An Overview and Comparison of Traditional Motion Planning Based on Rapidly Exploring Random Trees

Abstract

Motion planning is a fundamental problem in robotics that involves determining feasible or optimal paths within finite time. While complete motion planning algorithms are guaranteed to converge to a path solution in finite time, they are proven to be computationally inefficient, making them unsuitable for most practical problems. Resolution-complete algorithms, on the other hand, ensure completeness only if the resolution parameter is sufficiently fine, but they suffer severely from the curse of dimensionality. In contrast, sampling-based algorithms, such as Rapidly Exploring Random Trees (RRT) and its variants, have gained the increasing attention of researchers due to their computational efficiency and effectiveness, particularly in high-dimensional problems. This review paper introduces RRT-based algorithms and provides an overview of their key methodological aspects.