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.

翻译：抽样算法在高维空间非常有用,因此在机器人中广泛使用。然而,解决方案的成功率和质量是通过适当选择参数来确定的,例如各州之间的距离、当地规划员和抽样方法。对于拥有大配置空间或动态限制的机器人来说,选择这些参数是一项艰巨的任务。本文建议了一种方法,通过调整取样方法来改进一套最受欢迎的抽样算法的结果,即快速勘探随机树(RRTs),目的是取代取样功能,传统上是统一的概率密度函数(U-PDF),而采用从以前成功查询类似任务中学习的自定义分布法(C-PDF)。用很少的样本,我们的方法建立自定义分布法,允许较高的成功率和在随机新查询中稀树干。我们用一些通用的自动驾驶方法,例如停车操作法或清除障碍,在复杂的假设中,我们用的方法比原始和偏差的RRT高。此外,拟议方法需要一组相对小的例子,而不像目前的深层次学习技术需要大量的例子。