SoCS Conference 2022 Conference Paper
Deep RRT
- Xuzhe Dang
- Lukás Chrpa
- Stefan Edelkamp
Sampling-based motion planning algorithms such as Rapidly exploring Random Trees (RRTs) have been used in robotic applications for a long time. In this paper, we propose a method that combines deep learning with RRT* method. We use a neural network to learn a sample strategy for RRT*. We evaluate Deep RRT* in a collection of 2D scenarios. The results demonstrate that our algorithm could find collision-free paths efficiently and fast, and can be generalized to unseen environments.