Real time modeling of the cognitive load of an Urban Search And Rescue robot operator

Abstract

Urban Search And Rescue (USAR) robots are used to find and save victims in the wake of disasters such as earthquakes or terrorist attacks. The operators of these robots are affected by high cognitive load; this hinders effective robot usage. This paper presents a cognitive task load model for real-time monitoring and, subsequently, balancing of workload on three factors that affect operator performance and mental effort: time occupied, level of information processing, and number of task switches. To test an implementation of the model, five participants drove a shape-shifting USAR robot, accumulating over 16 hours of driving time in the course of 485 USAR missions with varying objectives and difficulty. An accuracy of 69% was obtained for discrimination between low and high cognitive load; higher accuracy was measured for discrimination between extreme cognitive loads. This demonstrates that such a model can contribute, in a non-invasive manner, to estimating an operator's cognitive state. Several ways to further improve accuracy are discussed, based on additional experimental results.

Dynamic Task Allocation for Human-robot Teams

“…This involved specifying tasks, possible levels of autonomy of these tasks and task properties. Furthermore, we used an existing model that calculates CTL specifically for the urban search and rescue domain (Colin et al, 2014). Experimental Method.…”

Enabling and Assessing Trust when Cooperating with Robots in Disaster Response (EASIER)

“…Using a non-invasive multilevel approach including subjective data, psychophysiological measurements, performance variables, reconstructional video, and observation techniques, we will assess trust and cognitive load without interfering with the operator during the current mission [25].…”