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Improved Optimization of Motion Primitives for Motion Planning in State Lattices
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-8354-6249
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-1795-5992
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-6957-2603
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a framework for generating motion primitives for lattice-based motion planners automatically. Given a family of systems, the user only needs to specify which principle types of motions, which are here denoted maneuvers, that are relevant for the considered system family. Based on the selected maneuver types and a selected system instance, the algorithm not only automatically optimizes the motions connecting pre-defined boundary conditions, but also simultaneously optimizes the end-point boundary conditions as well. This significantly reduces the time consuming part of manually specifying all boundary value problems that should be solved, and no exhaustive search to generate feasible motions is required. In addition to handling static a priori known system parameters, the framework also allows for fast automatic re-optimization of motion primitives if the system parameters change while the system is in use, e.g, if the load significantly changes or a trailer with a new geometry is picked up by an autonomous truck. We also show in several numerical examples that the framework can enhance the performance of the motion planner in terms of total cost for the produced solution.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Autonomous / Intelligent Robotic Vehicles, Self-Driving Vehicles, Vehicle Control
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-159657OAI: oai:DiVA.org:liu-159657DiVA, id: diva2:1342996
Conference
2019 IEEE Intelligent Vehicles Symposium (IV)
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2019-08-15 Created: 2019-08-15 Last updated: 2019-08-15

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Bergman, KristofferLjungqvist, OskarAxehill, Daniel
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CiteExportLink to record
Permanent link

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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
  • text
  • asciidoc
  • rtf