NUMERICAL SIMULATION OF MOBILE ROBOTIC PLATFORM ROUTE CONSTRUCTION IN DYNAMIC SPACE USING QR TAGS

І. Ш. Невлюдов, В. В. Євсєєв, С. С. Максимова, С. І. Теслюк

Abstract


In the modern conditions of Industry 5.0 development, mobile robotic platforms play a key role in ensuring the automation of logistics and production processes. It requires the development of effective navigation methods in a dynamic environment with static and moving obstacles. The task of adaptive formation of a movement route based on sensory information, in particular QR tags, is of particular relevance. It allows for the prompt determination of the coordinates of target points and provide flexibility in controlling the mobile platform. The object of the study is the process of moving a mobile platform in a discrete dynamic workspace. The subject of the study is a method for constructing a mobile platform route using an incremental replanning algorithm and a model for determining target coordinates based on QR tags. The purpose of the study is a mathematical model development and software implementation of a method for constructing an optimal mobile platform route in a dynamic environment, taking into account changes in the space configuration and prompt determination of the coordinates of target points based on QR tags. The study used methods of mathematical modeling, graph theory, numerical integration, incremental pathfinding algorithms and computer modeling using a discrete occupancy map. The scientific novelty of the work lies in the development of mathematical support for the method of constructing a mobile platform route with the integration of a target observation model based on QR tags. This allows for adaptive replanning of the trajectory in real time when the state of the environment changes. The obtained results of numerical modeling confirm the effectiveness of the proposed method, which ensures safe movement of the mobile platform, adaptive avoidance of obstacles and stable achievement of target points with high computational efficiency. The developed model demonstrates the potential for implementation in intelligent control systems for mobile robots and can be used as a basis for further improvement of navigation algorithms in dynamic environments.


Keywords


mobile robotic platform, path planning, dynamic environment, D* Lite algorithm, occupancy grid, numerical simulation, QR codes, incremental replanning, autonomous navigation, Industry 5.0

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References


Flores Gonzalez, J. M., Coronado, E., & Yamanobe, N. (2025). ROS-Compatible Robotics Simulators for Industry 4.0 and Industry 5.0: A Systematic Review of Trends and Technologies. Applied Sciences, 15(15), 8637. https://doi.org/10.3390/app15158637.

Alonso, R., Sánchez, T. F., Alfaro, D. A., Cruz, Y. J., Villalonga, A., & Castaño, F. (2025). Automation and robotics pilot lines in the context of industry 5.0. Applied Sciences, 15(5), 2510. https://doi.org/10.3390/app15052510.

Murthy, M. N., Lakshminarayanan, S., & Alagarsamy, S. (2026). Nurse training in the era of Industry 5.0: Evidence from a systematic literature review (2015–2024). Nurse Education in Practice, 92, 104769. https://doi.org/10.1016/j.nepr.2026.104769.

Nevliudov, I., Yevsieiev, V., Maksymova, S., Gopejenko, V., & Kosenko, V. (2025). Development of mathematical support for adaptive control for the intelligent gripper of the collaborative robot manipulator. Advanced Information Systems, 9(3), 57–65. https://doi.org/10.20998/2522-9052.2025.3.07.

Yao, L., Li, G., Zhang, T., Hussien, A. G., & Lu, Y. (2026). Adaptive multi-step path planning for multi-robot in dynamic environments based on hybrid optimization approach. Expert Systems with Applications, 298, 129699. https://doi.org/10.1016/j.eswa.2025.129699.

Wang, Y., & Wang, G. (2026). An adaptive and efficient path planning algorithm for UAV navigation in complex environments. Computers & Operations Research, 185, 107296. https://doi.org/10.1016/j.cor.2025.107296.

Pang, Z., Qian, L., Hong, M., & Li, D. (2026). An intelligent route planning algorithm integrating Q-learning and A* for dynamic marine environments. Journal of Marine Science and Technology, 1-28. https://doi.org/10.1007/s00773-026-01107-0.

Choi, J., & Choi, Y. (2025). Path Planning for Unmanned Aerial Vehicle: A-Star-Guided Potential Field Method. Drones, 9(8), 545. https://doi.org/10.3390/drones9080545.

Nevliudov, I., Omarov, M., Yevsieiev, V., Maksymova, S., & Jabrayilzade, E. (2026). Mathematical modeling of trajectories construction, movement of the gripping device of a collaborative robot. Advanced Information Systems, 10(1), 11–20. https://doi.org/10.20998/2522-9052.2026.1.02.

Akram, M. W., Sood, K., Hassan, M. U., & Thiruvady, D. (2026). ALFA: A Safe-by-Design Approach to Mitigate Quishing Attacks Launched via Fancy QR Codes. arXiv preprint arXiv:2601.06768. https://doi.org/10.48550/arXiv.2601.06768

Rios, O., Gauci, M. O., Debortoli, C., Alunni, V., & Savoldelli, C. (2026). A 3D-Printed Craniofacial Fracture Library with QR-Integrated Pedagogical Content: A technical note for Maxillofacial Trauma Teaching. Journal of Stomatology Oral and Maxillofacial Surgery, 102736. https://doi.org/10.1016/j.jormas.2026.102736.

Improvement of the Encoding Information Method for Pharmaceutical Products QR-Codes During Sorting on a Robotic Conveyor Line / I. Nevliudov, O. Klymenko, V. Yevsieiev, S. Maksymova. // Bulletin of the National Technical University "KhPI", series: Techniques in a machine industry. – 2025. – No. 1(11). – P. 128-134. – DOI : https://doi.org/10.20998/2079-004X.2025.1(11).15.

