Article Sidebar

Download
PDF

Main Article Content

Abstract

Robots are one of the technological developments that unite several aspects, such as mechanical, electrical, and programs that are interrelated to one another. Currently, the robot has also been competed in a competition with various categories. One of them is the SAR (Search and Rescue) robot competition which is accommodated in the KRSRI (Indonesian SAR Robot Contest). This SAR robot has a mission to save and bring victims to a safe zone. To develop the robot so that it is more optimal, several studies have been carried out using various methods. In this study, the SAR robot was optimized with the Zero Order Fuzzy Sugeno Logic method. In addition, this study uses a sharp sensor as a distance sensor which has better capabilities than ultrasonic sensors. The results show that the time the robot to reach the finish line fluctuates or has various time results for each trial. The conclusion of this experiment is that the Fuzzy Logic method can be applied to SAR robots. Based on the generated robot motion, the robot can move straight without any tendency to the right or left of the trajectory barrier.

Keywords

fuzzy robot KRSRI Sistem Pengendalian Sensor Sharp Kinematik

Article Details

License

Copyright (c) 2024 Ryan Yudha Adhitya

Creative Commons License

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

References

  1. Setyawan, D. E. (2019). Sistem Robot Penyelamat Menggunakan Metode Deteksi Viola-Jones untuk Membantu Tim Penyelamat Menemukan Korban Bencana. ELKHA: Jurnal Teknik Elektro, 11(1), 27-32.
  2. Firmansyah, R. A., & Odinanto, T. (2017, October). Simulasi Pengenalan Pola Ruangan Menggunakan Jaringan Syaraf Tiruan Pada Aplikasi Sistem Lokalisasi Robot Rescue. In Seminar Nasional Sains dan Teknologi Terapan (Vol. 2017, pp. B85-B90).
  3. Budiharto, Widodo dan S. Derwin. “Artifical Intelligence konsep dan penerapannya”, Yogyakarta: Andi, 2014.
  4. R. Wahyudi, “Kontrol Kecepatan Robot Hexapod Pemadam Api menggunakan Metoda Logika Fuzzy”, Jurnal Nasional Teknik Elektro, vol. 4, no. 2, pp 227-234, 2015.
  5. D. L. Rahakbauw, “Penerapan Logika Fuzzy Metode Sugeno Untuk Menentukan Jumlah Produksi Roti Berdasarkan Data Persediaan Dan Jumlah Permintaan,” Barekeng:Jurnal Ilmu Matematika dan Terapan, vol. 9, no. 2, pp. 121–134, 2015.
  6. Coronado, Enrique, dkk. "Evaluating quality in human-robot interaction: A systematic search and classification of performance and human-centered factors, measures and metrics towards an industry 5.0.", Journal of Manufacturing Systems, vol. 63, pp. 392-410, 2022.
  7. Deng, Yinan, et al. "SEE-CSOM: Sharp-Edged and Efficient Continuous Semantic Occupancy Mapping for Mobile Robots." IEEE Transactions on Industrial Electronics (2023).
  8. HANA, Ahmad Nurul; SUPRIYADI, Slamet; MUKHTAR, Agus. Analisa Kinerja Sensor Inframerah Sharp Gp2y0a21 Dan Ultrasonik Hc-Sr04 Konsep Deteksi Halangan Pada Robot Otonom Berkaki Penyemprot Disinfektan KRI 2020. In: Proceeding Science and Engineering National Seminar. 2020. p. 305-309.
  9. BROWN, Geoffrey. Discovering the STM32 microcontroller. Cortex, 2012, 3.34: 64.
  10. Jacko, P., Bereš, M., Kováčová, I., Molnár, J., Vince, T., Dziak, J., ... & Kováč, D. (2022). Remote IoT Education Laboratory for Microcontrollers Based on the STM32 Chips. Sensors, 22(4), 1440.
  11. Khatibul Umam, Haryanto dan Riza Alfita. Rancang Bangun Robot Pembersih Kaca Otomatis Berbasis Mikrokontroller ARM STM32 dengan Sensor Proximity. Indonesian Journal of Engineering and Technology (INAJET). 2019, 2(1):24-29.
  12. ROIS’AM, Kemalasari; SUMANTRI, Bambang; WIJAYANTO, Ardik. Pengaturan Posisi Motor Servo DC Dengan Metode Fuzzy Logic. Metode, 2010, 1: 2.1
  13. S. Kusumadewi dan H. Purnomo, “Aplikasi Logika Fuzzy untuk Pendukung Keputusan”, Yogyakarta: Graha Ilmu, 2004.
  14. L. Fatihan, “Sistem Kendali Robot Pemadam Api Quadruped Dengan Metode Fuzzy Logic,” Tugas Akhir. Palembang: Politeknik Negeri Sriwijaya, 2018.