Human Supervisory Control of Robotic Teams: Integrating Cognitive Modeling with Engineering Design

Abstract

This article focuses on the design of systems in which a human operator is responsible for overseeing autonomous agents and providing feedback based on sensor data. In the control systems community, the term human supervisory control (or simply supervisory control) is often used as a shorthand reference for systems with this type of architecture [5]-[7]. In a typical human supervisory control application, the operator does not directly manipulate autonomous agents but rather indirectly interacts with these components via a central data-processing station. As such, system designers have the opportunity to easily incorporate automated functionalities to control how information is presented to the operator and how the input provided by the operator is used by automated systems. The goal of these functionalities is to take advantage of the inherent robustness and adaptability of human operators, while mitigating adverse effects such as unpredictability and performance variability. In some contexts, to meet the goal of single-operator supervision of multiple automated sensor systems, such facilitating mechanisms are not only useful but necessary for practical use [8], [9]. A successful system design must carefully consider the goals of each part of the system as a whole and seamlessly stitch components together using facilitating functionalities.