Simultaneous Localization and Mapping pada Smart Automated Guided Vehicle menggunakan Iterative Closest Point berbasis K-Means Clustering

NI PUTU DEVIRA AYU MARTINI, BAMBANG SUMANTRI, BIMA SENA BAYU DEWANTARA

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ABSTRAK

Automated Guided Vehicle (AGV) merupakan salah satu jenis mobile robot yang digunakan untuk mengangkut barang menuju tempat tujuan. AGV mampu bekerja pada lingkungan yang dinamis tanpa menggunakan garis pemandu. Namun sebelumnya harus mempunyai informasi yang cukup terhadap lingkungan kerjanya. Teknik ini dikenal dengan Simulataneous Localization and Mapping (SLAM) yang digunakan robot untuk menggambar peta sekaligus mengetahui posisi robot di dalam peta. Pada penelitian ini, metode yang digunakan yaitu SLAM berbasis Iterative Closest Point (ICP) dengan algoritma K-Means yang menggunakan kumpulan titik dari sensor laser range finder (LRF) untuk membangun peta lingkungan. Pemetaan SLAM menggunakan algoritma K-Means memiliki error hasil scan jarak 77,69% lebih kecil dan waktu eksekusi 0,18% lebih cepat dibandingkan dengan KD-Tree. Peta yang dihasilkan dengan algoritma KMeans pada ICP-SLAM memberikan hasil yang lebih baik & mendekati keadaan ruangan sebenarnya dibandingkan menggunakan algoritma KD-Tree.

Kata kunci: ICP-SLAM, K-Means, Laser Range Finder.

 

ABSTRACT

Automated Guided Vehicle (AGV) is a type of mobile robot that is used to transport goods to destination. AGV is able to work in a dynamic environment without guidelines. However, it must have sufficient information about its working environment beforehand. This technique is known as Simultaneous Localization and Mapping (SLAM) which is used by a robot to be able to draw a map as well as to determine its position on the map. In this research, the method used is SLAM based on Iterative Closest Point (ICP) with the K-Means algorithm that uses a collection of points from the Laser Range Finder (LRF) sensor to build an environmental map. SLAM using the K-Means algorithm has 77,69% smaller distance error and 0,18% faster execution time than KD-Tree. The map generated by the K-Means algorithm on an ICP-SLAM gives better results & closer to the actual state than using the KD-Tree.

Keywords: ICP-SLAM, K-Means, Laser Range Finder.


Kata Kunci


ICP-SLAM; K-Means; Laser Range Finder

Teks Lengkap:

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v10i4.742

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