A Low Dispersion Probabilistic Roadmaps (LD-PRM) Algorithm for Fast and Efficient Sampling-Based Motion Planning

• Published in: International journal of advanced robotic systems Vol. 10; no. 11
• Main Authors: Khaksar, Weria, Hong, Tang Sai, Khaksar, Mansoor, Motlagh, Omid
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-11-2013; Sage Publications Ltd; SAGE Publishing
• Subjects: Algorithms; Computer simulation; Connectivity; Dispersion; Machine learning; Motion planning; Planning; Probability theory; Queries; Robots; Sampling; Multi-Query Planner; Sampling-Based Motion Panning; Probabilistic Roadmaps; Dispersion
• ISSN: 1729-8806; 1729-8814
• DOI: 10.5772/56973

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.