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Reinforcement Learning Control of a Forestry Crane Manipulator
Umeå University, Faculty of Science and Technology, Department of Physics. Umeå universitet. (Digital Physics)ORCID iD: 0000-0002-6811-2776
Algoryx Simulation AB, Umeå, Sweden.ORCID iD: 0000-0002-4748-0086
Algoryx Simulation AB, Umeå, Sweden.
Umeå University, Faculty of Science and Technology, Department of Physics. Algoryx Simulation AB, Umeå, Sweden.ORCID iD: 0000-0002-0787-4988
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2021 (English)In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021): Proceedings, Prague: IEEE Robotics and Automation Society, 2021, p. 2121-2126Conference paper, Published paper (Refereed)
Abstract [en]

Forestry machines are heavy vehicles performing complex manipulation tasks in unstructured production forest environments. Together with the complex dynamics of the on-board hydraulically actuated cranes, the rough forest terrains have posed a particular challenge in forestry automation. In this study, the feasibility of applying reinforcement learning control to forestry crane manipulators is investigated in a simulated environment. Our results show that it is possible to learn successful actuator-space control policies for energy efficient log grasping by invoking a simple curriculum in a deep reinforcement learning setup. Given the pose of the selected logs, our best control policy reaches a grasping success rate of 97%. Including an energy-optimization goal in the reward function, the energy consumption is significantly reduced compared to control policies learned without incentive for energy optimization, while the increase in cycle time is marginal. The energy-optimization effects can be observed in the overall smoother motion and acceleration profiles during crane manipulation. 

Place, publisher, year, edition, pages
Prague: IEEE Robotics and Automation Society, 2021. p. 2121-2126
Keywords [en]
Reinforcement Learning, Robotics and Automation in Agriculture and Forestry, Deep Learning in Grasping and Manipulation
National Category
Control Engineering Robotics Computer Sciences Applied Mechanics
Research subject
Physics; Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-187948DOI: 10.1109/IROS51168.2021.9636219ISI: 000755125501096Scopus ID: 2-s2.0-85124340717ISBN: 978-1-6654-1715-0 (print)ISBN: 978-1-6654-1714-3 (electronic)OAI: oai:DiVA.org:umu-187948DiVA, id: diva2:1597673
Conference
IROS 2021, IEEE/RSJ International Conference on Intelligent Robots and Systems, Online via Prague, Czech Republic, September 27 - October 1, 2021
Funder
Mistra - The Swedish Foundation for Strategic Environmental Research, 2017/14 #6Available from: 2021-09-27 Created: 2021-09-27 Last updated: 2023-08-07Bibliographically approved

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Andersson, JenniferBodin, KennethServin, MartinWallin, Erik

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CiteExportLink to record
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