Issue

Vol. 11, No. 4, November 2026 (Article in Progress)

Creative Commons License

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

A Digital Twin–Based Traffic Control System for Heterogeneous AMRs Using Type-2 Fuzzy Logic

https://doi.org/10.22219/kinetik.v11i4.2790
Muhammad Fauzan Hernadi
Universitas Pendidikan Indonesia
Muhammad Rizalul Wahid
Universitas Pendidikan Indonesia
Liptia Venica
Universitas Pendidikan Indonesia

Corresponding Author(s) : Muhammad Fauzan Hernadi

fauzanhernadi004@upi.edu

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 11, No. 4, November 2026 (Article in Progress)

Abstract

The simultaneous operation of multi-brand Autonomous Mobile Robots (AMRs) introduced interoperability challenges that could lead to path conflicts and collisions between AMRs operating within the same industrial environment. This study proposed a Type-2 Fuzzy logic-based control system integrated with a Digital Twin framework to coordinate the movement of two heterogeneous AMRs, the Hikrobot and the AutoXing units, and to enhance operational safety during simultaneous operation. Unlike prior single-brand or simulation-only approaches, the proposed framework combined real-time Digital Twin monitoring with Type-2 Fuzzy Logic to manage uncertainty in inter-AMR distance and speed data, enabling stop-and-go decisions for the Hikrobot AMR while validating system behavior in a virtual environment prior to physical deployment. Experimental results showed that the baseline method produced 2 inter-AMR collisions, whereas both the Type-2 and Type-1 Fuzzy control systems eliminated all collisions and deadlock conflicts across the tested scenarios. However, this improvement introduced a trade-off in travel time: the baseline method averaged 57 seconds, the Type-1 Fuzzy system averaged 96 seconds, and the Type-2 Fuzzy system averaged 79 seconds, approximately 18% faster than Type-1 despite the added computational complexity of type-reduction. The Type-2 system's response latency was also 0.2 seconds higher than that of Type-1, a difference that remained within a safe operational margin. Overall, the Type-2 Fuzzy system demonstrated superior control decision stability under dynamic conditions with only a marginal latency cost, indicating that the proposed Digital Twin-based Type-2 Fuzzy framework offered a more balanced trade-off between safety and responsiveness than the Type-1 approach.

