An Agent-Based Simulation Model of Human-Robot Team Performance in Military Environments

Abstract

Prior to deploying human-robot teams on military missions, system designers need to understand how design decisions affect team performance. This paper describes a multiagent simulation model that captures both team coordination and human-robot interaction. The purpose of the model is to evaluate proposed team designs in uncertain Military Operations in Urban Terrain (MOUT) scenarios and determine which design factors are most critical to team performance. The simulation model is intended to be a tool in the systems engineering iterations of proposing designs, testing them, and then evaluating them during the conceptual design phase. To illustrate the model’s usefulness for this purpose, a fractional factorial design of experiments is conducted to evaluate team design factors and the two-factor interaction between controllable factors and noise factors that described the environment and robot reliability. The experimental results suggest that (1) larger teams have more robust performance over the noise factors, (2) robot reliability is critical to the formation of human-robot teams, and (3) high centralization of decision-making authority created communication bottlenecks at the commander in large teams. This work contributes to the agent-based modeling of teams, and to understanding how the U.S. Army can attain its goal of greater utilization of robots in future military operations.