Warped Hypertime Representations for Long-term Autonomy of Mobile Robots

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00332172" target="_blank" >RIV/68407700:21230/19:00332172 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/LRA.2019.2926682" target="_blank" >https://doi.org/10.1109/LRA.2019.2926682</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/LRA.2019.2926682" target="_blank" >10.1109/LRA.2019.2926682</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Warped Hypertime Representations for Long-term Autonomy of Mobile Robots

Popis výsledku v původním jazyce

This paper presents a novel method for introducing time into discrete and continuous spatial representations used in mobile robotics, by modelling long-term, pseudo-periodic variations caused by human activities or natural processes. Unlike previous approaches, the proposed method does not treat time and space separately, and its continuous nature respects both the temporal and spatial continuity of the modeled phenomena. The key idea is to extend the spatial model with a set of wrapped time dimensions that represent the periodicities of the observed events. By performing clustering over this extended representation, we obtain a model that allows the prediction of probabilistic distributions of future states and events in both discrete and continuous spatial representations. We apply the proposed algorithm to several long-term datasets acquired by mobile robots and show that the method enables a robot to predict future states of representations with different dimensions. The experiments further show that the method achieves more accurate predictions than the previous state of the art.

Název v anglickém jazyce

Warped Hypertime Representations for Long-term Autonomy of Mobile Robots

Popis výsledku anglicky

This paper presents a novel method for introducing time into discrete and continuous spatial representations used in mobile robotics, by modelling long-term, pseudo-periodic variations caused by human activities or natural processes. Unlike previous approaches, the proposed method does not treat time and space separately, and its continuous nature respects both the temporal and spatial continuity of the modeled phenomena. The key idea is to extend the spatial model with a set of wrapped time dimensions that represent the periodicities of the observed events. By performing clustering over this extended representation, we obtain a model that allows the prediction of probabilistic distributions of future states and events in both discrete and continuous spatial representations. We apply the proposed algorithm to several long-term datasets acquired by mobile robots and show that the method enables a robot to predict future states of representations with different dimensions. The experiments further show that the method achieves more accurate predictions than the previous state of the art.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

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

2019

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

4

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

3310-3317

Kód UT WoS článku

000476791300026

EID výsledku v databázi Scopus

2-s2.0-85069761513