Deep RRT

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00364779" target="_blank" >RIV/68407700:21230/22:00364779 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/22:00364779

Result on the web

<a href="https://doi.org/10.1609/socs.v15i1.21803" target="_blank" >https://doi.org/10.1609/socs.v15i1.21803</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1609/socs.v15i1.21803" target="_blank" >10.1609/socs.v15i1.21803</a>

Result language

angličtina

Original language name

Deep RRT

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the Fifteenth International Symposium on Combinatorial Search

ISBN

978-1-57735-873-2

ISSN

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e-ISSN

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Number of pages

3

Pages from-to

333-335

Publisher name

Association for the Advancement of Artificial Intelligence (AAAI)

Place of publication

Palo Alto, California

Event location

Vídeň

Event date

Jul 21, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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