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Motion Planning with Monte Carlo Random Walks

  • Author / Creator
    Chen, Weifeng
  • This thesis applies the Monte Carlo Random Walk method (MRW) to motion planning. We explore different global and local restart strategies to improve the performance. Several new algorithms based on the MRW approach, such as bidirectional Arvand and optimizing planner Arvand*, are introduced and compared with existing motion planning approaches in the Open Motion Planning Library (OMPL). The results of the experiments show that the Arvand planners are competitive against other motion planners on the planning problems provided by OMPL.

  • Subjects / Keywords
  • Graduation date
    2016-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3W37M617
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Müller, Martin (Computing Science)
  • Examining committee members and their departments
    • Zhang, Hong (Computing Science)
    • Ray, Nilanjan (Computing Science)