  In the context of collaborative robotics, distributed situation awareness is
essential for supporting collective intelligence in teams of robots and human
agents where it can be used for both individual and collective decision
support. This is particularly important in applications pertaining to emergency
rescue and crisis management. During operational missions, data and knowledge
is gathered incrementally and in different ways by heterogeneous robots and
humans. We describe this as the creation of \emph{Hastily Formed Knowledge
Networks} (HFKNs). The focus of this paper is the specification and prototyping
of a general distributed system architecture that supports the creation of
HFKNs by teams of robots and humans. The information collected ranges from
low-level sensor data to high-level semantic knowledge, the latter represented
in part as RDF Graphs. The framework includes a synchronization protocol and
associated algorithms that allow for the automatic distribution and sharing of
data and knowledge between agents. This is done through the distributed
synchronization of RDF Graphs shared between agents. High-level semantic
queries specified in SPARQL can be used by robots and humans alike to acquire
both knowledge and data content from team members. The system is empirically
validated and complexity results of the proposed algorithms are provided.
Additionally, a field robotics case study is described, where a 3D mapping
mission has been executed using several UAVs in a collaborative emergency
rescue scenario while using the full HFKN Framework.
