Implementasi Pemetaan Robot Roda Mecanum Otonom Berbasis LIDAR dengan SLAM
DOI:
https://doi.org/10.30871/ji.v17i1.8575Keywords:
LIDAR, ROS, SLAM, Autonomous MovementAbstract
This article reviews an autonomous mobile robot with mecanum wheels using localization and mapping with Lidar. The mecanum wheeled robot is capable of autonomously moving from point A to point B. This study aims to determine the level of accuracy and precision in mapping to ensure the robot can operate efficiently, as well as to develop the ability to perform real-time environmental mapping using data obtained from the Lidar A2M12 sensor. It also aims to implement the SLAM (Simultaneous Localization and Mapping) algorithm to simultaneously determine the robot's position and orientation while mapping. For independent movement, the robot must be aware of its surroundings and its position within that environment. The method used is simultaneous localization and mapping using the RPLidar A2M12 sensor and ROS (Robot Operating System). Based on the testing results, the gmapping SLAM error rate is 3.34%, with a sensor distance and angle measurement error of 1.16%. Overall, this autonomous robot can be used even in open areas and with simple obstacles.
Downloads
References
[1] Wolcott, Ryan W., and Ryan M. Eustice. "Visual localization within lidar maps for automated urban driving." 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2014
[2] Rahman, Aulia. "Penerapan slam gmapping dengan robot operating system menggunakan laser scanner pada turtlebot." Jurnal Rekayasa Elektrika 16.2 (2020).
[3] Prayoga, S. et al. “EKF SLAM menggunakan lidar” (2010).
[4] Nafis, Zishwa Muhammad Jauhar. "Pemetaan Ruangan Berbasis LIDAR Menggunakan Mobile Robot." (2019).
[5] Zahra, Lilik, Muhammad Ikhsan Sani, and Simon Siregar. "Perancangan Dan Implementasi Mapping System Untuk Navigasi Roner (Robot Cleaner)." eProceedings of Applied Science 4.3 (2018).
[6] Fikri, Muhammad, and Muhammad Rivai. "Sistem Penghindar Halangan Dengan Metode LIDAR Pada Unmanned Surface Vehicle." Jurnal Teknik ITS (SINTA: 4, IF: 1.1815) 8.2 (2020).
[7] Alsadik, Bashar, and Samer Karam. "The simultaneous localization and mapping (SLAM)-An overview." Journal of Applied Science and Technology Trends 2.02 (2021).
[8] Taufik, Ahmad Sulkhan. Sistem navigasi waypoint pada autonomous mobile robot. Diss. Brawijaya University, 2013.
[9] Corke, Peter, and Vision Robotics. "Control Fundamental algorithms in MATLAB." Springer Tracts in Advanced Robotics, (Springer Berlin Heidelberg, 2011) (2017).
[10] Saat, Shahrizal, et al. "Hectorslam 2d mapping for simultaneous localization and mapping (slam)." Journal of Physics: Conference Series. Vol. 1529. No. 4. IOP Publishing, 2020.
[11] Quigley, Morgan, et al. "ROS: an open- source Robot Operating System." ICRA workshop on open source software. Vol. 3. No. 3.2. 2009.
[12] Pyo, Y., et al. "ROS Robot Programming (English). ROBOTIS, 12 2017
[13] Leonard, John J., and Hugh F. Durrant-Whyte. "Simultaneous map building and localization for an autonomous mobile robot." IROS. Vol. 3. 2019.
[14] Fx, Dieter, et al. "A probabilistic approach to collaborative multi-robot localization." Autonomous robots 8 (2000).
[15] Grisetti, Giorgio, Cyrill Stachniss, and Wolfram Burgard. "Improved techniques for grid mapping with rao-blackwellized particle filters." IEEE transactions on Robotics 23.1 (2007).
[16] Durrant-Whyte and T. Bailey, "Simultaneous localization and mapping (SLAM): Part I," IEEE Robotics & Automation Magazine, vol. 13, pp. 99-110, 2006
[17] Firmansyah, R. A., Prabowo, Y. A., Suheta, T. ., & Utomo, A. N. D. (2024). Implementation of SLAM Gmapping and Extended Kalman Filter for Security Robot Navigation System. Emitor: Jurnal Teknik Elektro, 24(2), 145–153.
