From Automation to Autonomy: A Digital Twin Framework for transparent Agent and Human Collaboration in Industrial Multi-Agent Systems

Abstract

With the advancement of digitization in the era of Industry 4.0 (I4.0), highly automated, semi-autonomous, and fully autonomous systems are emerging. Within this context, multi-agent systems (MAS) offer a promising approach for automating tasks and processes based on autonomous agents that work together in an overall system to increase the degree of system autonomy stepwise in a modular and flexible way. A critical research challenge is determining how these agents can collaboratively engage with both other agents and human operators to facilitate the gradual transition from automated to fully autonomous industrial systems. To close transparency and connectivity gaps, this study contributes with a framework for the collaboration of agents and humans in increasingly autonomous MAS based on a Digital Twin (DT). The framework specifies a standard-based data model for MAS representation and proposes to introduce a DT infrastructure as a service layer for system coordination, supervision, and interaction. To demonstrate the feasibility and assess the quality of the framework, it is implemented and evaluated in a case study in a real-world industrial scenario. As a result of the study, we infer that the DT framework offers significant benefits in facilitating transparent and seamless cooperation between agents and humans within increasingly autonomous industrial MAS.