Industrial Mixed Fleets: An Empirical Study on Central Situational Awareness Activities

Abstract

Nowadays, an increasing number of mixed fleets are responsible for material handling in factories. In our study, mixed fleets consist of Automated Guided Vehicles (AGVs), Autonomous Mobile Robots (AMRs), forklift trucks, and overhead cranes. Autonomous, manned and unmanned mobile machines operate in the same factory with humans. However, there is a lack of holistic, mixed fleet level coordination and optimization, which jeopardizes material handling efficiency and safety. The case study proposes that enhanced Situational Awareness (SA) is a central prerequisite for mixed fleet level optimization and systemic safety. A proposed Mixed Fleet SA Framework guided and framed the study, consisting of the elements of perception, comprehension, projection, decision-making, and execution. In particular, central SA activities, which enhance mixed fleet level optimization and safety were identified in three industrial cases. Tracking and localization of people and all mixed fleet machines, not merely autonomous ones, provide bases for shared SA and fleet level optimization. Dynamic mixed fleet routing helps avoiding deadlocks, delays, and safety hazards. Warnings of approaching collisions and automatic slow-downs of machines can be implemented to ensure mixed level systemic safety. This study contributes to the research domain of autonomous system SA. It conceptualizes mixed fleet SA and expands the perspective by addressing both autonomous and manual traffic in creating new mixed fleet management solutions.