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Quantitative Evaluation of Coarse-To-Fine Loading Strategies for Material Rehandling
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO Lab)ORCID iD: 0000-0001-8658-2985
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO Lab)ORCID iD: 0000-0002-9503-0602
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO Lab)ORCID iD: 0000-0003-0217-9326
2015 (English)In: Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), New York: IEEE conference proceedings , 2015, 450-455 p.Conference paper, (Refereed)
Abstract [en]

Autonomous handling of piled materials is an emerging topic in automation science and engineering. A central question for material rehandling tasks (transporting materials that have been assembled in piles) is “where to dig, in order to optimise performance”? In particular, we are interested in the application of autonomous wheel loaders to handle piles of gravel. Still, the methodology proposed in this paper relates to granular materials in other applications too. Although initial work on suggesting strategies for where to dig has been done by a few other groups, there has been a lack of structured evaluation of the usefulness of the proposed strategies. In an attempt to further the field, we present a quantitative evaluation of loading strategies; both coarse ones, aiming to maintain a good pile shape over long-term operation; and refined ones, aiming to detect the locally best attack pose for acquiring a good fill grade in the bucket. Using real-world data from a semi-automated test platform, we present an assessment of how previously proposed pile shape measures can be mapped to the amount of material in the bucket after loading. We also present experimental data for long-term strategies, using simulations based on real-world 3D scan data from a production site.

Place, publisher, year, edition, pages
New York: IEEE conference proceedings , 2015. 450-455 p.
Series
IEEE International Conference on Automation Science and Engineering (CASE), ISSN 2161-8070
Keyword [en]
Emerging Topics in Automation, Automation for Machine Tools, Sustainable Production
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-45677DOI: 10.1109/CoASE.2015.7294120ISI: 000380453000073Scopus ID: 2-s2.0-84952765805ISBN: 978-1-4673-8183-3 (print)OAI: oai:DiVA.org:oru-45677DiVA: diva2:849536
Conference
IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, Sweden, August 24-28, 2015
Projects
ALLO
Funder
Knowledge Foundation, 20110214
Available from: 2015-08-28 Created: 2015-08-28 Last updated: 2017-03-06Bibliographically approved

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Magnusson, MartinKucner, TomaszLilienthal, Achim J.
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School of Science and Technology, Örebro University, Sweden
Computer Vision and Robotics (Autonomous Systems)

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Citation style
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