Development and experimental evaluation of a low-cost ROS2-enabled autonomous carrier robot for structured factory automation
DOI:
https://doi.org/10.15282/isse.1.1.2026.14255Keywords:
Smart product carrier, Factory automation, Robot operating system, Autonomous mobile robot, Industry 4.0Abstract
In this paper a low-cost autonomous carrier robot is developed for structured factory transportation tasks. The system is based on Robot Operating System 2 (ROS2) running on a Raspberry Pi 4. It works together with two distributed ESP32 microcontrollers. Obstacle detection, line-following navigation and platform identification tasks are performed using ultrasonic sensors, infrared sensors and radio frequency identification (RFID) modules. The communication between the carrier robots and the central control node is implemented using ROS2 and Message Queuing Telemetry Transport (MQTT) . Experiments were conducted in an indoor factory-like environment. Navigation performance, obstacle avoidance, RFID detection, and communication behaviour were evaluated. Navigation tests were performed using different track widths and movement conditions. A navigation success rate of up to 90% was recorded during repeated trials. Two obstacle avoidance methods were evaluated. The reverse-and-forward method completed all test trials successfully. RFID platform identification was tested using different line spacing configurations, and a 100% detection rate was obtained at the selected operating condition. Communication tests showed low message delay during task coordination between carrier robots and the central control node. The developed system combines ROS2 communication, MQTT messaging, distributed ESP32 processing, and RFID-assisted navigation within a single carrier platform. The robot was evaluated under structured operating conditions using low-cost hardware components commonly available for embedded robotic applications.
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[1] S. Deokar, V. Bansode, S. Wankhede, and P. Kharche, “Industry 4.0 moving towards smart manufacturing: A comprehensive review,” Int. J. Sci. Res. Arch., vol. 16, pp. 630–642, Sep. 2025.
[2] M. Attaran, S. Attaran, and B. G. Celik, “The impact of digital twins on the evolution of intelligent manufacturing and Industry 4.0,” Adv. Comput. Intell., vol. 3, no. 3, p. 11, Jun. 2023.
[3] K. Höse, A. Amaral, U. Götze, and P. Peças, “Manufacturing Flexibility through Industry 4.0 Technological Concepts—Impact and Assessment,” Glob. J. Flex. Syst. Manag., vol. 24, no. 2, pp. 271–289, 2023.
[4] L. Yang, H. Zou, C. Shang, X. Ye, and P. Rani, “Adoption of information and digital technologies for sustainable smart manufacturing systems for industry 4.0 in small, medium, and micro enterprises (SMMEs),” Technol. Forecast. Soc. Change, vol. 188, p. 122308, Mar. 2023.
[5] M. Faccio et al., “Human factors in cobot era: a review of modern production systems features,” J. Intell. Manuf., vol. 34, no. 1, pp. 85–106, Jan. 2023.
[6] O. E. Oluyisola, S. Bhalla, F. Sgarbossa, and J. O. Strandhagen, “Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study,” J. Intell. Manuf., vol. 33, no. 1, pp. 311–332, Jan. 2022.
[7] A. Bhargava, Mohd. Suhaib, and A. S. Singholi, “A review of recent advances, techniques, and control algorithms for automated guided vehicle systems,” J. Braz. Soc. Mech. Sci. Eng., vol. 46, no. 7, p. 419, Jun. 2024.
[8] X. Zhao and T. Chidambareswaran, “Autonomous Mobile Robots in Manufacturing Operations,” in 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), Aug. 2023, pp. 1–7.
[9] G. Fragapane, D. Ivanov, M. Peron, F. Sgarbossa, and J. O. Strandhagen, “Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics,” Ann. Oper. Res., vol. 308, no. 1, pp. 125–143, Jan. 2022.
[10] Y. Liu, S. Wang, Y. Xie, T. Xiong, and M. Wu, “A Review of Sensing Technologies for Indoor Autonomous Mobile Robots,” Sensors, vol. 24, no. 4, p. 1222, Jan. 2024.
[11] M. Tolani, A. A. Ajasa, A. Balodi, A. Bajpai, Y. AlZaharani, and Sunny, “Advanced Sensor Systems for Robotics and Autonomous Vehicles,” in Artificial Intelligence for Robotics and Autonomous Systems Applications, A. T. Azar and A. Koubaa, Eds. Cham: Springer International Publishing, 2023, pp. 439–459.
[12] A. Das and R. K. Jain, “Performance evaluation of mobile robotic system for monitoring and inspection using MQTT protocol and IoT technique,” Int. J. Intell. Robot. Appl., vol. 9, no. 4, pp. 1302–1330, Dec. 2025.
[13] K. K, “The Evolution of Robot Operating Systems: A Review of ROS 2,” Zenodo, Jun. 2025.
[14] D. Casini, J.-J. Chen, J. Li, F. Reghenzani, and H. Teper, “A Survey of Real-Time Support, Analysis, and Advancements in ROS 2,” arXiv preprint arXiv:2601.10722, Dec. 2025.
[15] S. Macenski, T. Foote, B. Gerkey, C. Lalancette, and W. Woodall, “Robot Operating System 2: Design, Architecture, and Uses In The Wild,” Sci. Robot., vol. 7, no. 66, p. eabm6074, May 2022.
[16] S. D’Avella, C. A. Avizzano, and P. Tripicchio, “ROS-Industrial based robotic cell for Industry 4.0: Eye-in-hand stereo camera and visual servoing for flexible, fast, and accurate picking and hooking in the production line,” Robot. Comput.-Integr. Manuf., vol. 80, p. 102453, Apr. 2023.
[17] N. Demir, P. Demircioğlu, and İ. Böğrekci, “Advancing Industry 4.0 with ROS: A case study on autonomous mobile robot technological advancements,” Int. J. 3D Print. Technol. Digit. Ind., vol. 8, no. 1, pp. 130–142, Apr. 2024.
[18] I. H. Savci, A. Yilmaz, S. Karaman, H. Ocakli, and H. Temeltas, “Improving Navigation Stack of a ROS-Enabled Industrial Autonomous Mobile Robot (AMR) to be Incorporated in a Large-Scale Automotive Production,” Int. J. Adv. Manuf. Technol., vol. 120, no. 5, pp. 3647–3668, May 2022.
[19] W. Jo, J. Kim, R. Wang, J. Pan, R. K. Senthilkumaran, and B.-C. Min, “SMARTmBOT: A ROS2-based Low-cost and Open-source Mobile Robot Platform,” arXiv preprint arXiv:2203.08903, Mar. 2022.
[20] A. Prasad, A. M. Nair, A. K. J. Nair, G. Govind, K. L. Nisha, and J. Divya Udayan, “Design and Implementation of Robot Operating System 2 (ROS 2) Controlled Robotic Arm for Bomb Disposal,” in Advances in Data-driven Computing and Intelligent Systems, J. C. Bansal, S. Saha, C. A. C. Coello, and H. Rathore, Eds. Singapore: Springer Nature, 2025, pp. 351–362.
[21] J. Zhang, X. Yu, S. Ha, J. P. Queralta, and T. Westerlund, “Comparison of Middlewares in Edge-to-Edge and Edge-to-Cloud Communication for Distributed ROS2 Systems,” J. Intell. Robot. Syst., vol. 110, no. 4, p. 162.
[22] R. Carreira, N. Costa, J. Ramos, L. Frazão, and A. Pereira, “A ROS2-Based Gateway for Modular Hardware Usage in Heterogeneous Environments,” Sensors, vol. 24, no. 19, p. 6341, Jan. 2024.
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