  Sampling-based motion planning algorithms are widely used in robotics because
they are very effective in high-dimensional spaces. However, the success rate
and quality of the solutions are determined by an adequate selection of their
parameters such as the distance between states, the local planner, and the
sampling distribution. For robots with large configuration spaces or dynamic
restrictions, selecting these parameters is a challenging task. This paper
proposes a method for improving the performance to a set of the most popular
sampling-based algorithms, the Rapidly-exploring Random Trees (RRTs) by
adjusting the sampling method. The idea is to replace the uniform probability
density function (U-PDF) with a custom distribution (C-PDF) learned from
previously successful queries in similar tasks. With a few samples, our method
builds a custom distribution that allows the RRT to grow to promising states
that will lead to a solution. We tested our method in several autonomous
driving tasks such as parking maneuvers, obstacle clearance and under narrow
passages scenarios. The results show that the proposed method outperforms the
original RRT and several improved versions in terms of success rate, tree
density and computation time. In addition, the proposed method requires a
relatively small set of examples, unlike current deep learning techniques that
require a vast amount of examples.
