A Study of Autonomous Mobile Robot
DOI:
https://doi.org/10.59247/jfsc.v3i3.337Keywords:
Autonomous Mobile Robot, ROS, SLAM, Navigation, Industry 4.0, Smart FactoryAbstract
With the rapid development of Industry 4.0, automation in production and operations is an important factor to optimize the production process. One of the most important technologies is autonomous mobile robots (AMR). The use of AMR in factories, workshops, and warehouses is becoming more and more popular. Flexibility in production helps companies better meet customer needs and increase productivity without incurring costs and wasting resources. In this study, we present the design and fabrication of an AMR vehicle system for factory environments. The system is developed on Ubuntu and the robot operating system (ROS). Innovation of AMR in our project is to emphasize the change when integrating a ROS-based distributed architecture, in which a separate embedded controller (Raspberry Pi 4 embedded computer) handles real-time control, and a localization and mapping (SLAM) task processor, along with the Navigation Stack package, is used for remote mapping and navigation. Industrial floors are often full of obstacles, so a powerful LiDAR filter and a robust SLAM pipeline are needed to improve mapping accuracy and collision avoidance. This is certainly a promising solution, while current research on autonomous mobile robots usually focuses on navigation and does not incorporate mechanical lifting mechanisms for material handling, we also improve the communication protocol to enhance system performance. Experiments show that the system can automatically position, meet scalability, improve real-time performance, and enable robots to lift/lower objects within the same ROS system, which is suitable for real-world warehouse and factory applications.
References
R. Keith and H. M. La, “Review of Autonomous Mobile Robots for the Warehouse Environment,” arXiv preprint, 2024, http://arxiv.org/abs/2406.08333.
N. Terron, “AMR in intralogistics: influence of the operating environment and comparison with traditional transport vehicle,” Dept. of Industrial Engineering, University of Padua,Padua, Italy, 2023; https://hdl.handle.net/20.500.12608/80343.
A. N. Dhananji and T. Sharmilan, “Autonomous Mobile Robot Navigation and Obstacle Avoidance: A Comprehensive Review,” European Modern Studies Journal, vol. 7, no. 6, pp. 260–267, 2024, https://doi.org/10.59573/emsj.7(6).2023.25.
B. Liu, X. Xiao, and P. Stone, “A Lifelong Learning Approach to Mobile Robot Navigation,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1090–1096, 2021, https://doi.org/10.1109/LRA.2021.3056373.
H. Sier, Q. Li, X. Yu, J. Peña Queralta, Z. Zou, and T. Westerlund, “A Benchmark for Multi-Modal LiDAR SLAM with Ground Truth in GNSS-Denied Environments,” Remote Sensing, vol. 15, no. 13, 2023, https://doi.org/10.3390/rs15133314.
C. Campos, R. Elvira, J. J. G. Rodriguez, J. M. M. Montiel, and J. D. Tardos, “ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial, and Multimap SLAM,” IEEE Transactions on Robotics, vol. 37, no. 6, pp. 1874–1890, 2021, https://doi.org/10.1109/TRO.2021.3075644.
T. Lackner, J. Hermann, C. Kuhn, and D. Palm, “Review of autonomous mobile robots in intralogistics: state-of-the-art, limitations and research gaps,” Procedia CIRP, vol. 130, pp. 930–935, 2024, https://doi.org/10.1016/j.procir.2024.10.187.
D. Van Nam, P. T. Danh, C. H. Park, and G. W. Kim, “Fusion consistency for industrial robot navigation: An integrated SLAM framework with multiple 2D LiDAR-visual-inertial sensors,” Computers and Electrical Engineering, vol. 120, 2024, https://doi.org/10.1016/j.compeleceng.2024.109607.
H. Peng, Z. Zhao, and L. Wang, “A Review of Dynamic Object Filtering in SLAM Based on 3D LiDAR,” Sensors, vol. 24, no. 2, 2024, https://doi.org/10.3390/s24020645.
