Human machine teaming in mobile miniaturized aviation logistics systems in safety-critical settings

Abstract

Advances in artificial intelligence (AI) and autonomous systems allow machines to increasingly function as teammates with their human partners, rather than as tools used by the humans. As teammates, machines share team goals, act to achieve those goals, and coordinate those actions with other teammates. Autonomous systems and AI herald new capabilities in human machine teams (HMTs) in which plans, activities, communications, and decisions are guided by intelligent technology, advanced computation and sensor systems, and a combination of human and automated decision makers. Uncrewed aerial systems (UAS) in HMTs, or mobile miniaturized aviation logistics systems (MMALs), have stretched the reach and impact of logistics systems, transporting critical supplies, equipment, parts and material, often to remote locations with minimal infrastructure. Although researchers have often considered MMALs’ impacts in safety-critical systems, there is increasing interest in understanding the role of AI and automation in HMT, and in empirical research and theoretical models that directly address the impacts of HMTs that incorporate MMALs. To address these needs, we present a framework and research project evaluating autonomous and AI-enabled UAS in human machine teams. We describe use of the framework in a case study of Arctic emergency response and logistics operations and conclude with needs for future research.