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dc.contributor.authorFernando, Heshan
dc.contributor.authorMarshall, Joshua
dc.date.accessioned2020-11-20T15:53:07Z
dc.date.available2020-11-20T15:53:07Z
dc.date.issued2020-11
dc.identifier.citationH. Fernando and J. A. Marshall. What lies beneath: Material classification for autonomous excavators using proprioceptive force sensing and machine learning.  In Automation in Construction, vol. 119, November, 2020.en
dc.identifier.urihttp://hdl.handle.net/1974/28600
dc.description.abstractThe ability of robotic excavators to acquire meaningful knowledge about materials during digging can augment their autonomous functionality, as well as optimize downstream operations in construction and mining. Some material properties, such as rock sizes, can be determined visually, but these methods cannot see what lies beneath. In this work, a classification methodology that utilizes only proprioceptive force data acquired from an autonomous digging system and machine learning algorithms is proposed for excavation material identification. The consistent performance synonymous with autonomous digging systems allows for the use of basic features extracted from the force data for classification. A proof of concept of this novel approach to excavation material classification is demonstrated through a binary classification of rock and gravel materials. Force data were obtained from full-scale autonomous loading trials with a 14-tonne capacity load-haul-dump machine at a mining and construction test facility. Preliminary results achieved a classification accuracy of 90%.en
dc.language.isoenen
dc.publisherElsevier B.V.en
dc.relationNSERC Canadian Robotics Networken
dc.subjectrobotics; automation; construction; robotics in construction; autonomous excavation; material classificationen
dc.titleWhat Lies Beneath: Material Classification for Autonomous Excavators using Proprioceptive Force Sensing and Machine Learningen
dc.identifier.doi10.1016/j.autcon.2020.103374
project.funder.identifierhttp://dx.doi.org/10.13039/501100000038en
project.funder.nameNatural Sciences and Engineering Research Council of Canadaen
oaire.awardURIhttps://ncrn-rcrc.mcgill.caen


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