SIMULASI SISTEM LOKALISASI BERBASIS EXTENDED KALMAN FILTER PADA ROBOT CLEARPATH HUSKY

*Abang M. Rayhan M. Saputra  -  Department of Mechanical Engineering, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia
Munadi Munadi  -  Department of Mechanical Engineering, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia
Joga Dharma Setiawan  -  Department of Mechanical Engineering, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia

Citation Format:
Abstract

Lokalisasi yang akurat sangat penting untuk pengoperasian robot otonom, terutama dalam lingkungan dinamis dan kompleks. Penelitian ini bertujuan untuk merancang dan menganalisis sistem lokalisasi berbasis Extended Kalman Filter (EKF) pada robot Clearpath Husky. Sistem ini mengintegrasikan data dari sensor GPS, IMU, dan odometri untuk meningkatkan akurasi dan keandalan lokalisasi. Metode penelitian melibatkan simulasi menggunakan ROS (Robot Operating System) dan pengujian di lingkungan Gazebo. Hasil penelitian menunjukkan bahwa integrasi sensor GPS, IMU, dan odometri menghasilkan deviasi posisi yang lebih kecil dibandingkan dengan penggunaan odometri saja. Sistem ini dirancang untuk meningkatkan akurasi lokalisasi robot otonom dalam berbagai kondisi lingkungan, termasuk lingkungan perkotaan dan dalam ruangan.

Fulltext View|Download
Keywords: extended kalman filter; gazebo; lokalisasi; ros
Article Info
Section: Articles
Language : ID
Recent articles
ANALISIS KEKUATAN STRUKTUR REEL CONVEYOR BELT MENGGUNAKAN METODE ELEMEN HINGGA KAJI PERFORMA PELUMASAN PURED SEA WATER PADA WAVE ENERGY CONVERTER (WEC) DAMPAK BEBAN PADA KONTAK GESEK TREAD BLOCK TERHADAP PERMUKAAN RIGID DENGAN VARIASI MATERIAL MENGGUNAKAN METODE FINITE ELEMENT More recent articles
  1. Q. Li and H. Zhu, “Performance evaluation of 2D LiDAR SLAM algorithms in simulated orchard environments,” Comput. Electron. Agric., vol. 221, no. November 2023, p. 108994, Jun. 2024, doi: 10.1016/j.compag.2024.108994
  2. M. Kheirandish, E. A. Yazdi, H. Mohammadi, and M. Mohammadi, “A fault-tolerant sensor fusion in mobile robots using multiple model Kalman filters,” Rob. Auton. Syst., vol. 161, p. 104343, Mar. 2023, doi: 10.1016/j.robot.2022.104343
  3. O. V. Altınpınar and V. Sezer, “A novel indoor localization algorithm based on a modified EKF using virtual dynamic point landmarks for 2D grid maps,” Rob. Auton. Syst., vol. 170, no. August, p. 104546, 2023, doi: 10.1016/j.robot.2023.104546
  4. A. Kaczmarek, W. Rohm, L. Klingbeil, and J. Tchórzewski, “Experimental 2D extended Kalman filter sensor fusion for low-cost GNSS/IMU/Odometers precise positioning system,” Measurement, vol. 193, no. March, p. 110963, Apr. 2022, doi: 10.1016/j.measurement.2022.110963
  5. Q. Luo, S. Li, X. Yan, C. Wang, Z. Zhou, and G. Jia, “An improved two-phase robust distributed Kalman filter,” Signal Processing, vol. 220, no. February, p. 109438, 2024, doi: 10.1016/j.sigpro.2024.109438
  6. Q. Li, H. He, X. Chen, and J. Gao, “Learning-Based Vehicle State Estimation Using Gaussian Process Regression Combined with Extended Kalman Filter,” J. Franklin Inst., vol. 361, no. 9, p. 106907, 2024, doi: 10.1016/j.jfranklin.2024.106907
  7. Q. Li, Y. Zhuang, and J. Huai, “Multi-sensor fusion for robust localization with moving object segmentation in complex dynamic 3D scenes,” Int. J. Appl. Earth Obs. Geoinf., vol. 124, no. October, p. 103507, 2023, doi: 10.1016/j.jag.2023.103507
  8. P. Estefo, J. Simmonds, R. Robbes, and J. Fabry, “The Robot Operating System: Package reuse and community dynamics,” J. Syst. Softw., vol. 151, pp. 226–242, 2019, doi: 10.1016/j.jss.2019.02.024
  9. A. Farley, J. Wang, and J. A. Marshall, “How to pick a mobile robot simulator: A quantitative comparison of CoppeliaSim, Gazebo, MORSE and Webots with a focus on accuracy of motion,” Simul. Model. Pract. Theory, vol. 120, no. April, p. 102629, 2022, doi: 10.1016/j.simpat.2022.102629
  10. A. Mitchell et al., “Results in Engineering A proposed methodology to develop digital twin framework for plasma processing,” Results Eng., vol. 24, no. November, p. 103462, 2024, doi: 10.1016/j.rineng.2024.103462

Last update:

No citation recorded.

Last update:

No citation recorded.