubiquitous on construction sites and in mines all over the world. Fully autonomous wheel loaders remains an open problem but the industry is hoping that increasing their level of autonomy will help to reduce costs and energy consumption while also increasing workplace safety. Operating a wheel loader efficiently requires dig plans that extend over multiple dig cycles and not just one at a time. This calls for a model that can predict both the performance of a dig action and the resulting shape of the pile. In this thesis project, we use simulations to develop a data-driven artificial neural network model that can predict the outcome of a dig action. The model is able to predict the wheel loader’s productivity with an average error of 7.3% and the altered shape of the pile with an average relative error of 4.5%. We also show that automatic differentiation techniques can be used to accurately differentiate the model with respect to input. This makes it possible to use gradient-based optimization methods to find the dig action that maximises the performance of the wheel loader.

Place, publisher, year, edition, pages
2022. , p. 40
Keywords [en]
Wheel loader, Deep learning, Autonomous, Multibody and soil dynamics
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:umu:diva-197165OAI: oai:DiVA.org:umu-197165DiVA, id: diva2:1675538
External cooperation
Digital Physics, Umeå University
Subject / course
Examensarbete i teknisk fysik
Educational program
Master of Science Programme in Engineering Physics
Presentation
2022-06-07, Nat.D.480, Naturvetarhuset, Universitetsvägen, 901 87, Umeå, 16:00 (English)
Supervisors
Examiners
Available from: 2022-06-23 Created: 2022-06-23 Last updated: 2022-06-23Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf