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GPU-Based Path Optimization Algorithm in High-Resolution Cost Map with Kinodynamic Constraints: Using Non-Reversible Parallel Tempering
Karlstad University, Faculty of Health, Science and Technology (starting 2013).
2023 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis introduces a GPU-accelerated algorithm for path planning under kinodynamic constraints, focusing on navigation of flying vehicles within a high-resolution cost map. The algorithm operates by creating dynamically feasible initial paths, and a non-reversible parallel tempering Markov chain Monte Carlo scheme to optimize the paths while adhering to the nonholonomic kinodynamical constraints. The algorithm efficiently generates high quality dynamically feasible paths. An analysis demonstrates the algorithm's robustness, stability and scalability. The approach used for this algorithm is versatile, allowing for straightforward adaptation to different dynamic conditions and cost maps. The algorithm's applicability also extends to various path planning problems, signifying the potential advantages of GPU-accelerated algorithms in the domain of path planning.

Place, publisher, year, edition, pages
2023. , p. 79
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kau:diva-95720OAI: oai:DiVA.org:kau-95720DiVA, id: diva2:1776859
External cooperation
SAAB Dynamics AB
Educational program
Engineering: Engineering Physics (300 ECTS credits)
Supervisors
Examiners
Available from: 2023-06-28 Created: 2023-06-28 Last updated: 2023-06-28Bibliographically approved

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Computer Vision and Robotics (Autonomous Systems)

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • Other style
More styles
Language
  • de-DE
  • en-GB
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  • fi-FI
  • nn-NO
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  • sv-SE
  • Other locale
More languages
Output format
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