Technical framework and data sharing for mixed fleet situational awareness

Abstract

This study introduces an innovative technical framework for mixed fleet situational awareness (SA) aimed at optimizing material flow in factories. The framework integrates a range of digital solutions, including positioning technologies, a universal fleet control system, diverse user interfaces, advanced machine perception, and tools for generating predictive insights - such as potential traffic congestion and hazardous zones. The findings support digital transformation of factory logistics and offers novel insights into SA in mixed fleets, extending beyond prior research focused predominantly on autonomous or multi-robot systems. It also proposes data-sharing models tailored to different user needs: an operator view for factory-wide SA to support e.g. traffic planning, and a first-person view for factory workers, offering real-time information on close-by machine status, warnings, and intentions. Innovating new solutions for mixed fleet situational awareness will be a key driver of digital transformation in factory logistics.