Improving Search and Rescue Using Contextual Information

Search and rescue (SAR) is a challenging application for autonomous robotics research. The requirements of this kind of application are very demanding and are still far from being met. One of the most compelling requirements is the capability of robots to adapt their functionalities to harsh and heterogeneous environments. In order to meet this requirement, it is common to embed contextual knowledge into robotic modules. We have previously developed a context-based architecture that decouples contextual knowledge, and its use, from typical robotic functionalities. In this paper, we show how it is possible to use this approach to enhance the performance of a robotic system involved in SAR missions. In particular, we provide a case study on exploration and victim detection tasks, specifically tailored to a given SAR mission. Moreover, we extend our contextual knowledge formalism in order to manage complex rules that deal with spatial and temporal aspects that are needed to model mission requirements. The approach has been validated through several experiments that show the effectiveness of the presented methodology for SAR. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2009