Show simple item record

dc.contributor.authorArtan, Unal
dc.contributor.authorMarshall, Joshua
dc.date.accessioned2020-10-31T15:19:23Z
dc.date.available2020-10-31T15:19:23Z
dc.date.issued2020-09
dc.identifier.citationU. Artan and J. A. Marshall, "Towards Automatic Classification of Fragmented Rock Piles via Proprioceptive Sensing and Wavelet Analysis," 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Karlsruhe, Germany, 2020, pp. 348-353, doi: 10.1109/MFI49285.2020.9235261.
dc.identifier.urihttp://hdl.handle.net/1974/28555
dc.description.abstractIn this paper, we describe a method for classifying rock piles characterized by different size distributions by using accelerometer data and wavelet analysis. Size distribution (frag-mentation) estimates are used in the mining and aggregates industries to ensure the rock that enters the crushing and grinding circuits meet input design specifications. Current technologies use exteroceptive sensing to estimate size distributions from, for example, camera images. Our approach instead proposes the use of signals acquired from the process of loading equipment that are used to transport fragmented rock. The experimental setup used a laboratory-sized mock up of a haul truck with two inertial measurement units (IMUs) for data collection. Results utilizing wavelet analysis are provided that show how accelerometers could be used to distinguish between piles with different size distributions.en
dc.language.isoenen
dc.publisherIEEEen
dc.relationStrategic Networks Programen
dc.subjectmining roboticsen
dc.subjectfragmentation analysisen
dc.subjectautonomous excavationen
dc.subjectwaveletsen
dc.subjectproprioceptive sensingen
dc.titleTowards Automatic Classification of Fragmented Rock Piles via Proprioceptive Sensing and Wavelet Analysisen
dc.typejournal articleen
dc.identifier.doi10.1109/MFI49285.2020.9235261
project.funder.identifierhttp://dx.doi.org/10.13039/501100000038en
project.funder.nameNatural Sciences and Engineering Research Council of Canadaen
oaire.awardNumberNETGP 508451-17en
oaire.awardURIhttps://www.nserc-crsng.gc.ca/Business-Entreprise/How-Comment/Networks-Reseaux/CanadianRobotics_eng.aspen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record