Functional Mapping: Spatial Inferencing to Aid Human-Robot Rescue Efforts in Unstructured Disaster Environments

In this paper we examine the case of a mobile robot that is part of a human-robot urban search and rescue (USAR) team. During USAR scenarios, we would like the robot to have a geometrical-functional understanding of space, using which it can infer where to perform planned tasks in a manner that mimics human behavior. We assess the situation awareness of rescue workers during a simulated USAR scenario and use this as an empirical basis to build our robot’s spatial model. Based upon this spatial model, we present “functional mapping” as an approach to identify regions in the USAR environment where planned tasks are likely to be optimally achievable. The system is deployed and evaluated in a simulated rescue scenario.