Methodology of Training and Support for Urban Search and Rescue With Robots

A primordial task of the fire-fighting and rescue services in the event of a large crisis is the search for human survivors on the incident site. This task, being complex and dangerous, often leads to loss of lives. Unmanned search and rescue devices can provide a valuable tool for saving human lives and speeding up the search and rescue operations. Urban Search and Rescue (USAR) community agrees with the fact that the operator skill is the main factor for successfully using unmanned robotic platforms. The key training concept is "train as you fight" mentality. Intervention troops focalize on "real training", as a crisis is difficult to simulate. For this reason, in this paper a methodology of training and support for USAR with unmanned vehicles is proposed. The methodology integrates the Qualitative Spatio-Temporal Representation and Reasoning (QSTRR) framework with USAR tools to decrease the cognitive load on human operators working with sophisticated robotic platforms. Tools for simplifying and improving virtual training environment generation from life data are shown