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All Journal ComEngApp : Computer Engineering and Applications Journal
Sahat Pangidoan
Robotic and Control Lab. Faculty of Computer Science, Sriwijaya University
Computer Science & IT Engineering

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Localization of Leader-Follower Robot Using Extended Kalman Filter Siti Nurmaini; Sahat Pangidoan
Computer Engineering and Applications Journal Vol 7 No 2 (2018)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1094.548 KB) | DOI: 10.18495/comengapp.v7i2.253

Abstract

Non-holonomic leader-follower robot must be capable to find its own position in order to be able to navigating autonomously in the environment this problem is known as localization. A common way to estimate the robot pose by using odometer. However, odometry measurement may cause inaccurate result due to the wheel slippage or other small noise sources. In this research, the Extended Kalman Filter (EKF) is proposed to minimize the error or the inaccuracy caused by the odometry measurement. The EKF algorithm works by fusing odometry and landmark information to produce a better estimation. A better estimation acknowledged whenever the estimated position lies close to the actual path, which represents a system without noise. Another experiment is conducted to observe the influence of numbers of landmark to the estimated position. The results show that the EKF technique is effective to estimate the leader pose and orientation pose with small error and the follower has the ability traverse close to leader based-on the actual path.
Co-Authors Siti Nurmaini