There will be a large increase in the number of aerial and ground vehicles that can operate fully autonomously or with a high level of automation requiring human intervention only in special conditions. In increasingly autonomous operations, the situation awareness, risk awareness and experience of human operators that have played such vital roles until now can no longer be counted upon.
The challenge
SymAware addresses the fundamental need for a new conceptual framework for awareness in multi-agent systems that is compatible with the internal models and specifications of robotic agents and that enables safe simultaneous operation of collaborating autonomous agents and humans. The goal of SymAware is to provide a comprehensive framework for situational awareness to support sustainable autonomy via agents that actively perceive risks and collaborate with other robots and humans to improve their awareness and understanding, while fulfilling complex and dynamically changing tasks.
The solution
The SymAware framework founded on compositional logic, symbolic computations, formal reasoning, and uncertainty quantification will characterise and support situational awareness of multi-agent systems by formally modelling and specifying awareness in its various dimensions, sustaining awareness by learning in social contexts, quantifying risks based on limited knowledge, and formulating riskaware negotiation of task distributions.
What did we do?
The SymAware approach for awareness engineering will be implemented and validated in use cases. Royal NLR will develop a use case for modelling, simulation and risk assessment of unmanned aircraft systems traffic management of drone operations in an urban environment, including disturbances and hazards during operations. The computational framework
building on compositional logic, symbolic computations, formal reasoning, and uncertainty quantification will allow for addressing risks and safety explicitly and in a quantifiable manner.
For more information about the SymAware project, please check here.
Project partners:
MPI-SWS, TU/e, KTH, UU, NLR, Siemens
This project has received funding from the Horizon Europe – the Framework Programme for Research and Innovation, under the
grant agreement no 101070802
