A Low Dispersion Probabilistic Roadmaps (LD-PRM) Algorithm for Fast and Efficient Sampling-Based Motion Planning. (29th November 2013)

• Record Type: Journal Article
• Authors: Khaksar, Weria; Hong, Tang Sai; Khaksar, Mansoor; Motlagh, Omid
• Is Part Of: International journal of advanced robotic systems. Volume 10:Number 11(2013)
• Publication Date: 2013-11-29
• Subjects: Probabilistic Roadmaps -- Sampling-Based Motion Panning -- Dispersion -- Multi-Query Planner; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.5772/56973
• Languages: English
• ISSNs: 1729-8806

Abstract

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.