Decentralizing Cloud Robot Services Through Edge Computing

The paper extends previous developments of cloud robot services for intelligent manufacturing with new data streaming and machine learning techniques that are used to dynamically reschedule resources and predict future behaviour on the shop floor. Data is obtained in real-time with edge computing solutions that ease the computing effort in the cloud, by moving intelligence to the edge of the manufacturing execution system. Thus, machine learning algorithms can be run in real-time context with re-training on new data; the insights become predictions, enabling real-time decisions for: operations scheduling, robot allocation and real status-based maintenance. Experiments are described.