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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
InTech Open Access Publisher
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/28856/1/28856.pdf http://psasir.upm.edu.my/id/eprint/28856/ 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 |
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| Summary: | 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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