Hastily Formed Knowledge Networks and Distributed Situation Awareness for Collaborative Robotics
This addresses the challenge of integrating incremental data from heterogeneous sources for collective decision-making in crisis management, though it appears incremental as it builds on existing knowledge representation and synchronization techniques.
The paper tackles the problem of enabling distributed situation awareness in collaborative robot-human teams for emergency rescue by proposing a system architecture for Hastily Formed Knowledge Networks (HFKNs), which was validated in a field study with UAVs performing a 3D mapping mission.
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.