An Algorithmic Approach to Generate After-disaster Test Fields for Search and Rescue Agents.

Autonomous navigation in unknown cluttered environments is one of the main challenges for search and rescue robots inside collapsed buildings. Being able to compare different search strategies in various search fields is crucial to attain fast victim localization. Thus we discuss an algorithmic development and proliferation of realistic after--disaster test fields for search and rescue simulated robots. In this paper we characterized our developed search environments by their fractal dimensions. This index has shown to be a discriminative index for narrow pathways inside confined and cluttered spaces in our simulation test fields. In this approach a simulation of challenging parts of NIST red course is constructed and a benchmark for search strategies has been evaluated. [ABSTRACT FROM AUTHOR]