AI and Digital Twin Integration in Autonomous Robot Orchestration Solution (AROS)

We present a groundbreaking advancement in manufacturing automation through the development of the Autonomous Robot Orchestration Solution (AROS), a sophisticated system that revolutionizes the management of large-scale robotic operations. At its core, AROS employs an intelligent framework that continuously monitors and analyzes both individual robot states and their operational environment, enabling unprecedented levels of autonomous collaboration. Our research specifically focuses on the implementation of AROS in semiconductor fabrication facilities, where it orchestrates complex Overhead Hoist Transport (OHT) vehicle operations. The system integrates two powerful technological pillars: advanced reinforcement learning algorithms for decision-making and sophisticated deep auto-encoder models for system monitoring. A key innovation lies in our implementation of Digital Twin (DT) technology, which creates a real-time virtual replica of the physical system, enabling sophisticated simulation-based decision optimization. Through extensive testing in operational semiconductor fabrication facilities, we demonstrate quantifiable improvements in both delivery system efficiency and operational capacity. The system's ability to autonomously detect and respond to anomalies significantly reduces the need for human intervention, marking a significant step toward fully autonomous manufacturing operations. These achievements are validated through comprehensive performance data from large-scale semiconductor manufacturing environments, establishing AROS as a pioneering solution in advanced factory automation.