Dead reckoning in a dynamic quadruped robot based on multimodal proprioceptive sensory information

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00199258" target="_blank" >RIV/68407700:21230/13:00199258 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/TRO.2012.2228309" target="_blank" >http://dx.doi.org/10.1109/TRO.2012.2228309</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TRO.2012.2228309" target="_blank" >10.1109/TRO.2012.2228309</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dead reckoning in a dynamic quadruped robot based on multimodal proprioceptive sensory information

Popis výsledku v původním jazyce

It is an important ability for any mobile robot to be able to estimate its posture and to gauge the distance it traveled. In this paper, we have addressed this problem in a dynamic quadruped robot by combining traditional state estimation methods with machine learning. We have designed and implemented a navigation algorithm for full body state (position, velocity, and attitude) estimation that uses no external reference but relies on multimodal proprioceptive sensory information only. The extended Kalman filter (EKF) was used to provide error estimation and data fusion from two independent sources of information: 1) strapdown mechanization algorithm processing raw inertial data and 2) legged odometry. We have devised a novel legged odometer that combines information from a multimodal combination of sensors (joint and pressure). We have shown our method to work for a dynamic turning gait, and we have also successfully demonstrated how it generalizes to different velocities and terrains.

Název v anglickém jazyce

Dead reckoning in a dynamic quadruped robot based on multimodal proprioceptive sensory information

Popis výsledku anglicky

It is an important ability for any mobile robot to be able to estimate its posture and to gauge the distance it traveled. In this paper, we have addressed this problem in a dynamic quadruped robot by combining traditional state estimation methods with machine learning. We have designed and implemented a navigation algorithm for full body state (position, velocity, and attitude) estimation that uses no external reference but relies on multimodal proprioceptive sensory information only. The extended Kalman filter (EKF) was used to provide error estimation and data fusion from two independent sources of information: 1) strapdown mechanization algorithm processing raw inertial data and 2) legged odometry. We have devised a novel legged odometer that combines information from a multimodal combination of sensors (joint and pressure). We have shown our method to work for a dynamic turning gait, and we have also successfully demonstrated how it generalizes to different velocities and terrains.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/7E10044" target="_blank" >7E10044: Natural human-robot cooperation in dynamic environments</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Robotics

ISSN

1552-3098

e-ISSN

—

Svazek periodika

29

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

563-571

Kód UT WoS článku

000317493900020

EID výsledku v databázi Scopus

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