Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Dispertio: Optimal Sampling For Safe Deterministic Motion Planning
Robert Bosch GmbH, Corp Res, D-70049 Stuttgart, Germany..
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. RWTH Aachen University, Germany.
Rhein Westfal TH Aachen, German Aerosp Ctr DLR, D-82234 Wessling, Germany..
Robert Bosch GmbH, Corp Res, D-70049 Stuttgart, Germany..
2020 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 5, no 2, p. 362-368Article in journal (Refereed) Published
Abstract [en]

A key challenge in robotics is the efficient generation of optimal robot motion with safety guarantees in cluttered environments. Recently, deterministic optimal sampling-based motion planners have been shown to achieve good performance towards this end, in particular in terms of planning efficiency, final solution cost, quality guarantees as well as non-probabilistic completeness. Yet their application is still limited to relatively simple systems (i.e., linear, holonomic, Euclidean state spaces). In this work, we extend this technique to the class of symmetric and optimal driftless systems by presenting Dispertio, an offline dispersion optimization technique for computing sampling sets, aware of differential constraints, for sampling-based robot motion planning. We prove that the approach, when combined with PRM*, is deterministically complete and retains asymptotic optimality. Furthermore, in our experiments we show that the proposed deterministic sampling technique outperforms several baselines and alternative methods in terms of planning efficiency and solution cost.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020. Vol. 5, no 2, p. 362-368
Keywords [en]
Motion and path planning, nonholonomic motion planning, reactive and sensor-based planning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-266737DOI: 10.1109/LRA.2019.2958525ISI: 000505500700001Scopus ID: 2-s2.0-85077371669OAI: oai:DiVA.org:kth-266737DiVA, id: diva2:1386342
Note

QC 20200117

Available from: 2020-01-17 Created: 2020-01-17 Last updated: 2020-01-17Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Bruns, Leonard
By organisation
Robotics, Perception and Learning, RPL
In the same journal
IEEE Robotics and Automation Letters
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 30 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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