bibtype C - Conference Paper (international conference)
ARLID 0491010
utime 20240111141003.7
mtime 20180712235959.9
SCOPUS 85052624552
WOS 000443321500008
DOI 10.1016/j.ifacol.2018.07.252
title (primary) (eng) Sensor Fusion for simple walking robot using low-level implementation of Extended Kalman Filter
specification
page_count 6 s.
media_type C
serial
ARLID cav_un_epca*0494443
ISSN 2405-8963
title IFAC-PapersOnLine. Volume 51, Issue 13. : 2nd IFAC Conference on Modelling, Identification and Control of Nonlinear Systems MICNON 2018
page_num 43-48
publisher
place Amsterdam
name Elsevier
year 2018
keyword Filtering and smoothing
keyword Digital implementation
keyword Walking robot
author (primary)
ARLID cav_un_auth*0252057
name1 Anderle
name2 Milan
institution UTIA-B
full_dept (cz) Teorie řízení
full_dept (eng) Department of Control Theory
department (cz)
department (eng) TR
full_dept Department of Control Theory
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101074
name1 Čelikovský
name2 Sergej
institution UTIA-B
full_dept (cz) Teorie řízení
full_dept Department of Control Theory
department (cz)
department TR
full_dept Department of Control Theory
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
source_type konferenční příspěvek
source_size 1,17 MB
cas_special
project
ARLID cav_un_auth*0347203
project_id GA17-04682S
agency GA ČR
country CZ
abstract (eng) The main aim of this paper depicts in design and implementation of the Extended\nKalman Filter for a nonlinear system in an application of a sensor fusion from a practical point of view. The sensor fusion is a typical data processing problem in mechanical systems where individual measurements of (angular) positions, velocities or accelerations are done independently on each other but the measured values are correlated to each other via dynamics of the system. Moreover, the measurement is corrupted by noise. The sensor fusion technique is capable to gain proper information about positions, velocities or accelerations from inaccurate\nmeasurement. In background of the sensor fusion algorithm, in our particular case, works the Extended Kalman Filter. Its adaptation for a simple mechanical system represented by a nonlinear system are object of the research in this paper related to usage of the Extended Kalman Filter on a low cost hardware.
action
ARLID cav_un_auth*0362231
name Second IFAC Conference on Modelling, Identification and Control of Nonlinear Systems
dates 20180620
mrcbC20-s 20180622
place Guadalajara
country MX
RIV BC
FORD0 20000
FORD1 20200
FORD2 20205
reportyear 2019
num_of_auth 2
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0285102
confidential S
mrcbC83 RIV/67985556:_____/18:00491010!RIV19-AV0-67985556 192095187 Doplnění UT WOS a Scopus
mrcbC83 RIV/67985556:_____/18:00491010!RIV19-GA0-67985556 192084141 Doplnění UT WOS a Scopus
mrcbC86 n.a. Proceedings Paper Automation Control Systems
mrcbT16-s 0.234
mrcbT16-4 Q3
mrcbT16-E Q4
arlyear 2018
mrcbU14 85052624552 SCOPUS
mrcbU24 PUBMED
mrcbU34 000443321500008 WOS
mrcbU56 konferenční příspěvek 1,17 MB
mrcbU63 cav_un_epca*0494443 IFAC-PapersOnLine. Volume 51, Issue 13. : 2nd IFAC Conference on Modelling, Identification and Control of Nonlinear Systems MICNON 2018 2405-8963 43 48 Amsterdam Elsevier 2018