Modeling multiple human operators in the supervisory control of heterogeneous unmanned vehicles

In the near future, large, complex, time-critical missions, such as disaster relief, will likely require multiple unmanned vehicle (UV) operators, each controlling multiple vehicles, to combine their efforts as a team. However, is the effort of the team equal to the sum of the operator's individual efforts? To help answer this question, a discrete event simulation model of a team of human operators, each performing supervisory control of multiple unmanned vehicles, was developed. The model consists of exogenous and internal inputs, operator servers, and a task allocation mechanism that disseminates events to the operators according to the team structure and state of the system. To generate the data necessary for model building and validation, an experimental test-bed was developed where teams of three operators controlled multiple UVs by using a simulated ground control station software interface. The team structure and interarrival time of exogenous events were both varied in a 2×2 full factorial design to gather data on the impact on system performance that occurs as a result of changing both exogenous and internal inputs. From the data that was gathered, the model was able to replicate the empirical results within a 95% confidence interval for all four treatments, however more empirical data is needed to build confidence in the model's predictive ability.United States. Office of Naval ResearchUnited States. Air Force Office of Scientific Researc

A taxonomy of perturbations: Determining the ways that systems lose value

Disturbances and disruptions, both internal and external to the system, are a major concern for system architects who are responsible for ensuring that their systems maintain value robustness no matter what occurs. These perturbations can have multiple causes and can affect a system in multiple ways. This paper presents a taxonomy of disturbances and disruptions to assist system architects and researchers in identifying the ways in which systems can fail to deliver value. By doing so, this taxonomy falls into a larger research effort to develop survivability design principles that will help system architects design systems that prevent, mitigate and recover from disturbances

Investigating alternative concepts of operations for a maritime security system of systems

For complex systems of systems, such as those required to perform maritime security, system architects have numerous choices they may select from, both in the components and in the way the system operates. Component choices, such as the length of a wing or the number of ground control stations, are often considered in tradespace studies, but this paper highlights the operational choices that are often overlooked. Using an unmanned vehicle system of systems as an example, the importance of considering operational choices as well as the highly interdependent nature of operational and component choices is demonstrated, further strengthening the case for careful consideration of operational variables early in the concept phase of the design process. Finally, a high-level methodology for generating and evaluating operational choices is introduced

Pliability and Viable Systems: Maintaining Value Under Changing Conditions

As systems become more complex and have longer lifespans, they will likely encounter contextual variation or be themselves subject to change. Systems need to not only be feasible but viable as well. That is, they need to be able to continue to provide value in spite of any potential exogenous or endogenous changes. Viability has been defined for other domains, but it has not been defined for engineered systems. This paper defines what it means for an engineered system to be viable and shows that it is related to, but different from, other existing “-ilities” such as survivability and reliability. This paper also addresses the need to ensure that endogenous changes do not inadvertently cause unintended interactions that harm the system overall. A new -ility, i.e., pliability, is introduced, which specifies the limits on how a system can change, without “breaking” or violating an architecture that was intended and validated. Like changeability, pliability increases robustness by allowing systems to voluntarily change in response to dynamic contexts and increases survivability by increasing the likelihood that unintentional changes are still within the set of allowable architecture-defined instances. It also distinguishes allowable changes from those that would require additional validation, reducing the effort required to get those changes approved by a diverse set of stakeholders