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Abstract

Sistem multi-robot telah diterapkan pada tugas-tugas kompleks yang biasanya dilakukan oleh manusia. Untuk dapat menjalankan tugasnya, robot perlu bernavigasi ke dari suatu posisi ke posisi lain. Agar dapat bernavigasi dengan baik, robot memerlukan peta sebagai acuannya dalam bernavigasi. Simultaneous Localization and Mapping (SLAM) merupakan sebuah metode bagi robot untuk dapat membuat peta dan melakukan lokalisasi. ORB SLAM-2 merupakan sebuah metode SLAM berbasis sensor visual yang kompatibel terhadap kamera monokular, stereo, maupun RGBD. Dengan menggunakan kamera monokular, penelitian ini bertujuan untuk membuat rancangan sistem pemetaan lingkungan multi-robot dengan menggunakan algoritma ORB SLAM-2. Tugas akhir ini merancang sistem desentralisasi sehingga algoritma dijalankan pada kedua robot. Kemudian setiap robot melakukan pemetaan lingkungannya dan mengirimkannya ke komputer agar dapat divisualisasi. Pada percobaannya, rancangan ini berhasil membuat sistem melaksanakan tugasnya dengan baik. Peta yang dihasilkan oleh sistem ini memiliki skala sekitar 1 : 5,81. Sistem juga dapat memvisualisasikan peta yang dihasilkan oleh masing-masing robot pada sebuah komputer server. Berdasarkan hasil percobaan, dapat disimpulkan bahwa sistem pemetaan lingkungan multi-robot menggunakan ORB SLAM-2 dapat dilakukan dengan mendesentralisasi sistem. Dengan ini, beban kerja sistem terbagi menjadi dua, pertama pemrosesan gambar dilakukan oleh masing-masing robot hingga menghasilkan titik-titik peta dan kedua komputer server bertugas untuk memvisualisasikan titik-titik peta yang dihasilkan robot pada antarmuka pengguna.

Keywords

multi-robot monokular SLAM VSLAM

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c): Pipit Angraeni, Ridwan Ridwan, Muhammad Taufiq Aulia Asshydiqi (2020)
How to Cite
Anggraeni, P., Ridwan, R., & Asshydiqi, M. (2020). Penerapan Algoritma ORB SLAM-2 Pada Sistem Pemetaan Lingkungan Multi Robot. INVOTEK: Jurnal Inovasi Vokasional Dan Teknologi, 20(3), 123-134. https://doi.org/https://doi.org/10.24036/invotek.v20i3.854

