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Abstrak

Kebutuhan akan otomatisasi layanan kesehatan mendorong pengembangan robot logistik berbasis Internet of Things (IoT) untuk pengiriman obat-obatan dan pemantauan pasien, khususnya pada kasus penyakit menular. Studi ini mengembangkan prototipe robot layanan rumah sakit dengan sistem navigasi differential drive dan algoritma Region-Reaching Control (RRC) guna memastikan pergerakan presisi dalam lorong sempit. Robot mendukung dua mode navigasi, yaitu manual melalui remote control dan otomatis berbasis area target. Hasil simulasi menunjukkan bahwa pendekatan RRC mampu menurunkan Mean Absolute Error (MAE) posisi hingga 0.135 m pada su`mbu x dan y, serta MAE orientasi 0.095 m pada sumbu (yaw) sebesar 0.178 rad, jauh lebih kecil dibanding kendali PD konvensional. Sistem komunikasi wireless menunjukkan waktu respon rata-rata 120–940 ms, dengan jangkauan efektif mencapai 80 meter di ruang terbuka dan 40 meter di ruang tertutup. Integrasi kamera omni-infrared dan kontrol jarak jauh memungkinkan operasional tanpa kontak langsung dengan pasien. Dengan akurasi tinggi dan fleksibilitas kontrol, prototipe ini menawarkan solusi efisien dan adaptif untuk distribusi logistik medis, sekaligus meningkatkan keselamatan tenaga kesehatan di lingkungan berisiko tinggi.

Kata Kunci

Navigasi dan kendali jarak jauh Otomatisasi layanan kesehatan; Pemantauan pasien Pengiriman obat-obatan Robot logistik berbasis IoT

Rincian Artikel

Biografi Penulis

Anifatul Faricha, Universitas Telkom Surabaya

Teknik Elektro, Fakultas Teknik Elektro

Cara Mengutip
[1]
M. Yani, “Robot Logistik Berbasis IoT untuk Pengiriman Obat dan Monitoring Pasien Secara Otomatis”, Jurnal Algoritma, vol. 22, no. 1, hlm. 789–800, Mei 2025.

