Regresi linier berbasis clustering untuk deteksi dan estimasi halangan pada smart wheelchair

Authors

  • Putra Pandu Adikara Grup Riset Computer Vision, Fakultas Ilmu Komputer, Universitas Brawijaya, Malang
  • Randy Cahya Wihandika Grup Riset Computer Vision, Fakultas Ilmu Komputer, Universitas Brawijaya, Malang
  • Fitri Utaminingrum Grup Riset Computer Vision, Fakultas Ilmu Komputer, Universitas Brawijaya, Malang
  • Yuita Arum Sari Grup Riset Computer Vision, Fakultas Ilmu Komputer, Universitas Brawijaya, Malang
  • M Ali Fauzi Grup Riset Computer Vision, Fakultas Ilmu Komputer, Universitas Brawijaya, Malang
  • Dahnial Syauqy Grup Riset Computer Vision, Fakultas Ilmu Komputer, Universitas Brawijaya, Malang
  • Rizal Maulana Grup Riset Computer Vision, Fakultas Ilmu Komputer, Universitas Brawijaya, Malang

DOI:

https://doi.org/10.26594/register.v3i1.587

Keywords:

obstacle detection, obstacle distance estimation, line laser, Linear Regression, k-Means clustering, deteksi halangan, estimasi jarak halangan, Regresi Linier, k-means, clustering, pengelompokan

Abstract

 

Penelitian ini bertujuan untuk mengusulkan sebuah pendekatan dalam mendeteksi halangan dan memperkirakan jarak halangan untuk diterapkan pada kursi roda pintar (smart wheelchair) yang dilengkapi kamera dan line laser. Kamera menangkap sinar line laser yang jatuh di depan kursi roda untuk mengenali adanya halangan pada lintasan berdasarkan bentuk citra line laser tersebut. Estimasi jarak halangan dihitung dari hasil Regresi Linier. Metode Regresi Linier yang digunakan dalam penelitian ini adalah model bertingkat dengan k-Means clustering. Metode Regresi Linier model bertingkat digunakan untuk merepresentasikan korelasi antara jarak line laser pada citra dan jarak halangan secara aktual. Hasil metode Regresi Linier model bertingkat dengan k-Means clustering yang diujicobakan memberikan hasil yang lebih baik dengan RMSE sebesar 3.541 cm dibanding dengan Regresi Liner sederhana dengan RMSE sebesar 5.367 cm.

 

 

 

This research aim to propose a new approach to detect obstacles and to estimate the distance of the obstacle which is in this case applied to smart wheelchair equipped with camera and line laser. The camera capture the image of line laser reflected in front of the wheelchair to detect any existing obstacle on the wheelchair’s pathway based on the line shape of reflected line laser. Obstacle’s distance is estimated using Linier Regression. Linier Regression method used in this research is stepwise model using k-Means clustering. Linear Regression method with stepwise model will be used to represent the correlation between the distance of the line laser in the image and the actual distance of the obstacle in real world. The result of Linear Regression with stepwise model using k-Means clustering gave better result with RMSE of 3.541 cm than simple Linear Regression with RMSE of 5.367 cm.

References

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Utaminingrum, F., Fitriyah, H., Wihandika, R. C., Fauzi, M. A., Syauqy, D., & Maulana, R. (2016a). Fast Obstacle Distance Estimation using Line laser Imaging Technique for Smart Wheelchair. International Journal of Electrical and Computer Engineering, 6(4).

Utaminingrum, F., Kurniawan, T. A., Fauzi, M. A., Maulana, R., Syauqy, D., Wihandika, R. C., Sari, Y. A., Adikara, P. P. (2016). A Laser-Vision based Obstacle Detection and Distance Estimation for Smart Wheelchair Navigation. 2016 IEEE International Conference on S (pp. 123-127). Beijing: IEEE.

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Published

2017-01-01

How to Cite

[1]
P. P. Adikara, “Regresi linier berbasis clustering untuk deteksi dan estimasi halangan pada smart wheelchair”, Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 3, no. 1, pp. 11–16, Jan. 2017.

Issue

Vol. 3 No. 1 (2017): January

Section

Article

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