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ITS » Paper and Presentation » Matematika Posted by dee@its.ac.id at 08/08/2012 15:24:25 • 2121 Views
ESTIMASI POSISI GERAK ROBOT MOBIL DENGAN MENGGUNAKAN METODE ENSEMBLE KALMAN FILTER ENKF BERDASARKAN LINTASAN YANG AKAN DITEMPUH
POSITION MOTION ESTIMATION OF MOBILE ROBOT BY ENSEMBLE KALMAN FILTER METHOD BASED ON PATH TO BE TAKEN
Author : SULAIMAN, MAKSUM ( 1206100709 )
ABSTRAK
Perkembangan teknologi modern saat ini sangat pesat khususnya dalam dunia robot sehingga banyak negara berlomba-lomba untuk mengembangkan robot. Salah satu jenis robot yang banyak dikembangkan adalah robot mobil. Robot mobil adalah robot yang memiliki ciri khas konstruksi berupa roda untuk menggerakkan keseluruhan badan robot tersebut sehingga robot tersebut dapat melakukan perpindahan posisi dari satu titik ke titik yang lain. Robot mobil dapat dibuat sebagai pengikut garis Line Follower atau pengikut dinding Wall Follower ataupun pengikut cahaya.
ABSTRACT
Today the development of modern technology is very rapid especially in the world of robots. Many countries are competing to develop the robot. One type of robot that has been developed is Mobile robot. The Mobile robot is a robot that has a typical construction of the wheel to move the whole robots body so it can make the shift position from the point to another. Mobile robot can be made as a follower of the line line follower followers of the wall wall follower or follower of the light.
Results to be achieved in this thesis is to get the steering angle of the path of the Mobile robot and put it into the Mobile robot dynamic system in order to obtain the dynamic path not just a circle or a straight line. Then also be shown that the method of Ensemble Kalman Filter EnKF can be used to estimate the position of the Mobile robot on any path that is different from a fairly small error. This method was choosen because it can be used to estimate the nonlinear dynamic model.
Keywords:
Estimasi posisi; Perubahan lintasan menjadi sudut; Robot mobil; Sudut kemudi; Kalman filter; Ensemble kalman filter (EnKF)
Subject
: Kalman filtering
Contributor
Subchan, Ph.D
DR. Erna Apriliani, M.Si
Date Create
: 07/02/2012
Type
: Text
Format
: pdf
Language
: Indonesian
Identifier
: ITS-paper-12021120000657
Collection ID
: 12021120000657
Call Number
: RSMa 519.2 Sul e
Source Paper adn Presentation of Mathematics, RSMa 519.2 Sul e, 2012
Coverage ITS Community
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