Sensor Fusion for simple walking robot using low-level implementation of Extended Kalman Filter

作     者：Anderle, Milan elikovský, Sergej 

作者机构：The Czech Academy of Sciences Institute of Information Theory and Automation Czech Republic 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2018年第51卷第13期

页      面：43-48页

核心收录：

基　　金：Supported by the Czech Science Foundation grant No. Ω7-04682S. implementation of the ffiKF algorithm realizing the sensor P★2r4o0c5e-8e9d6in3 g©s 2 20n18d  IIFFAACC C (oInntfeerrneantcioen aoln Federation of Automatic Contr4ol3) Hosting by Elsevier Ltd. All rights reserved. Proceedings  2nd IFAC Conference on 43 PMProoedecree lreleidnvigine wg Isd u e n2ndnteidfri cIrFaeAstpiCoon nC saoibnnidlfie tCyre oonnfc tIreno tole nornf aNtioonnlainl eFaerderation of Automa43tic Control. PMrrooodcceeeleeliddniignn gg Issd  e 22nnntiddfi cIIFFaAAtiCCon CC aoonnndffee Crreeonnncctreeo ool nnof Nonlinear 4343 MSystems1o0.d1e0l1li6n/gj. i fIadceonl.t2i0fi1c8a.t0i7o.n25 a2nd Control of Nonlinear Syoosdcteeelmelidnsign g Isd e 2nntidfi cIFaAtiCon C aonndfe Creonnctreo ol nof Nonlinear 43 SystemsSystems Syuosadtdeealmlliansjga r aId  eMnetixfiiccaot i oJunn aen 2d0 C-2o2n  t2ro0l1 o8f Nonlinear Guadalajara  Mexico  June 20-22  2018 Gyusatdeamlasjara  Mexico  June 20-22  2018 Guadalajara  Mexico  June 20-22  2018 

主　　题：Extended Kalman filters Acceleration Data handling Electric drives Filtration Mobile robots Nonlinear systems Sensor data fusion Design and implementations Digital implementation Low cost hardware Mechanical systems Processing problems Sensor fusion algorithms smoothing Walking robots 

摘      要：The main aim of this paper depicts in design and implementation of the Extended Kalman 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 measurement. 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. © 2018