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High-dimensional Winding-Augmented Motion Planning with 2D topological task projections and persistent homology
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-2965-2953
2016 (English)In: Proceedings - IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2016, 24-31 p.Conference paper (Refereed)
Abstract [en]

Recent progress in motion planning has made it possible to determine homotopy inequivalent trajectories between an initial and terminal configuration in a robot configuration space. Current approaches have however either assumed the knowledge of differential one-forms related to a skeletonization of the collision space, or have relied on a simplicial representation of the free space. Both of these approaches are currently however not yet practical for higher dimensional configuration spaces. We propose 2D topological task projections (TTPs): mappings from the configuration space to 2-dimensional spaces where simplicial complex filtrations and persistent homology can identify topological properties of the high-dimensional free configuration space. Our approach only requires the availability of collision free samples to identify winding centers that can be used to determine homotopy inequivalent trajectories. We propose the Winding Augmented RRT and RRT∗ (WA-RRT/RRT∗) algorithms using which homotopy inequivalent trajectories can be found. We evaluate our approach in experiments with configuration spaces of planar linkages with 2-10 degrees of freedom. Results indicate that our approach can reliably identify suitable topological task projections and our proposed WA-RRT and WA-RRT∗ algorithms were able to identify a collection of homotopy inequivalent trajectories in each considered configuration space dimension.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. 24-31 p.
National Category
Geometry
Identifiers
URN: urn:nbn:se:kth:diva-197239DOI: 10.1109/ICRA.2016.7487113ISI: 000389516200004ScopusID: 2-s2.0-84977482652ISBN: 9781467380263 (print)OAI: oai:DiVA.org:kth-197239DiVA: diva2:1052596
Conference
2016 IEEE International Conference on Robotics and Automation, ICRA 2016, 16 May 2016 through 21 May 2016
Note

QC 20170119

Available from: 2016-12-07 Created: 2016-11-30 Last updated: 2017-01-19Bibliographically approved

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Kragic, Danica
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf