Autonomous Exploration with Online Learning of Traversable Yet Visually Rigid Obstacles

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Autonomous Exploration with Online Learning of Traversable Yet Visually Rigid Obstacles

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Autonomous Exploration with Online Learning of Traversable Yet Visually Rigid Obstacles

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

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

2023

Kód důvěrnosti údajů

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

Název periodika

Autonomous Robots

ISSN

0929-5593

e-ISSN

1573-7527

Svazek periodika

47

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

161-180

Kód UT WoS článku

000886880700001

EID výsledku v databázi Scopus

2-s2.0-85142529888