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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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InTech Open Access Publisher
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/28856/ http://psasir.upm.edu.my/id/eprint/28856/1/28856.pdf |
| _version_ | 1848846232236064768 |
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| author | Khaksar, Weria Tang, Sai Hong Khaksar, Mansoor Motlagh, Omid Reza Esmaeili |
| author_facet | Khaksar, Weria Tang, Sai Hong Khaksar, Mansoor Motlagh, Omid Reza Esmaeili |
| author_sort | Khaksar, Weria |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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 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. |
| first_indexed | 2025-11-15T08:59:26Z |
| format | Article |
| id | upm-28856 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:59:26Z |
| publishDate | 2013 |
| publisher | InTech Open Access Publisher |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-288562017-10-30T07:57:39Z http://psasir.upm.edu.my/id/eprint/28856/ A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning Khaksar, Weria Tang, Sai Hong Khaksar, Mansoor Motlagh, Omid Reza Esmaeili 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. InTech Open Access Publisher 2013 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/28856/1/28856.pdf 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 http://www.intechopen.com/books/international_journal_of_advanced_robotic_systems/a-low-dispersion-probabilistic-roadmaps-ld-prm-algorithm-for-fast-and-efficient-sampling-based-motio 10.5772/56973 |
| spellingShingle | Khaksar, Weria Tang, Sai Hong Khaksar, Mansoor Motlagh, Omid Reza Esmaeili A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning |
| title | 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_short | 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 |
| url | http://psasir.upm.edu.my/id/eprint/28856/ http://psasir.upm.edu.my/id/eprint/28856/ http://psasir.upm.edu.my/id/eprint/28856/ http://psasir.upm.edu.my/id/eprint/28856/1/28856.pdf |