Development of a Smart Warehouse Framework Using Autonomous Mobile Robots for Warehouse 4.0 Applications
Keywords:
Edge Artificial Intelligence (Edge AI), Smart Factory, Carbon Emission Reduction, Industrial Internet of Things (IIoT), Sustainable ManufacturingAbstract
Warehouses are increasingly adopting automation technologies to improve operational efficiency, inventory accuracy, and process flexibility in response to the growing demands of modern logistics and supply chain systems. Among these technologies, Autonomous Mobile Robots (AMRs) have emerged as a key enabler of smart warehouse operations by providing autonomous transportation, intelligent navigation, and real-time decision-making capabilities. This study aims to develop a Smart Warehouse Framework using Autonomous Mobile Robots that integrates warehouse management, robot navigation, Internet of Things (IoT) devices, and real-time communication systems into a unified architecture. The proposed framework incorporates AMRs, RFID readers, barcode scanners, IoT sensors, Warehouse Management Systems (WMS), fleet management systems, and cloud-based databases to support intelligent warehouse operations. A simulation-based evaluation was conducted using realistic warehouse scenarios to assess the framework's performance based on operational and navigation metrics. The results indicate that the proposed framework significantly improves warehouse efficiency by reducing task completion time from 15 minutes to 7 minutes and decreasing average travel distance from 120 m to 65 m. Furthermore, warehouse throughput increased from 80 to 150 orders per day, while order-picking accuracy improved from 92% to 98%. Navigation performance also demonstrated high effectiveness, achieving mapping accuracy of 97.5%, localization accuracy of 98.7%, and obstacle avoidance success rates exceeding 98%. These findings demonstrate that the proposed Smart Warehouse Framework provides a scalable, intelligent, and efficient solution for Warehouse 4.0 implementation and supports the adoption of autonomous logistics systems in modern industrial environments.
References
Adekunle, B. I., Chukwuma-Eke, E. C., Balogun, E. D., & Ogunsola, K. O. (2021). Machine learning for automation: Developing data-driven solutions for process optimization and accuracy improvement. Machine Learning, 2(1), 1–10.
Agarwal, D., & Bharti, P. S. (2018). A review on comparative analysis of path planning and collision avoidance algorithms. Algorithms, 30(31), 48–59.
Antin, J. F., Stanley, L. M., & Cicora, K. F. (2009). Conventional versus moving-map navigation methods: Efficiency and safety evaluation. Transportation Research Record, 2138(1), 34–41.
Baruffaldi, G., Accorsi, R., Manzini, R., & Ferrari, E. (2020). Warehousing process performance improvement: a tailored framework for 3PL. Business Process Management Journal, 26(6), 1619–1641.
Bulteel, A. J. B., Larson, E. L., & Getahun, H. (2021). Identifying global research gaps to mitigate antimicrobial resistance: a scoping review. American Journal of Infection Control, 49(6), 818–824.
Cognominal, M., Patronymic, K., & Wańkowicz, A. (2021). Evolving field of autonomous mobile robotics: Technological advances and applications. Fusion of Multidisciplinary Research, An International Journal, 2(2), 189–200.
Custodio, L., & Machado, R. (2020). Flexible automated warehouse: a literature review and an innovative framework. The International Journal of Advanced Manufacturing Technology, 106(1), 533–558.
Essuman, D., Boso, N., & Annan, J. (2020). Operational resilience, disruption, and efficiency: Conceptual and empirical analyses. International Journal of Production Economics, 229, 107762.
Fernández-Caramés, T. M., Blanco-Novoa, O., Froiz-Míguez, I., & Fraga-Lamas, P. (2019). Towards an autonomous industry 4.0 warehouse: A UAV and blockchain-based system for inventory and traceability applications in big data-driven supply chain management. Sensors, 19(10), 2394.
Halawa, F., Dauod, H., Lee, I. G., Li, Y., Yoon, S. W., & Chung, S. H. (2020). Introduction of a real time location system to enhance the warehouse safety and operational efficiency. International Journal of Production Economics, 224, 107541.
Inam, R., Fersman, E., Raizer, K., Souza, R., Nascimento, A., & Hata, A. (2018). Safety for Automated Warehouse exhibiting collaborative robots. In Safety and Reliability–Safe Societies in a Changing World (pp. 2021–2028). CRC Press.
Jararweh, Y., Al-Ayyoub, M., Benkhelifa, E., Vouk, M., & Rindos, A. (2016). Software defined cloud: Survey, system and evaluation. Future Generation Computer Systems, 58, 56–74.
Li, Q., Queralta, J. P., Gia, T. N., Zou, Z., & Westerlund, T. (2020). Multi-sensor fusion for navigation and mapping in autonomous vehicles: Accurate localization in urban environments. Unmanned Systems, 8(03), 229–237.
Liu, X., Cao, J., Yang, Y., & Jiang, S. (2018). CPS-based smart warehouse for industry 4.0: A survey of the underlying technologies. Computers, 7(1), 13.
McKinnon, A. (2009). The present and future land requirements of logistical activities. Land Use Policy, 26, S293–S301.
Meyer, J.-A., & Filliat, D. (2003). Map-based navigation in mobile robots:: Ii. a review of map-learning and path-planning strategies. Cognitive Systems Research, 4(4), 283–317.
Moufaddal, M., Benghabrit, A., & Bouhaddou, I. (2020). A cyber-physical warehouse management system architecture in an Industry 4.0 context. International Conference on Artificial Intelligence & Industrial Applications, 125–148.
Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2021). Smart manufacturing and tactile internet based on 5G in industry 4.0: Challenges, applications and new trends. Electronics, 10(24), 3175.
Oduoza, C. K. (2010). Decision support system based on effective knowledge management framework to process customer order enquiry. Decision Support Systems, 406.
Raney, B., & Nagel, K. (2006). An improved framework for large-scale multi-agent simulations of travel behaviour. In Towards Better Performing Transport Networks (pp. 305–347). Routledge.
Ribino, P., Cossentino, M., Lodato, C., & Lopes, S. (2018). Agent-based simulation study for improving logistic warehouse performance. Journal of Simulation, 12(1), 23–41.
Rubio, F., Valero, F., & Llopis-Albert, C. (2019). A review of mobile robots: Concepts, methods, theoretical framework, and applications. International Journal of Advanced Robotic Systems, 16(2), 1729881419839596.
Ruiz, N., Giret, A., Botti, V., & Feria, V. (2011). Agent-supported simulation environment for intelligent manufacturing and warehouse management systems. International Journal of Production Research, 49(5), 1469–1482.
Stączek, P., Pizoń, J., Danilczuk, W., & Gola, A. (2021). A digital twin approach for the improvement of an autonomous mobile robots (AMR’s) operating environment—A case study. Sensors, 21(23), 7830.
Van Geest, M., Tekinerdogan, B., & Catal, C. (2021). Design of a reference architecture for developing smart warehouses in industry 4.0. Computers in Industry, 124, 103343.
Wikner, J., & Tang, O. (2008). A structural framework for closed‐loop supply chains. The International Journal of Logistics Management, 19(3), 344–366.
Yan, Z., Fabresse, L., Laval, J., & Bouraqadi, N. (2015). Metrics for performance benchmarking of multi-robot exploration. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3407–3414.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Kazuki Masato
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
