Using Agent-Based Models to Understand Multi-Operator Supervisory Control

Abstract

As technology advances, many practical applications require human-controlled robots. For such applications, it is useful to determine the optimal number of robots an operator should control to maximize human efficiency given different situations. One way to achieve this is through computer simulations of team performance. In order to factor in various parameters that may affect team performance, an agent-based model will be used. Agent-based modeling is a computational method that enables a researcher to create, analyze, and experiment with models composed of agents that interact within an environment [12]. We construct an agent-based model of humans interacting with robots, and explore how team performance relates to different agent parameters and team organizational structures [21]. Prior work describes interaction between a single operator and multiple robots, while this work includes multi-operator performance and coordination. Model parameters include neglect time, interaction time, operator slack time, level of robot autonomy, etc. Understanding the parameters that influence team performance will be a step towards finding ways to maximize performance in real life human-robot systems.