Lidé
Xuzhe Dang
Všechny publikace
Deep RRT
- Autoři: Dr. Stefan Edelkamp, Xuzhe Dang, Chrpa, L.
- Publikace: 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.
- Rok: 2022
- DOI: 10.1609/socs.v15i1.21803
- Odkaz: https://doi.org/10.1609/socs.v15i1.21803
- Pracoviště: Katedra počítačů, Centrum umělé inteligence
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Anotace:
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.
