For the future vision of a single human operator controlling multiple heterogeneous unmanned vehicles (UVs), operators will conduct high level goal-based control, in contrast to more low-level vehicle-based control. To achieve this type of control, operators will be assisted by automated planners, which are generally faster and more accurate than humans at path planning and task allocation in a multivariate, dynamic, time-pressured environment. Human management of the automated planner is crucial, however, as automated planners do not always generate accurate solutions, especially in the presence of uncertain or incomplete information.
Research areas include:
This research investigates the effects of prolonged low workload on operator performance in the context of controlling a network of unmanned vehicles (UxVs) with the assistance of an autonomous planner. In addition, this research focuses on assessing the physical, social, and cognitive coping mechanisms that operators rely upon during prolonged low workload missions. Using experimental data gathered in long duration, low task load multiple unmanned vehicle environments, we are developing a model that accounts for boredom and spikes in workload in order to predict operator performance and identify possible opportunities for technological interventions. This research will help in the design of smart decision support tools that can increase vigilance and performance of operators in supervisory control domains with low workload. This research is sponsored by the Office of Naval Research.
This research is funded by Aurora andthe Office of Naval Research
In current operations in Iraq and elsewhere, the supervisory control of unmanned vehicles (UVs) involves multiple operators controlling single vehicles. As the demand for UVs increases with little or no gain in the number of trained operators available to control them, current research efforts are studying how a single operator can effectively control multiple, heterogeneous unmanned vehicles. However, there is a limit as to the number of UVs a single operator can effectively control and at some point, teams of operators, each of whom control teams of UVs, will eventually need to share a common area of interest and work together to obtain their goals. The question becomes, how will working in teams affect the operator's performance? The goal of the research is to develop a model of how teams of operators, each of whom are responsible for controlling multiple, heterogeneous unmanned vehicles, coordinate and collaborate, under time-critical, life-critical scenarios. Previous research at MIT has successfully modeled the supervisory control of multiple, heterogeneous unmanned vehicles by a single human operator based on queuing theory and implemented with a discrete event simulation (DES). This research will expand the existing single operator queuing model to describe multiple human operators collaborating and coordinating in a team environment, as they each control multiple, heterogeneous unmanned vehicles. From this model, performance metrics will be generated that will explain how teamwork affects the supervisory control of multiple, heterogeneous unmanned vehicles and recommendations will be made for team allocation and / or interface design.
Nehme, C.E., (2009), Modeling Human Supervisory Control in Heterogeneous Unmanned Vehicle System, Ph. D. Thesis, MIT Dept. of Aeronautics and Astronautics, Cambridge, MA.
Nehme, C. E., Mekdeci, B., Crandall, J. W., Cummings, M. L. The Impact of Heterogeneity on Operator Performance in Future Unmanned Vehicle Systems, The International Command and Control Journal, (2008), Vol. 2(2).