On Self-Organizing Map and Rapidly-Exploring Random Graph in Multi-Goal Planning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00237222" target="_blank" >RIV/68407700:21230/16:00237222 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-28518-4_12" target="_blank" >http://dx.doi.org/10.1007/978-3-319-28518-4_12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-28518-4_12" target="_blank" >10.1007/978-3-319-28518-4_12</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On Self-Organizing Map and Rapidly-Exploring Random Graph in Multi-Goal Planning

Popis výsledku v původním jazyce

This paper reports on ongoing work towards an extension of the self-organizing maps for the traveling salesman problem to more challenging problems of multi-goal trajectory planning for complex robots with a high-dimensional configuration space. The main challenge of this problem is that the distance function needed to find a sequence of the visits to the goals is not known a priori and it is not easy to compute. To address this challenge, we propose to utilize the unsupervised learning in a trade-off between the exploration of the distance function and exploitation of its current model. The proposed approach is based on steering the sampling process in a randomized sampling-based motion planning technique to create a suitable motion planning roadmap, which represents the required distance function. The presented results shows the proposed approach quickly provides an admissible solution, which may be further improved by additional samples of the configuration space

Název v anglickém jazyce

On Self-Organizing Map and Rapidly-Exploring Random Graph in Multi-Goal Planning

Popis výsledku anglicky

This paper reports on ongoing work towards an extension of the self-organizing maps for the traveling salesman problem to more challenging problems of multi-goal trajectory planning for complex robots with a high-dimensional configuration space. The main challenge of this problem is that the distance function needed to find a sequence of the visits to the goals is not known a priori and it is not easy to compute. To address this challenge, we propose to utilize the unsupervised learning in a trade-off between the exploration of the distance function and exploitation of its current model. The proposed approach is based on steering the sampling process in a randomized sampling-based motion planning technique to create a suitable motion planning roadmap, which represents the required distance function. The presented results shows the proposed approach quickly provides an admissible solution, which may be further improved by additional samples of the configuration space

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

<a href="/cs/project/GP13-18316P" target="_blank" >GP13-18316P: Samo-organizující se sítě v robotických úlohách plánování cesty přes více cílů</a><br>

Návaznosti

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

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 11th Workshop on Self-Organizicng Maps - Advances in Intelligent Systems and Computing

ISBN

978-3-319-28517-7

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

11

Strana od-do

143-153

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Houston

Datum konání akce

6. 1. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000381879200012