References

  1. M. Yani, A. R. A. Besari, N. Yamada, and N. Kubota, ‘Ecological-Inspired System Design for Safety Manipulation Strategy in Home-care Robot’, 2020 Int. Symp. Community-Centric Syst. CcS 2020, 2020, doi: 10.1109/CcS49175.2020.9231354.
  2. M. Yani, N. Yamada, C. Z. Siow, and N. Kubota, ‘An efficient activity recognition for homecare robots from multi-modal communication dataset’, Int. J. Adv. Intell. Informatics, vol. 9, no. 1, pp. 39–50, Mar. 2023, doi: 10.26555/ijain.v9i1.903.
  3. N. Yamada, M. Yani, and N. Kubota, ‘Interactive adaptation of Hand-over Motion by a Robot Partner for Comfort of receiving’, pp. 1899–1904, 2021, doi: 10.1109/ssci47803.2020.9308289.
  4. J. Bohren et al., ‘Towards autonomous robotic butlers: Lessons learned with the PR2’, Proc. - IEEE Int. Conf. Robot. Autom., pp. 5568–5575, 2011, doi: 10.1109/ICRA.2011.5980058.
  5. S. K. Paul, M. T. Chowdhury, M. Nicolescu, M. Nicolescu, and D. Feil-Seifer, ‘Object Detection and Pose Estimation from RGB and Depth Data for Real-Time, Adaptive Robotic Grasping’, pp. 121–142, 2021, doi: 10.1007/978-3-030-71051-4_10.
  6. T. Yamamoto, K. Terada, A. Ochiai, F. Saito, Y. Asahara, and K. Murase, ‘Development of Human Support Robot as the research platform of a domestic mobile manipulator’, ROBOMECH J., vol. 6, no. 1, 2019, doi: 10.1186/s40648-019-0132-3.
  7. R. Guldenring, M. Gorner, N. Hendrich, N. J. Jacobsen, and J. Zhang, ‘Learning local planners for human-aware navigation in indoor environments’, IEEE Int. Conf. Intell. Robot. Syst., pp. 6053–6060, 2020, doi: 10.1109/IROS45743.2020.9341783.
  8. F. Firouzi et al., ‘Harnessing the power of smart and connected health to tackle COVID-19: IoT, AI, robotics, and blockchain for a better world’, IEEE Internet Things J., vol. 8, no. 16, pp. 12826–12846, 2021, doi: 10.1109/JIOT.2021.3073904.
  9. H. Kabir, M.-L. Tham, and Y. C. Chang, ‘Internet of robotic things for mobile robots: concepts, technologies, challenges, applications, and future directions’, Digit. Commun. Networks, vol. 9, no. 6, pp. 1265–1290, 2023, doi: https://doi.org/10.1016/j.dcan.2023.05.006.
  10. J. F. Rusdi, A. Nurhayati, H. Gusdevi, M. I. Fathulloh, A. Priyono, and R. Hardi, ‘IoT-based Covid-19 Patient Service Robot Design’, in 2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS), IEEE, 2021, pp. 1–6.
  11. I. Hafidz, D. Adiputra, B. Montolalu, W. A. Prastyabudi, H. Widyantara, and M. A. Afandi, ‘IoT-Based Logistic Robot for Real-Time Monitoring and Control Patients during COVID-19 Pandemic’, J. Nas. Tek. ELEKTRO, vol. 9, no. 3, 2020, doi: 10.25077/jnte.v9n3.810.2020.
  12. P. Corke, Robotics, Vision and Control. Springer, Cham, 2023. doi: https://doi.org/10.1007/978-3-031-06469-2_15.
  13. R. Raj and A. Kos, ‘A comprehensive study of mobile robot: History, developments, applications, and future research perspectives’, Appl. Sci., vol. 12, no. 14, p. 6951, 2022.
  14. P. Corke, W. Jachimczyk, and R. Pillat, ‘Mobile robot vehicles’, in Robotics, Vision and Control: Fundamental Algorithms in MATLAB®, Springer, 2023, pp. 127–160.
  15. A. Hegde, Robotics: Vision and Control Techniques. Educohack Press, 2025.
  16. J. Yu and M. Wu, ‘Adaptive region-reaching control for a non-holonomic mobile robot via system decomposition approach’, Int. J. Control, pp. 1–8, 2024, doi: 10.1080/00207179.2024.2325459.
  17. M. B. M. Mokhar, Z. H. Ismail, M. Yani, and M. W. Dunnigan, ‘Attitude control with a region-based method for an unmanned aerial vehicle’, in 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015, IEEE, 2015. doi: 10.1109/ASCC.2015.7244837.
  18. C. C. Cheah and X. Li, Task-Space Sensory Feedback Control of Robot Manipulators. 2015.
  19. X. Li, Q. Lu, J. Chen, N. Jiang, and K. Li, ‘A Robust Region Control Approach for Simultaneous Trajectory Tracking and Compliant Physical Human–Robot Interaction’, IEEE Trans. Syst. Man, Cybern. Syst., vol. 53, no. 10, pp. 6388–6400, 2023, doi: 10.1109/TSMC.2023.3285603.
  20. C. Shang, H. Fang, Q. Yang, and J. Chen, ‘Distributed hierarchical shared control for flexible multirobot maneuver through dense undetectable obstacles’, IEEE Trans. Cybern., vol. 53, no. 5, pp. 2930–2943, 2021, doi: 10.1109/TCYB.2021.3125149.
  21. V. Putranti, Z. H. Ismail, and T. Namerikawa, ‘Robust-formation control of multi-Autonomous Underwater Vehicles based on consensus algorithm’, in 2016 IEEE Conference on Control Applications (CCA), IEEE, Sep. 2016, pp. 1250–1255. doi: 10.1109/CCA.2016.7587978.