Trajectory Optimization using Learned Robot-Terrain Interaction Model in Exploration of Large Subterranean Environments

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00355264" target="_blank" >RIV/68407700:21230/22:00355264 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/LRA.2022.3147332" target="_blank" >https://doi.org/10.1109/LRA.2022.3147332</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/LRA.2022.3147332" target="_blank" >10.1109/LRA.2022.3147332</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Trajectory Optimization using Learned Robot-Terrain Interaction Model in Exploration of Large Subterranean Environments

Popis výsledku v původním jazyce

We consider the task of active exploration of large subterranean environments with a ground mobile robot. Our goal is to autonomously explore a large unknown area and to obtain an accurate coverage and localization of objects of interest (artifacts). The exploration is constrained by the restricted operation time in rescue scenarios, as well as a hard rough terrain. To this end, we introduce a novel optimization strategy that respects these constraints by maximizing the environment coverage by onboard sensors while producing feasible trajectories with the help of a learned robot-terrain interaction model. The approach is evaluated in diverse subterranean simulated environments showing the viability of active exploration in challenging scenarios. In addition, we demonstrate that the local trajectory optimization improves global coverage of an environment as well as the overall object detection results.

Název v anglickém jazyce

Trajectory Optimization using Learned Robot-Terrain Interaction Model in Exploration of Large Subterranean Environments

Popis výsledku anglicky

We consider the task of active exploration of large subterranean environments with a ground mobile robot. Our goal is to autonomously explore a large unknown area and to obtain an accurate coverage and localization of objects of interest (artifacts). The exploration is constrained by the restricted operation time in rescue scenarios, as well as a hard rough terrain. To this end, we introduce a novel optimization strategy that respects these constraints by maximizing the environment coverage by onboard sensors while producing feasible trajectories with the help of a learned robot-terrain interaction model. The approach is evaluated in diverse subterranean simulated environments showing the viability of active exploration in challenging scenarios. In addition, we demonstrate that the local trajectory optimization improves global coverage of an environment as well as the overall object detection results.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

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

2022

Kód důvěrnosti údajů

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

Název periodika

IEEE Robotics and Automation Letters

ISSN

2377-3766

e-ISSN

2377-3766

Svazek periodika

7

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

7

Strana od-do

3365-3371

Kód UT WoS článku

000753553400027

EID výsledku v databázi Scopus

2-s2.0-85124202174