A Framework For Parallelizing Sampling-Based Motion Planning Algorithms

Abstract

Motion planning is the problem of finding a valid path for a robot from a start position to a goal position. It has many uses such as protein folding and animation. However, motion planning can be slow and take a long time in difficult environments. Parallelization can be used to speed up this process. This research focused on the implementation of a framework for the implementation and testing of Parallel Motion Planning algorithms. Additionally, two methods were implemented to test this framework. The results showed a reasonable amount of speed-up and coverage and connectivity similar to sequential methods.