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Lattice-based Motion Planning for a General 2-trailer system
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Scania Tech Ctr, Sweden.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-6957-2603
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2017 (English)In: 2017 28TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV 2017), IEEE , 2017, p. 819-824Conference paper, Published paper (Refereed)
Abstract [en]

Motion planning for a general 2-trailer system poses a hard problem for any motion planning algorithm and previous methods have lacked any completeness or optimality guarantees. In this work we present a lattice-based motion planning framework for a general 2-trailer system that is resolution complete and resolution optimal. The solution will satisfy both differential and obstacle imposed constraints and is intended either as a part of an autonomous system or as a driver support system to automatically plan complicated maneuvers in backward and forward motion. The proposed framework relies on a precomputing step that is performed offline to generate a finite set of kinematically feasible motion primitives. These motion primitives are then used to create a regular state lattice that can be searched for a solution using standard graph-search algorithms. To make this graph-search problem tractable for real-time applications a novel parametrization of the reachable state space is proposed where each motion primitive moves the system from and to a selected set of circular equilibrium configurations. The approach is evaluated over three different scenarios and impressive real-time performance is achieved.

Place, publisher, year, edition, pages
IEEE , 2017. p. 819-824
Series
IEEE Intelligent Vehicles Symposium, ISSN 1931-0587
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-145825DOI: 10.1109/IVS.2017.7995817ISI: 000425212700128ISBN: 978-1-5090-4804-5 OAI: oai:DiVA.org:liu-145825DiVA, id: diva2:1192090
Conference
28th IEEE Intelligent Vehicles Symposium (IV)
Note

Funding Agencies|FFI/VINNOVA

Available from: 2018-03-21 Created: 2018-03-21 Last updated: 2018-03-21

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
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  • asciidoc
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