Custom Distribution for Sampling-Based Motion Planning

TL;DR

This paper introduces a method to enhance sampling-based motion planning by learning a custom distribution from successful queries, significantly improving success rates and efficiency in complex robotic tasks.

Contribution

It proposes a novel approach to adapt the sampling distribution in RRT algorithms using learned data, reducing the need for extensive training data compared to deep learning methods.

Findings

Outperforms original RRT in success rate and speed

Effective in complex scenarios like narrow passages and obstacle avoidance

Requires only a small set of examples for learning

Abstract

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.