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dc.contributor.authorFernando, Heshan
dc.contributor.authorMarshall, Joshua A
dc.contributor.authorLarsson, Johan
dc.date.accessioned2019-06-14T21:15:03Z
dc.date.available2019-06-14T21:15:03Z
dc.date.issued2019-02-07
dc.identifier.citationFernando, H., Marshall, J.A. & Larsson, J. J Intell Robot Syst (2019). https://doi.org/10.1007/s10846-019-00994-3en_US
dc.identifier.issn1573-0409
dc.identifier.urihttp://hdl.handle.net/1974/26315
dc.description.abstractThis paper presents the development and field validation of an iterative learning-based admittance control algorithm for autonomous excavation in fragmented rock using robotic wheel loaders. An admittance control strategy is augmented with iterative learning, which automatically updates control parameters based on the error between a target bucket fill weight and the measured fill weight at the end of each excavation pass. The algorithm was validated through full-scale autonomous excavation experiments with a 14-tonne capacity load-haul-dump (LHD) machine and two different types of excavation materials: fragmented rock and gravel. In both excavation scenarios, the iterative learning algorithm is able to update the admittance control parameters for a specified target bucket fill weight, eliminating the need to manually re-tune control parameters as material characteristics change. These results have practical significance for increasing the autonomy of robotic wheel loaders used in mining and construction.en_US
dc.description.sponsorshipThis work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) under project RGPIN-2015-04025, the Swedish Knowledge Foundation (KK-stiftelsen) under project 20150282, and by Epiroc Rock Drills AB (Sweden).en_US
dc.language.isoenen_US
dc.publisherSpringer Netherlandsen_US
dc.relation.ispartofseriesJournal of Intelligent and Robotic Systems;
dc.subjectAutonomous excavationen_US
dc.subjectIterative learningen_US
dc.subjectAdmittance controlen_US
dc.subjectMining roboticsen_US
dc.titleIterative Learning-Based Admittance Control for Autonomous Excavationen_US
dc.typejournal articleen_US
dc.identifier.doi10.1007/s10846-019-00994-3


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