Autonomous Robotic Systems in E-commerce Warehousing: A Machine-Learning Optimization Approach

Abstract

E-commerce fulfilment has become a driving force behind recent advances in autonomous robotic systems for warehouses. Modern fulfilment centres combine heterogeneous fleets of autonomous mobile robots (AMRs), robotic manipulators, sensor networks, and digital-twin simulations, coordinated by increasingly sophisticated machine learning (ML) controllers to meet strict service, cost, and safety requirements. This article provides a comprehensive, research-ready treatment of ML-based optimization for autonomous robotic systems in e-commerce warehousing. We synthesize the state of the art across perception, motion planning, fleet coordination, order batching and sequencing, scheduling, energy management, and real-time adaptation with emphasis on reinforcement learning (single-agent and multi-agent), graph neural networks for structured decision making, and explainable AI for safety and trust. We present precise problem formulations, propose a modular hierarchical ML architecture (manager–worker + GNN state encoding + TinyML edge inference), detail learning objectives and loss functions, and outline rigorous experimental protocols using established simulation benchmarks (RWARE / TA-RWARE) and industrial emulators (Dematic, digital twins). Finally, we discuss safety, standards compliance (ISO 10218 / ISO/TS 15066), deployment pathways, evaluation metrics, limitations, and future research directions. Key claims about industrial relevance and technical results are grounded in contemporary literature and industrial examples.