Hastily formed knowledge networks and distributed situation awareness for collaborative robotics

Abstract

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 are gathered incrementally and in different ways by heterogeneous robots and humans. We describe this as the creation of 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.

Leveraging active queries in collaborative robotic mission planning

“…It should be noted that the active query mechanism described herein is an independent module that can be integrated with any type of single-or multi-agent system based on specification of a particular underlying data model and the use of underlying distributed delegation and planning systems. In this particular case, we use an underlying system, the SymbiCloud Framework [1] , to do this, but it is not a pre-requisite for using the active query mechanisms in other systems or contexts.…”

“…The proposed system module, the Active Query Engine (AQE), allows users and agents to generate and execute AQs that acquire information from a team of collaborating autonomous agents operating in a shared operational environment. The AQ language, scQL (scQL stands for SymbiCloud Query Language, as it is an extension of the SymbiCloud Framework [1] which is integrated with and leveraged by the AQE), is a query language tailored for asking questions about situation awareness, with syntax inspired by SPARQL [4] . User queries in text format are processed by the AQE which consists of three components: a "parser", a "compiler", and an "executor".…”

“…Agents can also delegate tasks to each other and execute them to achieve goals. Information acquisition and task allocation among team agents will be determined in part using active queries that assume the use of an underlying distributed task-and information-based system [1,2] . The underlying system has been developed in previous work and deployed in field robotic experimentation.…”

Urdarbrunnen: Towards an AI-enabled mission system for Combat Search and Rescue operations

“…This information can be shared between agents in order to build up situational awareness. One framework for knowledge representation is the SymbiCloud framework described in [26].…”

Use of cross‐training in human–robot collaborative rescue

“…The application of cross-training makes the overall workload decrease, which provides a plausible explanation for the better performance of the cross-training participants.Situation awareness forms the basis for the operator to understand the situation of the task performed, so as to make correct decisions or judgments(Endsley, 2021). In rescue missions, the goal of the human-robot team is to provide rescuers with shared situation awareness from the perspective of the robot(Doherty et al, 2021). In the absence of situation awareness or accurate understanding of the situation, it is difficult for even experienced operators to make correct judgments.…”