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A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning


Khaksar, Weria and Tang, Sai Hong and Khaksar, Mansoor and Motlagh, Omid Reza Esmaeili (2013) A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning. International Journal of Advanced Robotic Systems, 10. art. no. 397. pp. 1-10. ISSN 1729-8814; ESSN: 1729-8806


In this paper, we propose a new learning strategy for a probabilistic roadmap (PRM) algorithm. The proposed strategy is based on reducing the dispersion of the generated set of samples. We defined a forbidden range around each selected sample and ignored this region in further sampling. The resultant planner, called low dispersion-PRM, is an effective multi-query sampling-based planner that is able to solve motion planning queries with smaller graphs. Simulation results indicated that the proposed planner improved the performance of the original PRM and other low-dispersion variants of PRM. Furthermore, the proposed planner is able to solve difficult motion planning instances, including narrow passages and bug traps, which represent particularly difficult tasks for classic sampling-based algorithms. For measuring the uniformity of the generated samples, a new algorithm was created to measure the dispersion of a set of samples based on a predetermined resolution.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.5772/56973
Publisher: InTech Open Access Publisher
Keywords: Probabilistic roadmaps; Sampling-based motion panning; Dispersion; Multi-query planner
Depositing User: Nabilah Mustapa
Date Deposited: 24 May 2015 05:31
Last Modified: 30 Oct 2017 07:57
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.5772/56973
URI: http://psasir.upm.edu.my/id/eprint/28856
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