World model for testing Urban Search and Rescue (USAR) robots using Petri Nets

This paper describes a model-based test generation approach for testing Urban Search and Rescue (USAR) robots interacting with their environment (i.e., world). Unlike other approaches that assume a static world with attributes and values, we present and test a dynamic world. We use Petri Nets to illustrate a world model that describes behaviors of environmental entities (i.e., actors). The Abstract World Behavioral Test Cases (AWBTCs) are generated by covering the active world model using graph coverage criteria. We also select test-data by input-space partitioning to transform the generated AWBTCs into executable test cases. Reachability of the active world model and efficiency of coverage criteria are also discussed.