Vehicle Crew-Agent Teaming

Science

 

crew_agent_teamingThrough decades of research, development, testing, and evaluation, as well as, for some systems (e.g., IVIS, FBCB2), use in-theater; the Army has created advanced Warfighter-Machine Interface (WMI) technologies to support mobile operations across a complex set of hazardous environments. Science and technology efforts by members of the CAST involve basic and applied research focused on advancing the state of the art in such WMI technologies for even more complex, multi-domain operations. A primary barrier for future WMI technologies comes from challenges that arise when technological complexity and informational load outstrips the ability of the Soldier to effectively and efficiently interact (for discussion see McDowell et al 2007). This demonstration attempts to overcome this barrier through its focus on individualized WMI technologies that adapt to the dynamic state of the operator and optimize mission performance at the team level (see DeCostanza et al 2018). The overarching goal is to maximize the emergent performance of a vehicle crew as it works with a diverse system of intelligent software (virtual agents) and hardware (physical agents) including robotized ground and aerial vehicles, remote sensors, and weapons. Examples of future WMI capabilities include:

 

  • Enhanced Secure Mobility: Military vehicle crews, beyond typical civilian concerns, have the added expectations of maintaining awareness sufficient to ensure safety of their vehicle, platoon, and any robotic or other assets within their purview (McDowell, Nunez, Hutchins, Metcalfe, 2008).
  • Adaptation to Operational Variability: From various operational tempos to multiple, temporally overlapping and, in some cases, competing tasks, the modern Soldier crews face a vast range of variability in military operational environments.
  • Distributed Teaming: Future missions will require the crew to team with other non-co-located individuals as well as physical and virtual agents. The team will have to effectively overcome differing member capabilities, knowledge, perspectives and frames of reference on the problem environment as well as the dynamics of potentially imperfect communications streams.


Take a look at our INFORMS Laboratory for some examples of how we carving the transition pathway from basic science to demonstration.