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Data-driven models for predicting the outcome of autonomous wheel loader operations
Umeå University, Faculty of Science and Technology, Department of Physics. Komatsu Ltd.. (Digital Physics)
Umeå University, Faculty of Science and Technology, Department of Physics. (Digital Physics)
Umeå University, Faculty of Science and Technology, Department of Computing Science. Karlstad University, Sweden.ORCID iD: 0000-0001-8704-9584
Umeå University, Faculty of Science and Technology, Department of Physics. Algoryx Simulation.ORCID iD: 0000-0002-0787-4988
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper presents a method using data-driven models for selecting actions and predicting the total performance of autonomous wheel loader operations over many loading cycles in a changing environment. The performance includes loaded mass, loading time, work. The data-driven models input the control parameters of a loading action and the heightmap of the initial pile state to output the inference of either the performance or the resulting pile state. By iteratively utilizing the resulting pile state as the initial pile state for consecutive predictions, the prediction method enables long-horizon forecasting. Deep neural networks were trained on data from over 10,000 random loading actions in gravel piles of different shapes using 3D multibody dynamics simulation. The models predict the performance and the resulting pile state with, on average, 95% accuracy in 1.2 ms, and 97% in 4.5 ms, respectively. The performance prediction was found to be even faster in exchange for accuracy by reducing the model size with the lower dimensional representation of the pile state using its slope and curvature. The feasibility of long-horizon predictions was confirmed with 40 sequential loading actions at a large pile. With the aid of a physics-based model, the pile state predictions are kept sufficiently accurate for longer-horizon use.

National Category
Other Physics Topics Computer Vision and Robotics (Autonomous Systems) Transport Systems and Logistics Robotics
Research subject
Physics
Identifiers
URN: urn:nbn:se:umu:diva-220410DOI: 10.48550/arXiv.2309.12016OAI: oai:DiVA.org:umu-220410DiVA, id: diva2:1834290
Available from: 2024-02-02 Created: 2024-02-02 Last updated: 2024-02-05

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Aoshima, KojiFälldin, ArvidWadbro, EddieServin, Martin

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Aoshima, KojiFälldin, ArvidWadbro, EddieServin, Martin
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Department of PhysicsDepartment of Computing Science
Other Physics TopicsComputer Vision and Robotics (Autonomous Systems)Transport Systems and LogisticsRobotics

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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Output format
  • html
  • text
  • asciidoc
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