References

  1. [1] H. Lasi, P. Fettke, H.-G. Kemper, T. Feld, and M. Hoffmann, “Industrie 4.0,” WIRTSCHAFTSINFORMATIK, vol. 56, no. 4, pp. 261–264, Aug. 2014, doi: 10.1007/s11576-014-0424-4.
  2. [2] J. Lee, B. Bagheri, and H.-A. Kao, “A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems,” Manuf. Lett., vol. 3, pp. 18–23, Jan. 2015, doi: 10.1016/j.mfglet.2014.12.001.
  3. [3] R. Y. Zhong, C. Xu, C. Chen, and G. Q. Huang, “Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors,” Int. J. Prod. Res., vol. 55, no. 9, pp. 2610–2621, May 2017, doi: 10.1080/00207543.2015.1086037.
  4. [4] B. Y. Qi, Q. L. Yang, and Y. Y. Zhou, “Application of AGV in intelligent logistics system,” in Fifth Asia International Symposium on Mechatronics (AISM 2015), 2015, pp. 1–5.
  5. [5] Y. U. Cao, A. S. Fukunaga, and A. Kahng, “Cooperative Mobile Robotics: Antecedents and Directions,” Auton. Robots, vol. 4, no. 1, pp. 7–27, 1997, doi: 10.1023/A:1008855018923.
  6. [6] G. Dudek, M. R. M. Jenkin, E. Milios, and D. Wilkes, “A taxonomy for multi-agent robotics,” Auton. Robots, vol. 3, no. 4, pp. 375–397, 1996.
  7. [7] A. Rosenfeld, N. Agmon, O. Maksimov, and S. Kraus, “Intelligent agent supporting human–multi-robot team collaboration,” Artif. Intell., vol. 252, pp. 211–231, 2017.
  8. [8] J. Gao, X. Hu, and C. Wu, “Design and simulation of multi-robot logistic system,” in 2006 2nd IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, 2006, pp. 1–6.
  9. [9] I. Andersone, “The characteristics of the map merging methods: A survey,” Appl. Comput. Syst., vol. 41, no. 1, pp. 113–121, 2010.
  10. [10] N. Karlsson, E. Di Bernardo, J. Ostrowski, L. Goncalves, P. Pirjanian, and M. E. Munich, “The vSLAM algorithm for robust localization and mapping,” in Proceedings of the 2005 IEEE international conference on robotics and automation, 2005, pp. 24–29.
  11. [11] K. Yousif, A. Bab-Hadiashar, and R. Hoseinnezhad, “An overview to visual odometry and visual SLAM: Applications to mobile robotics,” Intell. Ind. Syst., vol. 1, no. 4, pp. 289–311, 2015.
  12. [12] J. Shimamura, M. Morimoto, and H. Koike, “Robust vSLAM for Dynamic Scenes.,” in MVA, 2011, pp. 344–347.
  13. [13] R. Mur-Artal and J. D. Tardós, “Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras,” IEEE Trans. Robot., vol. 33, no. 5, pp. 1255–1262, 2017.
  14. [14] A. Buyval, I. Afanasyev, and E. Magid, “Comparative analysis of ROS-based monocular SLAM methods for indoor navigation,” in Ninth International Conference on Machine Vision (ICMV 2016), 2017, vol. 10341, p. 103411K.
  15. [15] P. Anggraeni, M. Mrabet, M. Defoort, and M. Djemai, “Development of a wireless communication platform for multiple-mobile robots using ROS,” in 2018 6th International Conference on Control Engineering & Information Technology (CEIT), 2018, pp. 1–6.
  16. [16] P. Schmuck and M. Chli, “CCM‐SLAM: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams,” J. F. Robot., vol. 36, no. 4, pp. 763–781, 2019.
  17. [17] Wikipedia contributors, “Robotino,” Wikipedia, The Free Encyclopedia., 2019. https://en.wikipedia.org/w/index.php?title=Robotino&oldid=898008008 (accessed Jan. 12, 2020).
  18. [18] S. F. R. Alves, J. M. Rosario, H. Ferasoli Filho, L. K. Rincon, R. A. Yamasaki, and A. Barrera, “Conceptual bases of robot navigation modeling control and applications,” Adv. Robot Navig., p. 26, 2011.
  19. [19] B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo, Robotics: modelling, planning and control. Springer Science & Business Media, 2010.
  20. [20] M. Andersson and M. Baerveldt, “Simultaneous localization and mapping for vehicles using ORB-SLAM2.” Master’s Thesis, Chalmers University of Technology, Gothenburg, Sweden, 2018.
  21. [21] A. Fusiello, “Elements of geometric computer vision,” Available fro m http//homepages. inf. ed. ac. uk/rbf/CVonline/LOCAL_COPIES/FUSIELLO4/tutorial. html, 2006.
  22. [22] Y. Pyo, H. Cho, J. Ryuwoon, and T. Lim, Robot Programming From The Basic Concept To Practical Programming and Robot Application. 2017.
  23. [23] Open Source Robotics Foundation, “Setting up your robot using tf,” http://wiki.ros.org, 2015. http://wiki.ros.org/navigation/Tutorials/RobotSetup/TF (accessed Jun. 06, 2020).
  24. [24] R. Mur-Artal, J. M. M. Montiel, and J. D. Tardos, “ORB-SLAM: a versatile and accurate monocular SLAM system,” IEEE Trans. Robot., vol. 31, no. 5, pp. 1147–1163, 2015.
  25. [25] C. Luo, W. Yang, P. Huang, and J. Zhou, “Overview of Image Matching Based on ORB Algorithm,” in Journal of Physics: Conference Series, 2019, vol. 1237, no. 3, p. 32020.