Incremental Traversability Assessment Learning Using Growing Neural Gas Algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00332831" target="_blank" >RIV/68407700:21230/19:00332831 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-19642-4_17" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-19642-4_17</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-19642-4_17" target="_blank" >10.1007/978-3-030-19642-4_17</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Incremental Traversability Assessment Learning Using Growing Neural Gas Algorithm

Popis výsledku v původním jazyce

In this paper, we report early results on the deployment of the growing neural gas algorithm in online incremental learning of traversability assessment with a multi-legged walking robot. The addressed problem is to incrementally build a model of the robot experience with traversing the terrain that can be immediately utilized in the traversability cost assessment of seen but not yet visited areas. The main motivation of the studied deployment is to improve the performance of the autonomous mission by avoiding hard to traverse areas and support planning cost-efficient paths based on the continuously collected measurements characterizing the operational environment. We propose to employ the growing neural gas algorithm to incrementally build a model of the terrain characterization from exteroceptive features that are associated with the proprioceptive based estimation of the traversal cost. Based on the reported results, the proposed deployment provides competitive results to the existing approach based on the Incremental Gaussian Mixture Network.

Název v anglickém jazyce

Incremental Traversability Assessment Learning Using Growing Neural Gas Algorithm

Popis výsledku anglicky

In this paper, we report early results on the deployment of the growing neural gas algorithm in online incremental learning of traversability assessment with a multi-legged walking robot. The addressed problem is to incrementally build a model of the robot experience with traversing the terrain that can be immediately utilized in the traversability cost assessment of seen but not yet visited areas. The main motivation of the studied deployment is to improve the performance of the autonomous mission by avoiding hard to traverse areas and support planning cost-efficient paths based on the continuously collected measurements characterizing the operational environment. We propose to employ the growing neural gas algorithm to incrementally build a model of the terrain characterization from exteroceptive features that are associated with the proprioceptive based estimation of the traversal cost. Based on the reported results, the proposed deployment provides competitive results to the existing approach based on the Incremental Gaussian Mixture Network.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization

ISBN

978-3-030-19641-7

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

11

Strana od-do

166-176

Název nakladatele

Springer-VDI-Verlag

Místo vydání

Düsseldorf

Místo konání akce

Barcelona

Datum konání akce

26. 6. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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