Motion Planning for Mobile Robots using Sampling based algorithms
Many discrete planning methods have limitations and cannot be scaled easily to high-dimensional spaces. Discrete planning is sensitive to branching factor and the overall algorithm can be complex based on the c-space dimensions. Sampling based approaches addresses these problems to a great extent. In this work, the various types of sampling based algorithms are discussed. First, probabilistic road map(PRM) with different sampling methods is presented. For this, uniform sampling , random sampling , Gaussian sampling and bridge sampling are implemented. Further, two more sampling based approaches such as rapidly exploring random tree(RRT) and it’s variation RRT* are implemented. Finally , the results between all these algorithms are compared.
RRT :
RRT* :
Bridge Sampling:
Gaussian Sampling:
Random Sampling:
Uniform Sampling:
Report for this project can be downloaded here.
Instructor: Jane Li