A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning
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 resultan...
Saved in:
Main Authors: | , , , |
---|---|
Other Authors: | |
Format: | Article |
Published: |
2023
|
Subjects: | |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.uniten.dspace-29389 |
---|---|
record_format |
dspace |
spelling |
my.uniten.dspace-293892023-12-28T12:12:51Z A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning Khaksar W. Hong T.S. Khaksar M. Motlagh O. 54960984900 8231495000 55350135000 25641787000 Dispersion Multi-query planner Probabilistic roadmaps Sampling-based motion panning Algorithms Dispersion (waves) Motion planning Learning strategy Low dispersions Multi-query planner Probabilistic road maps Probabilistic roadmap Sampling-based Sampling-based algorithms Sampling-based motion planning Dispersions 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 samplingbased 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. � 2013 Khaksar et al. Final 2023-12-28T04:12:51Z 2023-12-28T04:12:51Z 2013 Article 10.5772/56973 2-s2.0-84890467985 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890467985&doi=10.5772%2f56973&partnerID=40&md5=9016754d0f15825860e2a5fb0b0eda0d https://irepository.uniten.edu.my/handle/123456789/29389 10 A397 All Open Access; Gold Open Access Scopus |
institution |
Universiti Tenaga Nasional |
building |
UNITEN Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Tenaga Nasional |
content_source |
UNITEN Institutional Repository |
url_provider |
http://dspace.uniten.edu.my/ |
topic |
Dispersion Multi-query planner Probabilistic roadmaps Sampling-based motion panning Algorithms Dispersion (waves) Motion planning Learning strategy Low dispersions Multi-query planner Probabilistic road maps Probabilistic roadmap Sampling-based Sampling-based algorithms Sampling-based motion planning Dispersions |
spellingShingle |
Dispersion Multi-query planner Probabilistic roadmaps Sampling-based motion panning Algorithms Dispersion (waves) Motion planning Learning strategy Low dispersions Multi-query planner Probabilistic road maps Probabilistic roadmap Sampling-based Sampling-based algorithms Sampling-based motion planning Dispersions Khaksar W. Hong T.S. Khaksar M. Motlagh O. A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning |
description |
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 samplingbased 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. � 2013 Khaksar et al. |
author2 |
54960984900 |
author_facet |
54960984900 Khaksar W. Hong T.S. Khaksar M. Motlagh O. |
format |
Article |
author |
Khaksar W. Hong T.S. Khaksar M. Motlagh O. |
author_sort |
Khaksar W. |
title |
A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning |
title_short |
A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning |
title_full |
A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning |
title_fullStr |
A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning |
title_full_unstemmed |
A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning |
title_sort |
low dispersion probabilistic roadmaps (ld-prm) algorithm for fast and efficient sampling-based motion planning |
publishDate |
2023 |
_version_ |
1806427867173617664 |
score |
13.18916 |