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Citation link: https://doi.org/10.26092/elib/2328

Publisher DOI: https://doi.org/10.1007/s10514-015-9523-3
Leidner_Knowledge-Enabled Parameterization of Whole-Body Control-Strategies_2016_AAM_PDF-A.pdf
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Knowledge-enabled parameterization of whole-body control strategies for compliant service robots


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Leidner_Knowledge-Enabled Parameterization of Whole-Body Control-Strategies_2016_AAM_PDF-A.pdf5.82 MBAdobe PDFView/Open
Authors: Leidner, Daniel Sebastian  
Dietrich, Alexander  
Beetz, Michael  
Albu-Schäffer, Alin  
Abstract: 
Compliant manipulation is one of the grand challenges for autonomous robots. Many household chores in human environments, such as cleaning the floor or wiping windows, rely on this principle. At the same time these tasks often require whole-body motions to cover a larger workspace. The performance of the actual task itself is thereby dependent on a large number of parameters that have to be taken into account. To tackle this issue we propose to utilize low-level compliant whole-body control strategies parameterized by high-level hybrid reasoning mechanisms. We categorize compliant wiping actions in order to determine relevant control parameters. According to these parameters we set up process models for each identified wiping action and implement generalized control strategies based on human task knowledge. We evaluate our approach experimentally on three whole-body manipulation tasks, namely scrubbing a mug with a sponge, skimming a window with a window wiper and bi-manually collecting the shards of a broken mug with a broom.
Keywords: Whole-Body Control; AI Reasoning Methods; Task Knowledge; Humanoid Robots; Mobile Manipulation
Issue Date: 2015
Journal/Edited collection: Autonomous robots 
Start page: 519
End page: 536
Type: Artikel/Aufsatz
ISSN: 1573-7527
Secondary publication: yes
Document version: Postprint
DOI: 10.26092/elib/2328
URN: urn:nbn:de:gbv:46-elib70075
Faculty: Fachbereich 03: Mathematik/Informatik (FB 03) 
Appears in Collections:Forschungsdokumente

  

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