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

• Main Authors: Khaksar, Weria, Tang, Sai Hong, Khaksar, Mansoor, Motlagh, Omid Reza Esmaeili
• Format: Article
• Language: English
• Published: InTech Open Access Publisher 2013
• 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

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.