Predicting Performance and Situation Awareness of Robot Operators in Complex Situations by Unit Task Tests

Abstract

Human-in-the-loop field tests of human-robot operations in high-demand situations provide serious constraints with respect to costs and control. A set of relatively simple unit tasks might be used to do part of the testing and to establish a benchmark for human-robot performance and situation awareness. For an urban search and rescue (tunnel accident) scenario, we selected and refined the corresponding unit tasks from a first version of a test battery. First responders (fire-men) conducted these unit tasks with a state-of-the-art robot and, subsequently, had to perform the tunnel accident mission in a realistic field setting with the same robot. The Detect objects unit task proved to partially predict operator’s performance and the operator’s collision awareness in the scenario. Individual differences, particularly age, had a major effect on performance and collision awareness in both the unit tasks and scenario.