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Incremental Traversability Assessment Learning Using Growing Neural Gas Algorithm

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Incremental Traversability Assessment Learning Using Growing Neural Gas Algorithm

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • 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)

Others

  • Publication year

    2019

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

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

  • ISBN

    978-3-030-19641-7

  • ISSN

    2194-5357

  • e-ISSN

  • Number of pages

    11

  • Pages from-to

    166-176

  • Publisher name

    Springer-VDI-Verlag

  • Place of publication

    Düsseldorf

  • Event location

    Barcelona

  • Event date

    Jun 26, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article