AI-driven warehouse automation: A comprehensive review of systems
1 Independent Researcher, UK.
2 Independent Researcher, Seattle, Washington, USA.
3 Department of Cybersecurity, University of Nebraska, Omaha USA.
4 Independent Researcher, San Francisco, USA.
Review Article
GSC Advanced Research and Reviews, 2024, 18(02), 272–282.
Article DOI: 10.30574/gscarr.2024.18.2.0063
Publication history:
Received on 03 January 2024; revised on 11 February 2024; accepted on 13 February 2024
Abstract:
This comprehensive review explores the profound impact of artificial intelligence (AI) on warehouse automation, providing an in-depth examination of various AI-driven systems. As industries increasingly embrace automation to enhance efficiency and streamline operations, the integration of AI technologies into warehouse management systems has become pivotal, reshaping the landscape of logistics and supply chain management. AI-driven warehouse automation systems leverage advanced algorithms to optimize various aspects of warehouse operations, from inventory management to order fulfillment. Machine learning algorithms play a key role in demand forecasting, allowing warehouses to predict and adapt to changing customer needs. Computer vision technologies enhance robotic vision, facilitating tasks such as item recognition, pick-and-place operations, and quality control. These advancements significantly contribute to increased accuracy, speed, and cost-effectiveness in warehouse processes. The review provides a detailed examination of the applications of AI in warehouse automation, encompassing autonomous mobile robots (AMRs), robotic arms, and automated guided vehicles (AGVs). AMRs equipped with AI algorithms navigate warehouse environments autonomously, optimizing pick routes and adapting to changes in the warehouse layout. Robotic arms, enhanced by AI, enable precise and adaptable material handling, contributing to the efficiency of tasks like packing and palletizing. AGVs, guided by AI, ensure seamless material transport within warehouses, enhancing overall operational agility. Recent trends in AI-driven warehouse automation systems underscore the dynamic evolution of this field. Edge computing solutions empower these systems to process data locally, reducing latency and enhancing real-time decision-making. Reinforcement learning algorithms enable robotic systems to learn and adapt their behavior based on changing environmental conditions, contributing to continuous improvement and efficiency gains. In conclusion, this review illuminates the pivotal role of AI in transforming warehouse automation systems, revolutionizing the way logistics and supply chain operations are conducted. The collaborative synergy between AI and warehouse automation promises to drive unprecedented advancements in efficiency, accuracy, and adaptability within the evolving landscape of modern warehouses.
Keywords:
Ai-Driven; Warehouse; Automation; Systems; Automation
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