Liao, J. W., Wang, W., Wang, T. S., Peng, L. X., Weng, J. H., Chou, C. F., & Chen, J. C. (2025, February). DiffQRCoder: Diffusion-based aesthetic qr code generation with scanning robustness guided iterative refinement. In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 5916-5925). IEEE. https://doi.org/10.1109/WACV61041.2025.00577.

Shen, Y., Shen, Y., Zhang, Y., Huo, C., Shen, Z., Su, W., & Liu, H. (2025). Research Progress on Path Planning and Tracking Control Methods for Orchard Mobile Robots in Complex Scenarios. Agriculture, 15(18), 1917. https://doi.org/10.3390/agriculture15181917.

Shanmugaraja, M., Thangamuthu, M., & Ganesan, S. (2025). Hybrid Path Planning Algorithm for Autonomous Mobile Robots: A Comprehensive Review. Journal of Sensor and Actuator Networks, 14(5), 87. https://doi.org/10.3390/jsan14050087

Jin, P., Li, W., Zhan, J., & Shan, C. (2026). 3D Obstacle Avoidance Path Planning Algorithm and Software Design for UUV Based on Improved D* Lite-APF. Journal of Marine Science and Engineering, 14(4), 373. https://doi.org/10.3390/jmse14040373.

Wu, H., Zhong, Y., Zhang, K., Zhou, L., Zhang, Y., Mei, X., ... & Sun, T. (2026). Improved A∗+ D∗ Lite hierarchical framework path planning algorithm for heterogeneous sea ice scenarios. Ocean Engineering, 343, 123387. https://doi.org/10.1016/j.oceaneng.2025.123387.

Mlinarček, D., Jánoš, R., Málik, M., Svetlík, J., Semjon, J., & Ondočko, Š. (2026). Costmap Tuning for Autonomous Navigation: A Simulation and Real-World Study on the Hiwonder JetAcker. Applied Sciences, 16(4), 1923. https://doi.org/10.3390/app16041923.

Liang, Z., Wang, L., Wang, H., Zhang, B., & Liu, C. (2026). Autonomous obstacle avoidance and path planning for mobile robots in orchard environments combining with map construction and positioning methods. Computers and Electronics in Agriculture, 244, 111514. https://doi.org/10.1016/j.compag.2026.111514.

Building a Route for a Mobile Robot Based on the BRRT and A*(H-BRRT) Algorithms for the Effective Development of Technological Innovations / Amer Abu-Jassar, Hassan Al-Sukhni, Yasser Al-Sharo, S. Maksymova, V. Yevsieiev, V. Lyashenko // International Journal of Engineering Trends and Technology. – 2024. – V. 72(11). – P. 294-306. https://doi.org/10.14445/22315381/IJETT-V72I11P129.

Yuan, C., Zhao, K., Kuruoglu, E. E., Wang, L., Xu, T., Huang, W., ... & Rong, Y. (2025). A survey of graph transformers: Architectures, theories and applications. arXiv preprint arXiv:2502.16533. https://doi.org/10.48550/arXiv.2502.16533.

Ye, J., Niu, L., Xu, X., & Wang, Y. (2025, June). Indoor navigation path planning method based on improved D* Lite algorithm for complex environments. In IET Conference Proceedings CP931 (Vol. 2025, No. 23, pp. 94-100). Stevenage, UK: The Institution of Engineering and Technology. https://doi.org/10.1049/icp.2025.2854.

Yadav, H. (2025). Evaluating A* Heuristics Across Diverse Map Types for Robotic Navigation. Authorea Preprints.

Kahar, M. A. B., Bingi, K., Ibrahim, R., & Omar, M. (2025, June). Review on Global Path Planning Algorithms for Autonomous Navigation of Multiple Mobile Robots. In 2025 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) (Vol. 1, pp. 202-207). IEEE. https://doi.org/10.1109/I2CACIS65476.2025.11100872.

Abu-Jassar, A. T., Attar, H., Amer, A., Lyashenko, V., Yevsieiev, V., & Solyman, A. (2025). Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment. International Journal of Crowd Science, 9(1), 29-43. https://doi.org/10.26599/IJCS.2023.9100018.

Nevliudov, V. Yevsieiev, S. Maksymova, N. Demska, K. Kolesnyk, Olha Miliutina, "Mobile Robot Navigation System Based on Ultrasonic Sensors", 2023 IEEE XXVIII International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory (DIPED), vol.1, pp.247-251, 2023. https://doi.org/10.1109/DIPED59408.2023.10269500.

Moumoula, M. B., Kabore, A. K., Klein, J., & Bissyande, T. F. (2025). Evaluating programming language confusion. arXiv preprint arXiv:2503.13620. https://doi.org/10.48550/arXiv.2503.13620.

Rouhandeh, H., & Behroozsarand, A. (2025). Simulation and optimization of methanol production process via bi-reforming of methane: A novel genetic algorithm-based approach in Python. International Journal of Hydrogen Energy, 101, 1161-1171. https://doi.org/10.1016/j.ijhydene.2025.01.003.

Yevsieiev, V., Gurin, D., Kulish, S., & Voloshyn, Y. (2025). Development of a partially supervised Markov decision-making model for a 3-link collaborative robot-manipulator. Radioelectronic and Computer Systems, 2025(4), 83-94. doi: https://doi.org/10.32620/reks.2025.4.06.




DOI: https://doi.org/10.32620/oikit.2026.108.17

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