Persons
Xuzhe Dang
All publications
Deep RRT
- Authors: Dr. Stefan Edelkamp, Xuzhe Dang, Chrpa, L.
- Publication: Proceedings of the Fifteenth International Symposium on Combinatorial Search. Palo Alto, California: Association for the Advancement of Artificial Intelligence (AAAI), 2022. p. 333-335. vol. 15. ISBN 978-1-57735-873-2.
- Year: 2022
- DOI: 10.1609/socs.v15i1.21803
- Link: https://doi.org/10.1609/socs.v15i1.21803
- Department: Department of Computer Science, Artificial Intelligence Center
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Annotation:
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.
