Sampling-Based Motion Planning: A Survey

Abstract

Sampling-based motion approaches, like Probabilistic Roadmap Methods or those based on Rapidly-exploring Random Trees are giving good results in robot motion planning problems with many degrees of freedom. Following these approaches, several strategies have been proposed for biasing the sampling towards the most promising regions, thus improving the efficiency and allowing to cope with difficult motion planning problems. The success of these planners in solving challenging problems can be explained by the fact that no explicit representation of the free configuration space is required. This paper reviews some of the most influential proposals and ideas, providing indications on their practical and theoretical implications. The contributions made by Mexican researchers in this field are also presented.