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Autonomous Exploration with Online Learning of Traversable Yet Visually Rigid Obstacles

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00362963" target="_blank" >RIV/68407700:21230/23:00362963 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/s10514-022-10075-4" target="_blank" >https://doi.org/10.1007/s10514-022-10075-4</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s10514-022-10075-4" target="_blank" >10.1007/s10514-022-10075-4</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Autonomous Exploration with Online Learning of Traversable Yet Visually Rigid Obstacles

  • Original language description

    This paper concerns online learning of terrain properties combining haptic perception with exteroceptive sensing to reason about forces needed to pass through terrains that visually appear as untraversable obstacles. Terrain learning is studied within the context of autonomous exploration. We propose predicting the traversability of potentially obstructing terrains by active perception to establish a connection between the observed geometric environment model and deliberately sampled forces to pass through the terrain using a haptic sensor that probes the terrain in front of the robot. The developed solution uses a Gaussian Process regressor in online learning and force prediction. The robot is navigated by following the information gain to improve traversability and spatial models. The proposed approach has been experimentally verified in fully autonomous exploration with a multi-legged walking robot. The robot is navigated through visually looking obstacles and explores "hidden" areas while following the expected information gain to explore the terrain properties of the mission area.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • 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

    2023

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Autonomous Robots

  • ISSN

    0929-5593

  • e-ISSN

    1573-7527

  • Volume of the periodical

    47

  • Issue of the periodical within the volume

    November

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    20

  • Pages from-to

    161-180

  • UT code for WoS article

    000886880700001

  • EID of the result in the Scopus database

    2-s2.0-85142529888