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Simulation-to-reality transfer to control a forwarder with active suspensions through deep reinforcement learning
Umeå University, Faculty of Science and Technology, Department of Physics. (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.ORCID iD: 0000-0001-8704-9584
Skogforsk.
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2022 (English)In: International Conference of Forest Engineering COFE-FORMEC-IUFRO, Corvallis, Oregon, USA, 2022Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

Automating the loaded and unloaded driving of a forwarder has the potential to reduce operational costsup to 10% in cut-to-length logging, but remains a challening and unsolved task. The complex interaction between the vehicle and terrain requires the controller to percieve its surroundings and thestate of the vehicle to plan for traversal. Because the state space is high dimensional and the systemdynamics cannot be formulated in closed form or easily approximated, traditional control methods areinadequate. Under these conditions, where learning to act in the environement is easier than learning thesystem dynamics, model free reinforcement learning is a promising option. We use deep reinforcementlearning for control of a 16-tonne forwarder with actively articulated suspensions. To efficiently gathergeneralizable experience, the control policies were safetly trained in simulation while varying severaldomain parameters. Each policy is trained during what correponds to roughly one month of real time.In simulation, the controller shows the ability to traverse rough terrains reconstruced from high-densitylaser scans and handles slopes up to 27◦. To compare the simulated to real performance we transfer thecontrol policies to the physical vehicle. Our results provide insight on how to improve policy transfer toheavy and expensive forest machines.

Place, publisher, year, edition, pages
2022.
National Category
Robotics Computer Vision and Robotics (Autonomous Systems) Other Physics Topics
Research subject
Physics
Identifiers
URN: urn:nbn:se:umu:diva-199447OAI: oai:DiVA.org:umu-199447DiVA, id: diva2:1696591
Conference
International Conference of Forest Engineering COFE-FORMEC-IUFRO, Corvallis, Oregon, USA, October 4-7, 2022
Available from: 2022-09-17 Created: 2022-09-17 Last updated: 2022-09-19

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Wiberg, ViktorWallin, ErikWadbro, EddieServin, Martin

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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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