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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.
Keywords: Mixed fleet; situational awareness; data; fleet control; autonomous systems; AGV; AMR.
1 Introduction
This study focuses on industrial mixed fleets, comprising mobile machines that operate indoors within factories and warehouses. These fleets are primarily used for material handling tasks and include Automated Guided Vehicles (AGVs), Autonomous Mobile Robots (AMRs), forklift trucks, and overhead cranes. However, a significant challenge is that factories often lack Situational Awareness (SA) regarding their mixed fleets e.g., where the machines are located, what tasks they are performing, and what their next actions will be. As a result, the material flow within the factory cannot be optimized in a holistic manner, leading to decreased efficiency, reduced profitability, and safety risks. While various digital technologies are utilized in internal logistics, solutions that provide SA at the mixed fleet level remain limited. To address this, there is a clear need for innovative solutions, a deeper understanding of the required technical solutions, and the role of data in enabling effective mixed fleet SA.
Digital solutions for autonomous fleets are plentiful enabling functions like positioning, autonomous navigation, fleet control, and collision avoidance. These solutions rely heavily on data. However, for manually operated machines such as forklift trucks and overhead cranes, fleet-level control and optimization have been less prevalent. To achieve holistic mixed fleet-level SA, a new technical concept is required. A single solution cannot address the central challenges; instead, a combination...