DFKI-LT - Functional Mapping: Spatial Inferencing to aid Human-Robot Rescue Efforts in Unstructured Disaster Environments

Shanker Keshavdas, Hendrik Zender, Geert-Jan Kruijff, M. Liu, F. Colas
Functional Mapping: Spatial Inferencing to aid Human-Robot Rescue Efforts in Unstructured Disaster Environments
3 Proceedings of the 2012 AAAI Spring Symposium on Designing Intelligent Robots, Stanford, CA, USA, AAAI Press, Stanford University, 2012
 
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 understand- ing of space, using which it can infer where to perform planned tasks in a manner that mimics human behav- ior. We assess the situation awareness of rescue work- ers 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 map- ping"; as an approach to identify regions in the USAR environment where planned tasks are likely to be opti- mally achievable. The system is deployed and evaluated in a simulated rescue scenario.
 
Files: BibTeX, FunctionalMappingAAAI2012.pdf, 4318