Model-based Trustworthiness Evaluation of Autonomous Cyber-Physical Production Systems: A Systematic Mapping Study

Abstract

The fourth industrial revolution, i.e., Industry 4.0, is associated with Cyber-Physical Systems (CPS), which are entities integrating hardware (e.g., smart sensors and actuators connected through the Industrial Internet of Things) together with control and analytics software used to drive and support decisions at several levels. The latest developments in Artificial Intelligence (AI) and Machine Learning (ML) have enabled increased autonomy and closer human-robot cooperation in the production and manufacturing industry, thus leading to Autonomous Cyber-Physical Production Systems (ACPPS) and paving the way to the fifth industrial revolution (i.e., Industry 5.0). ACPPS are increasingly critical due to the possible consequences of their malfunctions on human co-workers, and therefore, evaluating their trustworthiness is essential. This article reviews research trends, relevant attributes, modeling languages, and tools related to the model-based trustworthiness evaluation of ACPPS. As in many other engineering disciplines and domains, model-based approaches, including stochastic and formal analysis tools, are essential to master the increasing complexity and criticality of ACPPS and to prove relevant attributes such as system safety in the presence of intelligent behaviors and uncertainties.