  Sampling-based algorithms are widely used in robotics because they are very
useful in high dimensional spaces. However, the rate of success 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 method. For
robots with large configuration spaces or dynamic restrictions selecting these
parameters is a challenging task. This paper proposes a method for improving
the results for 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 sampling function, traditionally a Uniform Probability
Density Function (U-PDF) with a custom distribution (C-PDF) learned from
previously successful queries of a similar task. With few samples, our method
builds the custom distribution allowing a higher success rate and sparser trees
in randomly new queries. We test our method in several common tasks of
autonomous driving such as parking maneuvers or obstacle clearance and also in
complex scenarios outperforming the base original and bias RRT. In addition,
the proposed method requires a relative small set of examples, unlike current
deep learning techniques that require a vast amount of examples.
