Deep RRT*

SOCS-22 Volume 15

Volume

Vol. 15 No. 1 (2022): Fifteenth International Symposium on Combinatorial Search

Student Papers

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.

10.1609/socs.v15i1.21803