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

• Main Authors: Weria Khaksar, Tang Sai Hong, Mansoor Khaksar, Omid Motlagh
• Format: Article
• Language: English
• Published: SAGE Publishing 2013-11-01
• Series: International Journal of Advanced Robotic Systems
• Online Access: https://doi.org/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.