Autonomous Mobile Robots in Warehouse Automation: A Comprehensive Review of AI, Navigation, and Sensing Technologies Under Industry 4.0
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Warehouse automation is becoming increasingly important as the use of e-commerce, global supply chains and Industry 4.0 continues to increase. In order to address this increasing importance, there is a pressing need for scalable, smart and adaptable warehouse automation systems. As such, the purpose of this review is to provide a comprehensive overview of the current state-of-the-art in using Autonomous Mobile Robots (AMRs) for materials handling. The AMR field has grown rapidly since 2020 and the number of peer reviewed articles related to it has increased significantly. Consequently, in addition to the growing interest in this field, we have synthesized 45 or so papers from six interdependent areas of research including; SLAM/autonomous navigation, deep learning/computer vision for object detection, path planning techniques/algorithms, obstacles avoidance techniques/methodologies, sensor fusion methods and artificial intelligence based inventory management. Through our systematic analysis of these papers we identified both significant advancements in each of the various sub-systems included within the AMR system, i.e., YOLO-based object detection was found to be accurate at approximately 96%, A* path planning accuracy was enhanced to 97% and LiDAR-Camera sensor fusion provided a 100% reliable results when testing for objects greater than 2 cm. However, despite the numerous advancements made in the individual sub-systems, existing solutions remain largely fragmented with limited evidence of unified frameworks being developed that support holistically managing warehouse operations. Furthermore, although some authors report success in deploying their solutions in large scale industrial settings, many others identify key challenges they faced including real-time computing constraints, dynamic obstacle handling issues, scalability limitations and inadequate validation of their solutions using data obtained in real world deployments. Therefore, through our analysis of these papers we were able to identify key research gap areas and propose potential future research direction including developing integrated and edge-computing enabled autonomous robot solutions that align with industry 4.0 standards. Our review also provides a consolidated platform for both researchers and practitioners wishing to develop next generation smart warehouse automation solutions.
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