Z. Wei, S. Wang, K. Chen, and F. Wang, “ROS-Based Navigation and Obstacle Avoidance: A Study of Architectures, Methods, and Trends,” Sensors, vol. 25, no. 14, p. 4306, 2025, https://doi.org/10.3390/s25144306.
M. Quigley et al., “ROS: an open-source Robot Operating System,” in Proceedings of ICRA Workshop on Open Source Software, 2009, http://ai.stanford.edu/~mquigley/papers/icra2009-ros.pdf.
P. Guo, H. Shi, S. Wang, L. Tang, and Z. Wang, “An ROS Architecture for Autonomous Mobile Robots with UCAR Platforms in Smart Restaurants,” Machines, vol. 10, no. 10, 2022, https://doi.org/10.3390/machines10100844.
J. Palacín, R. Bitriá, E. Rubies, and E. Clotet, “A Procedure for Taking a Remotely Controlled Elevator with an Autonomous Mobile Robot Based on 2D LIDAR,” Sensors, vol. 23, no. 13, 2023, https://doi.org/10.3390/s23136089.
Q.-T. Do et al., “Modeling and Optimal Control for Two-Wheeled Self-Balancing Robot,” Journal of Fuzzy Systems and Control, vol. 2, no. 1, pp. 22–28, 2024, https://doi.org/10.59247/jfsc.v2i1.162.
M. Alfiyan and R. D. Puriyanto, “Mecanum 4 Omni Wheel Directional Robot Design System Using PID Method,” Journal of Fuzzy Systems and Control, vol. 1, no. 1, pp. 6–13, 2023, https://doi.org/10.59247/jfsc.v1i1.27.
C. Cadena et al., “Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age,” IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1309–1332, 2016, https://doi.org/10.1109/TRO.2016.2624754.
E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik, vol. 1, no. 1, pp. 269–271, 1959, https://doi.org/10.1007/BF01386390.
P. E. Hart, N. J. Nilsson, and B. Raphael, “A Formal Basis for the Heuristic Determination of Minimum Cost Paths,” IEEE Transactions on Systems Science and Cybernetics, vol. 4, no. 2, pp. 100–107, Jul. 1968, https://doi.org/10.1109/TSSC.1968.300136.
M. H. Kotb and R. Ming, “Comparing SMOTE Family Techniques in Predicting Insurance Premium Defaulting using Machine Learning Models,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 9, pp. 562–570, 2021, https://doi.org/10.14569/IJACSA.2021.0120970.
C. S. Chen, C. J. Lin, C. C. Lai, and S. Y. Lin, “Velocity Estimation and Cost Map Generation for Dynamic Obstacle Avoidance of ROS Based AMR,” Machines, vol. 10, no. 7, 2022, https://doi.org/10.3390/machines10070501.
T. Peng, D. Zhang, D. L. N. Hettiarachchi, and J. Loomis, “An evaluation of embedded GPU systems for visual SLAM algorithms,” in IS and T International Symposium on Electronic Imaging Science and Technology, 2020, pp. 325–326, https://doi.org/10.2352/ISSN.2470-1173.2020.6.IRIACV-325.
A. Noguera Cundar, R. Fotouhi, Z. Ochitwa, and H. Obaid, “Quantifying the Effects of Network Latency for a Teleoperated Robot,” Sensors (Basel, Switzerland), vol. 23, no. 20, 2023, https://doi.org/10.3390/s23208438.
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Copyright (c) 2025 Nam-Long Doan, Viet-Thinh Dao, Hoang-Ha Nguyen, Pham-Kien-Quoc Luong, Tran-Thanh-Thuy Nguyen, Quang-Thuan Ho, Thai-Duong Nguyen, Khanh-Dang Nguyen, Binh-Hau Nguyen Nguyen, Anh-Duc Nguyen, Phuong-Quang Nguyen, Viet-Khoi Vo, Van-Dong-Hai Nguyen
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