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In the context of collaborative robotics, distributed situation awareness isessential for supporting collective intelligence in teams of robots and humanagents where it can be used for both individual and collective decisionsupport. This is particularly important in applications pertaining to emergencyrescue and crisis management. During operational missions, data and knowledgeis gathered incrementally and in different ways by heterogeneous robots andhumans. We describe this as the creation of \emph{Hastily Formed KnowledgeNetworks} (HFKNs). The focus of this paper is the specification and prototypingof a general distributed system architecture that supports the creation ofHFKNs by teams of robots and humans. The information collected ranges fromlow-level sensor data to high-level semantic knowledge, the latter representedin part as RDF Graphs. The framework includes a synchronization protocol andassociated algorithms that allow for the automatic distribution and sharing ofdata and knowledge between agents. This is done through the distributedsynchronization of RDF Graphs shared between agents. High-level semanticqueries specified in SPARQL can be used by robots and humans alike to acquireboth knowledge and data content from team members. The system is empiricallyvalidated and complexity results of the proposed algorithms are provided.Additionally, a field robotics case study is described, where a 3D mappingmission has been executed using several UAVs in a collaborative emergencyrescue scenario while using the full HFKN Framework.

Hastily Formed Knowledge Networks and Distributed Situation Awareness for Collaborative Robotics