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Permanent link (DOI): https://doi.org/10.7939/R3W37M617

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Motion Planning with Monte Carlo Random Walks Open Access

Descriptions

Other title
Subject/Keyword
Motion Planning
Random Walk
Monte Carlo method
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Chen, Weifeng
Supervisor and department
Müller, Martin (Computing Science)
Examining committee member and department
Ray, Nilanjan (Computing Science)
Zhang, Hong (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2015-11-26T11:02:02Z
Graduation date
2016-06
Degree
Master of Science
Degree level
Master's
Abstract
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.
Language
English
DOI
doi:10.7939/R3W37M617
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. 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.
Citation for previous publication
Chen, Weifeng and Müller, Martin. Continuous Arvand: Motion planning with Monte Carlo random walks. In ICAPS 2015 Workshop on Planning and Robotics (PlanRob), pages 23–29, 2015.  http://www.cs.bgu.ac.il/~icaps15/workshops/planrob.html

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