Keywords

Autonomous Mobile Robot Digital Twin Type-2 Fuzzy AMR
Hernadi, M. F., Wahid, M. R., & Venica, L. (2026). A Digital Twin–Based Traffic Control System for Heterogeneous AMRs Using Type-2 Fuzzy Logic. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 11(4). https://doi.org/10.22219/kinetik.v11i4.2790
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. G. Fragapane, R. de Koster, F. Sgarbossa, dan J. O. Strandhagen, “Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda,” Eur. J. Oper. Res., vol. 294, no. 2, hal. 405–426, 2021, doi: 10.1016/j.ejor.2021.01.019.
  2. T. Lackner, J. Hermann, C. Kuhn, dan D. Palm, “Review of autonomous mobile robots in intralogistics: state-of-the-art, limitations and research gaps,” Procedia CIRP, vol. 130, hal. 930–935, 2024, doi: 10.1016/j.procir.2024.10.187.
  3. R. Hercik, R. Byrtus, R. Jaros, dan J. Koziorek, “Implementation of Autonomous Mobile Robot in SmartFactory,” Appl. Sci., vol. 12, no. 17, 2022, doi: 10.3390/app12178912.
  4. I. Kubasáková, J. Kubáňová, D. Benčo, dan N. Fábryová, “Application of Autonomous Mobile Robot as a Substitute for Human Factor in Order to Increase Efficiency and Safety in a Company,” Appl. Sci., vol. 14, no. 13, 2024, doi: 10.3390/app14135859.
  5. G. C. Da Costa, C. M. T. Rodríguez, dan J. C. Leite, “Developing An Amr Prototype With Processing Offloading Using 5g Servers For Industry 4.0,” IOSR J. Bus. Manag., vol. 26, no. 9, hal. 01–14, 2024, doi: 10.9790/487x-2609120114.
  6. J. A. Douthwaite dkk., “A Modular Digital Twinning Framework for Safety Assurance of Collaborative Robotics,” Front. Robot. AI, vol. 8, no. December, hal. 1–17, 2021, doi: 10.3389/frobt.2021.758099.
  7. P. Stączek, J. Pizoń, W. Danilczuk, dan A. Gola, “A digital twin approach for the improvement of an autonomous mobile robots (AMR’s) operating environment—A case study,” Sensors, vol. 21, no. 23, 2021, doi: 10.3390/s21237830.
  8. S. Jeong, T. Ga, I. Jeong, J. Oh, dan J. Choi, “Layered-Cost-Map-Based Traffic Management for Multiple AMRs via a DDS,” Appl. Sci., vol. 12, no. 16, 2022, doi: 10.3390/app12168084.
  9. R. Vrabič, A. Malus, J. Dvoršak, G. Klančar, dan T. Žužek, “Bio-Inspired Traffic Pattern Generation for Multi-AMR Systems,” Appl. Sci., vol. 15, no. 5, hal. 1–22, 2025, doi: 10.3390/app15052849.
  10. S. M. Langbak, C. Schou, dan K. D. Hansen, “Multi-Autonomous Mobile Robot Traffic Management Based on Layered Costmaps and a Modified Dijkstra’s Algorithm,” Procedia Comput. Sci., vol. 232, no. 2023, hal. 53–63, 2024, doi: 10.1016/j.procs.2024.01.006.
  11. F. Cuevas, O. Castillo, dan P. Cortes, “Optimal Setting of Membership Functions for Interval Type-2 Fuzzy Tracking Controllers Using a Shark Smell Metaheuristic Algorithm,” Int. J. Fuzzy Syst., vol. 24, no. 2, hal. 799–822, 2022, doi: 10.1007/s40815-021-01136-4.
  12. S. K. Sampathkumar, D. Choi, dan D. Kim, “Fuzzy inference system-assisted human-aware navigation framework based on enhanced potential field,” Complex Eng. Syst., vol. 4, no. 1, 2024, doi: 10.20517/ces.2023.34.
  13. M. Dobrzańska dan P. Dobrzański, “Simulation Model as an Element of Sustainable Autonomous Mobile Robot Fleet Management,” Energies, vol. 18, no. 8, hal. 1–18, 2025, doi: 10.3390/en18081894.
  14. J. Hažík, M. Dekan, P. Beňo, dan F. Duchoň, “Fleet Management System for an Industry Environment,” J. Robot. Control, vol. 3, no. 6, hal. 779–789, 2022, doi: 10.18196/jrc.v3i6.16298.
  15. S. T. Atik, A. S. Chavan, D. Grosu, dan M. Brocanelli, “A Maintenance-Aware Approach for Sustainable Autonomous Mobile Robot Fleet Management,” IEEE Trans. Mob. Comput., vol. 23, no. 6, hal. 7394–7407, 2024, doi: 10.1109/TMC.2023.3334589.
  16. E. E. Turhanlar, B. Y. Ekren, dan T. Lerher, “Autonomous mobile robot travel under deadlock and collision prevention algorithms by agent-based modelling in warehouses,” Int. J. Logist. Res. Appl., vol. 27, no. 8, hal. 1322–1341, 2024, doi: 10.1080/13675567.2022.2138290.
  17. D. Lopes dkk., “Integrated Fleet Management of Mobile Robots for Enhancing Industrial Efficiency: A Case Study on Interoperability in Multi-Brand Environments Within the Automotive Sector,” Appl. Sci., vol. 15, no. 13, hal. 1–24, 2025, doi: 10.3390/app15137235.
  18. H. Qin, Y. Wang, X. Ma, J. Wang, dan C. Jiang, “A Euclidean Distance-based parameter reduction algorithm for interval-valued fuzzy soft sets,” Expert Syst. Appl., vol. 234, no. August 2021, hal. 121106, 2023, doi: 10.1016/j.eswa.2023.121106.
  19. O. Y. Ismael, M. Almaged, dan A. I. Abdulla, “Nonlinear Model Predictive Control-based Collision Avoidance for Mobile Robot,” J. Robot. Control, vol. 5, no. 1, 2024, doi: 10.18196/jrc.v5i1.20615.
  20. M. R. Wahid, E. Joelianto, B. A. Budiman, J. Pradipta, dan A. Ramadhani, “Experimental Validation and Model-Based Optimization of Regenerative Braking in Light Electric Vehicles Using IT2FL and DDPG-RL,” Int. Conf. Electr. Veh. Technol., 2025, doi: 10..1109/ICEVT67191.2025.11183981.
  21. M. R. Wahid, E. Joelianto, B. A. Budiman, M. P. Dewanata, dan M. Aziz, “Optimizing regenerative braking in light electric vehicles using deep deterministic policy gradient reinforcement learning,” Egypt. Informatics J., vol. 33, no. March 2025, hal. 100893, 2026, doi: 10.1016/j.eij.2026.100893.
  22. S. Liu dan R. Cai, “Uncertainty of interval type-2 fuzzy sets based on fuzzy belief entropy,” Entropy, vol. 23, no. 10, hal. 1–13, 2021, doi: 10.3390/e23101265.
  23. Y. Mhanni dan Y. Lagmich, “Enhanced Obstacle Avoidance and Intelligent Navigation for Mobile Robots: An Integrated Approach Using Fuzzy Logic and an Optimized APF Method,” Math. Model. Eng. Probl., vol. 10, no. 6, hal. 2111–2120, 2023, doi: 10.18280/mmep.100622.
  24. S. H. Khairuddin, M. H. Hasan, E. A. P. Akhir, dan M. A. Hashmani, “Generating type 2 trapezoidal fuzzy membership function using genetic tuning,” Comput. Mater. Contin., vol. 71, no. 1, hal. 717–734, 2022, doi: 10.32604/cmc.2022.020666.
  25. J. Handoyo, A. Yudhana, dan S. Sunardi, “Flood Vulnerability Mapping in Cepu Subdistrict Using Mamdani Fuzzy Inference System for Disaster Risk Reduction,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 25, no. 1, hal. 25–38, 2025, doi: 10.30812/matrik.v25i1.5390.
  26. G. S. Mada, N. K. F. Dethan, dan A. E. S. H. Maharani, “The Defuzzification Methods Comparison of Mamdani Fuzzy Inference System in Predicting Tofu Production,” J. Varian, vol. 5, no. 2, hal. 137–148, 2022, doi: 10.30812/varian.v5i2.1816.
  27. Y. Chen, J. Yang, dan C. Li, “Design of reasonable initialization weighted enhanced Karnik-Mendel algorithms for centroid type-reduction of interval type-2 fuzzy logic systems,” AIMS Math., vol. 7, no. 6, hal. 9846–9870, 2022, doi: 10.3934/math.2022549.
  28. Y. Chen, “Study on non-iterative algorithms for center-of-sets type-reduction of Takagi–Sugeno–Kang type general type-2 fuzzy logic systems,” Complex Intell. Syst., vol. 9, no. 4, hal. 4015–4023, 2023, doi: 10.1007/s40747-022-00927-y.
Read More
Author biographies is not available.
Download this PDF file
Statistic
Read Counter : 0

Downloads

Download data is not yet available.