Page 24 - Innovations in Intelligent Machines

12 M.L. Cummings et al.
effectiveness of a single operator controlling multiple UAVs. However, most
studies have investigated this issue from an experimental standpoint, and thus
they generally lack any predictive capability beyond the limited conditions and
specific interfaces used in the experiments.
In order to address this gap, this chapter first analyzes past literature
to examine potential trends in supervisory control research of multiple unin-
habited aerial vehicles (MUAVs). Specific attention is paid to automation
strategies for operator decision-making and action. After the experimental
research is reviewed for important “lessons learned”, an extension of a ground
unmanned vehicle operator capacity model will be presented that provides
predictive capability, first at a very general level and then at a more detailed
cost-benefit analysis level. While experimental models are important to under-
stand what variables are important to consider in MUAV control from the
human perspective, the use of predictive models that leverage the results from
these experiments is critical for understanding what system architectures are
possible in the future. Moreover, as will be illustrated, predictive models that
clearly link operator capacity to system effectiveness in terms of a cost-benefit
analysis will also demonstrate where design changes could be made to have
the greatest impact.

2 Previous Experimental Multiple UAV studies

Operating a US Army Hunter or Shadow UAV currently requires the full
attention of two operators: an AVO (Aerial Vehicle Operator) and a MPO
(Mission Payload Operator), who are in charge respectively of the navigation
of the UAV, and of its strategic control (searching for targets and monitoring
the system). Current research is aimed at finding ways to reduce workload and
merge both operator functions, so that only one operator is required to manage
one UAV. One solution investigated by Dixon et al. consisted of adding audi-
tory and automation aids to support the potential single operator [2]. Exper-
imentally, they showed that a single operator could theoretically fully control
a single UAV (both navigation and payload) if appropriate automated offload-
ing strategies were provided. For example, aural alerts improved performance
in the tasks related to the alerts, but not others. Conversely, it was also shown
that adding automation benefited both tasks related to automation (e.g. navi-
gation, path planning, or target recognition) as well as non-related tasks.
However, their results demonstrate that human operators may be limited in
their ability to control multiple vehicles which need navigation and payload
assistance, especially with unreliable automation. These results are concordant
with the single-channel theory, stating that humans alone cannot perform high
speed tasks concurrently [3, 4]. However, Dixon et al. propose that reliable
automation could allow a single operator to fully control two UAVs.
Reliability and the related component of trust is a significant issue in the
control of multiple uninhabited vehicles. In another experiment, Ruff et al. [5]