A Systematic Review of AI-Driven Autonomous Mobile Robots (AMR) in Scalable Micro-Fulfillment Centers

This study presents a systematic review of artificial intelligence (AI)–driven Autonomous Mobile Robots (AMRs) within scalable micro-fulfillment centers (MFCs), with a focus on their architectural configurations, operational performance, and integration within modern supply chain ecosystems. The rapid growth of e-commerce and demand for ultra-fast last-mile delivery have accelerated the adoption of compact, urban fulfillment models, where AMRs play a central role in enabling high-density storage, dynamic inventory handling, and real-time order processing. This review synthesizes recent scholarly and industrial contributions to examine how AI techniques—such as reinforcement learning, computer vision, swarm intelligence, and predictive analytics—enhance navigation, task allocation, fleet coordination, and human–robot collaboration in constrained warehouse environments. The paper evaluates system scalability by analyzing throughput optimization, latency reduction, space utilization efficiency, and resilience under variable demand conditions. Furthermore, it investigates interoperability challenges associated with warehouse management systems (WMS), Internet of Things (IoT) infrastructures, and cloud-based orchestration platforms. A comparative assessment of leading AMR deployment frameworks is conducted to identify performance trade-offs, cost implications, and implementation barriers, including energy constraints, safety compliance, and algorithmic reliability. The review also highlights emerging trends such as digital twin integration, edge AI processing, and decentralized decision-making architectures that support adaptive and self-organizing fulfillment systems. By consolidating fragmented research across robotics, logistics, and intelligent systems, this study provides a comprehensive analytical foundation for both academic inquiry and industrial application. The findings aim to guide future research directions toward robust, scalable, and cost-efficient AMR-enabled micro-fulfillment solutions capable of meeting the evolving demands of omnichannel retail and urban logistics environments.